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Fuel starvation during the fuel cell operation inevitably leads to high potential anodes, which causes carbon corrosion and catalyst layer collapse and poses a challenge to the durability of proton exchange membrane fuel cells. Herein, Ti4O7 with a high specific surface area was synthesized facilely and utilized to replace carbon as the anode catalyst support. Previously, the initial performance of the Ti4O7-supported catalyst was obstructed by the disadvantaged electrical conductivity of Ti4O7. This work obtains a comparable polarization performance after optimizing the Ti4O7-supported anode catalyst layer parameters by shortening the electron transfer pathway and increasing metal coverage. Meanwhile, reversal-tolerant anodes (RTAs) were fabricated by the traditional IrO2 addition and core–shell structured Ir@IrOx to validate the applicability of the Ti4O7 support. Typically, the Ir@IrOx/Pt/Ti4O7-fabricated RTA with low Ir loading displays an approximately ten times longer reversal time (6 hours) and two orders of magnitude lower degradation rate than a conventional carbon-supported counterpart. The degradation origin of the Ti4O7-supported anodes was also studied by postmortem characterizations, pointing to the Pt oxidation caused by the formation of TiOx thin layers on the Pt surface. The study aims to promote the development of carbon-free anodes and provide a bright perspective for practical high-performance, low-degradation, and cost-friendly RTA fabrication.

Background: Intervertebral disc herniation, defined as the protrusion or extrusion of the disc mass outside the disc space, is common and easy to diagnose on conventional Magnetic Resonance imaging (MRI) or Computed Tomography (CT) scans. However, the sequestrated disc fragments are challenging to detect, and intervertebral disc mass displacement into the dural sac, which can lead to serious neurological problems such as Cauda equina syndrome (CES). The sequestrated disc fragments do not have specific clinical or radiological characteristics that can differentiate an atypical disc mass from a tumor, making the diagnosis difficult preoperatively. Herein, we describe the use of Sampling Perfection with Application Optimized Contrast using different flip angle Evolution in Magnetic Resonance Imaging (3D SPACE MRI) in the diagnosis of the intervertebral disc fragment that mimicked a tumor. Case presentation: In this study, we report two cases of sequestered lumbar disc herniation. The first case was a 37-year-old patient with a 2-year history of intermittent left lower limb pain that aggravates with exercise and is relieved at rest, while the second case was a 42-year-old patient with a history of 40 days of numbness and pain in the left lower limb. Conclusion: 3D SPACE MRI is a beneficial diagnostic imaging tool for discriminating between disc mass that mimics a tumor and a tumor before surgery. If the disc fragment mimicking a tumor can be identified before the operation, open surgical treatment won't be necessary for all patients.

Single-pointing observations of NH$_3$ (1,1) and (2,2) were conducted towards 672 Planck Early Release Cold Cores (ECCs) using the Nanshan 26-m radio telescope. Out of these sources, a detection rate of 37% (249 cores) was achieved, with NH$_3$(1,1) hyperfine structure detected in 187 and NH$_3$(2,2) emission lines detected in 76 cores. The detection rate of NH3 is positively correlated with the continuum emission fluxes at a frequency of 857 GHz. Among the observed 672 cores, ~22% have associated stellar and IR objects within the beam size (~2$\arcmin$). This suggests that most of the cores in our sample may be starless. The kinetic temperatures of the cores range from 8.9 to 20.7 K, with an average of 12.3 K, indicating a coupling between gas and dust temperatures. The ammonia column densities range from 0.36 to 6.07$\times10^{15}$ cm$^{-2}$, with a median value of 2.04$\times10^{15}$ cm$^{-2}$. The fractional abundances of ammonia range from 0.3 to 9.7$\times10^{-7}$, with an average of 2.7 $\times10^{-7}$, which is one order of magnitude larger than that of Massive Star-Forming (MSF) regions and Infrared Dark Clouds (IRDCs). The correlation between thermal and non-thermal velocity dispersion of the NH$_3$(1,1) inversion transition indicates the dominance of supersonic non-thermal motions in the dense gas traced by NH$_3$, and the relationship between these two parameters in Planck cold cores is weaker, with lower values observed for both parameters relative to other samples under our examination. The cumulative distribution shapes of line widths in the Planck cold cores closely resemble those of the dense cores found in regions of Cepheus, and Orion L1630 and L1641, with higher values compared to Ophiuchus. A comparison of NH3 line-center velocities with those of $^{13}$CO and C$^{18}$O shows small differences (0.13 and 0.12 km s$^{-1}$ ), suggesting quiescence on small scales., Comment: 29 pages, 13 figures

Recent progress in single-image super-resolution (SISR) has achieved remarkable performance, yet the computational costs of these methods remain a challenge for deployment on resource-constrained devices. Especially for transformer-based methods, the self-attention mechanism in such models brings great breakthroughs while incurring substantial computational costs. To tackle this issue, we introduce the Convolutional Transformer layer (ConvFormer) and the ConvFormer-based Super-Resolution network (CFSR), which offer an effective and efficient solution for lightweight image super-resolution tasks. In detail, CFSR leverages the large kernel convolution as the feature mixer to replace the self-attention module, efficiently modeling long-range dependencies and extensive receptive fields with a slight computational cost. Furthermore, we propose an edge-preserving feed-forward network, simplified as EFN, to obtain local feature aggregation and simultaneously preserve more high-frequency information. Extensive experiments demonstrate that CFSR can achieve an advanced trade-off between computational cost and performance when compared to existing lightweight SR methods. Compared to state-of-the-art methods, e.g. ShuffleMixer, the proposed CFSR achieves 0.39 dB gains on Urban100 dataset for x2 SR task while containing 26% and 31% fewer parameters and FLOPs, respectively. Code and pre-trained models are available at https://github.com/Aitical/CFSR., Comment: submitting to TIP

A variety of ranging threats represented by Ghost Peak attack have raised concerns regarding the security performance of Ultra-Wide Band (UWB) systems with the finalization of the IEEE 802.15.4z standard. Based on channel reciprocity, this paper proposes a low complexity attack detection scheme that compares Channel Impulse Response (CIR) features of both ranging sides utilizing an autoencoder with the capability of data compression and feature extraction. Taking Ghost Peak attack as an example, this paper demonstrates the effectiveness, feasibility and generalizability of the proposed attack detection scheme through simulation and experimental validation. The proposed scheme achieves an attack detection success rate of over 99% and can be implemented in current systems at low cost.

Recent advances in self-supervised learning, predominantly studied in high-level visual tasks, have been explored in low-level image processing. This paper introduces a novel self-supervised constraint for single image super-resolution, termed SSC-SR. SSC-SR uniquely addresses the divergence in image complexity by employing a dual asymmetric paradigm and a target model updated via exponential moving average to enhance stability. The proposed SSC-SR framework works as a plug-and-play paradigm and can be easily applied to existing SR models. Empirical evaluations reveal that our SSC-SR framework delivers substantial enhancements on a variety of benchmark datasets, achieving an average increase of 0.1 dB over EDSR and 0.06 dB over SwinIR. In addition, extensive ablation studies corroborate the effectiveness of each constituent in our SSC-SR framework. Codes are available at https://github.com/Aitical/SSCSR., Comment: ICME 2024

Large language models (LLMs) have recently achieved state-of-the-art performance across various tasks, yet due to their large computational requirements, they struggle with strict latency and power demands. Deep neural network (DNN) quantization has traditionally addressed these limitations by converting models to low-precision integer formats. Yet recently alternative formats, such as Normal Float (NF4), have been shown to consistently increase model accuracy, albeit at the cost of increased chip area. In this work, we first conduct a large-scale analysis of LLM weights and activations across 30 networks to conclude most distributions follow a Student's t-distribution. We then derive a new theoretically optimal format, Student Float (SF4), with respect to this distribution, that improves over NF4 across modern LLMs, for example increasing the average accuracy on LLaMA2-7B by 0.76% across tasks. Using this format as a high-accuracy reference, we then propose augmenting E2M1 with two variants of supernormal support for higher model accuracy. Finally, we explore the quality and performance frontier across 11 datatypes, including non-traditional formats like Additive-Powers-of-Two (APoT), by evaluating their model accuracy and hardware complexity. We discover a Pareto curve composed of INT4, E2M1, and E2M1 with supernormal support, which offers a continuous tradeoff between model accuracy and chip area. For example, E2M1 with supernormal support increases the accuracy of Phi-2 by up to 2.19% with 1.22% area overhead, enabling more LLM-based applications to be run at four bits., Comment: Accepted to ICML 2024

This paper discusses the results of the third edition of the Monocular Depth Estimation Challenge (MDEC). The challenge focuses on zero-shot generalization to the challenging SYNS-Patches dataset, featuring complex scenes in natural and indoor settings. As with the previous edition, methods can use any form of supervision, i.e. supervised or self-supervised. The challenge received a total of 19 submissions outperforming the baseline on the test set: 10 among them submitted a report describing their approach, highlighting a diffused use of foundational models such as Depth Anything at the core of their method. The challenge winners drastically improved 3D F-Score performance, from 17.51% to 23.72%., Comment: To appear in CVPRW2024

Ectomycorrhizal fungi (ECM) sustain nutrient recycling in most terrestrial ecosystems, yet we know little about what major biogeographical events gave rise to present-day diversity and distribution patterns. Given the strict relationship between some ECM lineages and their hosts, geographically well-sampled phylogenies are central to understanding major evolutionary processes of fungal biodiversity patterns. Here, we focus on Amanita sect. Vaginatae to address global diversity and distribution patterns. Ancestral-state-reconstruction based on a 4-gene timetree with over 200 species supports an African origin between the late Paleocene and the early Eocene (ca. 56 Ma). Major biogeographic "out-of-Africa" events include multiple dispersal events to Southeast Asia (ca. 45-21 Ma), Madagascar (ca. 18 Ma), and the current Amazonian basin (ca. 45-36 Ma), the last two likely transoceanic. Later events originating in Southeast Asia involve Nearctic dispersal to North America (ca. 20-5 Ma), Oceania (Australia and New Zealand; ca. 15 Ma), and Europe (ca. 10-5 Ma). Subsequent dispersals were also inferred from Southeast Asia to East Asia (ca. 4 Ma); from North America to East Asia (ca. 11-8 Ma), Southeast Asia (ca. 19-2 Ma), Northern Andes (ca. 15 Ma), and Europe (ca. 15-2 Ma), respectively; and from the Amazon to the Caribbean region (ca. 25-20 Ma). Finally, we detected a significant increase in the net diversification rates in the branch leading to most northern temperate species in addition to higher state-dependent diversification rates in temperate lineages, consistent with previous findings. These results suggest that species of sect. Vaginatae likely have higher dispersal ability and higher adaptability to new environments, in particular compared to those of its sister clade, sect. Caesareae. Overall, the much wider distribution of A. sect. Vaginatae, from pan-tropical to pan-arctic, provides a unique window to understanding niche conservatism across a species-rich clade

Oncogenic fusions formed through chromosomal rearrangements are hallmarks of childhood cancer that define cancer subtype, predict outcome, persist through treatment, and can be ideal therapeutic targets. However, mechanistic understanding of the etiology of oncogenic fusions remains elusive. Here we report a comprehensive detection of 272 oncogenic fusion gene pairs by using tumor transcriptome sequencing data from 5190 childhood cancer patients. We identify diverse factors, including translation frame, protein domain, splicing, and gene length, that shape the formation of oncogenic fusions. Our mathematical modeling reveals a strong link between differential selection pressure and clinical outcome in CBFB-MYH11. We discover 4 oncogenic fusions, including RUNX1-RUNX1T1, TCF3-PBX1, CBFA2T3-GLIS2, and KMT2A-AFDN, with promoter-hijacking-like features that may offer alternative strategies for therapeutic targeting. We uncover extensive alternative splicing in oncogenic fusions including KMT2A-MLLT3, KMT2A-MLLT10, C11orf95-RELA, NUP98-NSD1, KMT2A-AFDN and ETV6-RUNX1. We discover neo splice sites in 18 oncogenic fusion gene pairs and demonstrate that such splice sites confer therapeutic vulnerability for etiology-based genome editing. Our study reveals general principles on the etiology of oncogenic fusions in childhood cancer and suggests profound clinical implications including etiology-based risk stratification and genome-editing-based therapeutics.

As the world's largest developing country, China's rapid economic development has caused more serious damage to the environment, and environmental pollution has become the number one problem in China's development. How to correctly deal with the coexistence of economy and environment is very important for the construction of our country. This paper analyzes the impact of urban design on regional ecology and how these influences can be evaluated, using a high-density city in eastern China as an example. This study takes the layout structure, spatial structure, facility support, flow organization and environmental ecology of the urban space as the direct carrier, and the cultural integration and technical response as the indirect carrier, and carries out the corresponding urban design in the above-mentioned space through the corresponding principles, strategies and methods. The impact of these elements on the urban ecological environment was also analyzed. The results of the study show that the ranking of ecological environment quality score in Anhui Province has been rising, and the number of the top five regions has increased from 20% to 80%

Wan Tang,1,2 Hongdou Chen,1,2 Zixin Zhang,1,2 Gang Wu,1,2 Yanquan Lin1– 3 1Pharmacy, Suqian Hospital of Nanjing Drum-Tower Hospital Group, Suqian, People’s Republic of China; 2Pharmacy, Affiliated Suqian Hospital of Xuzhou Medical University, Suqian, People’s Republic of China; 3Pharmacy, Xuzhou Medical University, Xuzhou, People’s Republic of ChinaCorrespondence: Hongdou Chen, Email chenhongdou2000@aliyun.comObjective: To analyse the relationship between the adverse drug reactions (ADRs) of essential drugs and visits, based on the recorded annual increase in ADRs associated with essential medicines in China, to provide a reliable theoretical basis for further analysis and optimization of the safety of essential drugs.Methods: The data of adverse reactions of essential drugs in China from 2011 to 2020, time series analysis was conducted, and vector autoregressive (VAR) model was established. The relationship between the number of ADRs and visits was explored empirically through Granger causality test, impulse response function and variance decomposition.Results: There was a long-term cointegration relationship and one-way causality between the number of visits and ADRs caused by essential medicines. In the initial stage, the ADR response to the number of visits increased sharply, but with an increase in the number of lag periods, the impact remained basically stable, even showing a slight decreasing trend.Conclusion: The number of visits impacts ADRs caused by essential medicines, but this impact remains basically stable after reaching a certain level.Keywords: essential medicines, adverse drug reactions, vector autoregressive model, time-series analysis

Zu-Fei Wu,1,* Wen-Tao Su,2,3,* Shi Chen,4 Bai-Da Xu,2 Gang-Jun Zong,2,3 Cun-Ming Fang,1 Zheng Huang,1 Xue-Jun Hu,1 Gang-Yong Wu,2,3 Xiao-Lin Ma1 1Department of Cardiology, Xuancheng People’s Hospital, Xuanchen, Anhui, 242000, People’s Republic of China; 2Department of Cardiology, the 904th Hospital of the PLA Joint Logistics Support Force, Wuxi, Jiangsu, 214044, People’s Republic of China; 3Department of Cardiology, Wuxi Clinical College of Anhui Medical University, Wuxi, Jiangsu, 214044, People’s Republic of China; 4Department of Cardiology, Wuxi No.5 People’s Hospital, Wuxi, Jiangsu, 214044, People’s Republic of China*These authors contributed equally to this workCorrespondence: Gang-Yong Wu, Department of Cardiology, the 904th Hospital of the PLA Joint Logistics Support Force, Wuxi, Jiangsu, 214044, People’s Republic of China, Email wuwangyi520@sina.comObjective: To investigate the correlation between serum parathyroid hormone (PTH) levels and in-hospital major adverse cardiovascular events (MACE) in patients with acute ST-segment elevation myocardial infarction (STEMI) after primary percutaneous coronary intervention (PCI), and establish a risk prediction model based on parameters such as PTH for in-hospital MACE.Methods: This observational retrospective study consecutively enrolled 340 patients who underwent primary PCI for STEMI between January 2016 and December 2020, divided into a MACE group (n=92) and a control group (n=248). The least absolute shrinkage and selection operator (LASSO) and logistic regression analyses were used to determine the risk factors for MACE after primary PCI. The rms package in R-studio statistical software was used to construct a nomogram, to detect the line chart C-index, and to draw a calibration curve. The decision curve analysis (DCA) method was used to evaluate the clinical application value and net benefit.Results: Correlation analysis revealed that PTH level positively correlated w

This study presents a general outline of the Qitai radio telescope (QTT) project. Qitai, the site of the telescope, is a county of Xinjiang Uygur Autonomous Region of China, located in the east Tianshan Mountains at an elevation of about 1800 m. The QTT is a fully steerable, Gregorian type telescope with a standard parabolic main reflector of 110 m diameter. The QTT has adopted an um-brella support, homology-symmetric lightweight design. The main reflector is active so that the deformation caused by gravity can be corrected. The structural design aims to ultimately allow high-sensitivity observations from 150 MHz up to 115 GHz. To satisfy the requirements for early scientific goals, the QTT will be equipped with ultra-wideband receivers and large field-of-view mul-ti-beam receivers. A multi-function signal-processing system based on RFSoC and GPU processor chips will be developed. These will enable the QTT to operate in pulsar, spectral line, continuum and Very Long Baseline Interferometer (VLBI) observing modes. Electromagnetic compatibility (EMC) and radio frequency interference (RFI) control techniques are adopted throughout the system design. The QTT will form a world-class observational platform for the detection of low-frequency (nanoHertz) gravitational waves through pulsar timing array (PTA) techniques, pulsar surveys, the discovery of binary black-hole systems, and exploring dark matter and the origin of life in the universe., Comment: 12 pages, 11 figures, accepted for publication in Science China Physics, Mechanics & Astronomy

Protostellar jets and outflows are essential ingredients of the star formation process. A better understanding of this phenomenon is important in its own right as well as for many fundamental aspects of star formation. Jets and outflows associated with O-type protostars are rarely studied with observations reaching the close vicinity of the protostars. In this work, we report high-resolution ALMA and VLBA observations to reveal a clear and consistent picture of an outflow associated with an O-type protostar candidate in the G26.50+0.28 region. These observations reveal, for the first time, a collimated jet located in the middle of the outflow cavity. The jet is found to be perpendicular to an elongated disk/toroid and its velocity gradient. The collimated jet appears to show a small amplitude ($\alpha$$\approx$0$\,.\!\!^{\circ}$06) counterclockwise precession, when looking along the blueshifted jet axis from the strongest continuum source MM1, with a precession length of 0.22 pc. The inclination of the jet is likely to be very low ($\approx$8$^{\circ}$), which makes it a promising target to study its transverse morphologies and kinematics. However, no clear evidence of jet rotation is found in the ALMA and VLBA observations. The three-dimensional velocities of the water maser spots appear to show the same absolute speed with respect to different opening angles, suggesting the jet winds may be launched in a relatively small region. This favors the X-wind model, that is, jets are launched in a small area near the inner disk edge., Comment: 14 pages, 13 figures, accepted for publication in A&A

We studied the hub filament system G323.46-0.08 based on archival molecular line data from the SEDIGISM 13CO survey and infrared data from the GLIMPSE, MIPS, and Hi-GAL surveys. G323.46-0.08 consists of three filaments, F-north, F-west, and F-south, that converge toward the central high_mass clump AGAL 323.459-0.079. F-west and Part 1 of the F-south show clear large-scale velocity gradients 0.28 and 0.44 km s-1 pc-1, respectively. They seem to be channeling materials into AGAL 323.459-0.079. The minimum accretion rate was estimated to be 1216 M Myr-1. A characteristic V-shape appears around AGAL 323.459-0.079 in the PV diagram, which traces the accelerated gas motions under gravitational collapse. This has also been supported by model fitting results. All three filaments are supercritical and they have fragmented into many dense clumps. The seesaw patterns near most dense clumps in the PV diagram suggests that mass accretion also occurs along the filament toward the clumps. Our results show that filamentary accretion flows appear to be an important mechanism for supplying the materials necessary to form the central high-mass clump AGAL 323.459-0.079 and to propel the star forming activity taking place therein.

Min Zhu,1 Yuyu Sun,1 Huawei Xue,1 Gang Wu,1 Zhen Wang,1 Junfeng Shi,1 Jiye Ma,1 Baorong Gu,1 Xiaoling Yan2 1Department of Spine Surgery, Nantong Third People’s Hospital, Affiliated Nantong Hospital 3 of Nantong University, Nantong, People’s Republic of China; 2Department of Chemotherapy, Affiliated Hospital of Nantong University, Nantong, People’s Republic of ChinaCorrespondence: Xiaoling Yan, Department of Chemotherapy, Affiliated Hospital of Nantong University, Nantong, 226001, People’s Republic of China, Email 5200007@ntu.edu.cnBackground: Osteosarcoma is a malignant tumor originating from the skeletal system. There is no effective treatment other than surgery and chemotherapy, which seriously endangers the health of children and adolescents. NEK6 is a novel discovered Serine/Threonine protein kinase that can regulate cell cycle and activate several oncogenic pathways.Methods: NEK6 expression in pan-cancer including sarcoma was evaluated using analysis tools of TIMER, UALCNA and GEPIA with TCGA database, and its association with overall survival in patients with sarcoma was also analyzed. TargetScan, tarbase, microT-CDS and Starbase online software were used to predict NEK6-targeted miRNAs, including miR-26a-5p. Tumor tissues from patients with osteosarcoma were collected for NEK6 and miRNA detection using RT-qPCR. NEK6 down-regulated by siRNAs or miR-26a-5p in osteosarcoma cells was detected by RT-qPCR, Western blot and Immunofluorescence staining assays. Effects of NEK6 knockdown on proliferation, migration, invasion and apoptosis of osteosarcoma cells were detected by CCK-8, wound healing, transwell and flow cytometry, respectively. The expressions of STAT3, metastasis and apoptosis-related genes were detected by Western blot.Results: High expression of NEK6 and low expression of miR-26a-5p were lowly expressed in osteosarcoma and they were negative correlation. NEK6 has been confirmed as a direct target for miR-26a-5p. In addition, NEK6 down-regulated

Pengcheng Ma,1 Yadi Li,2 Yusen Feng,1 Gang Wu,3 Bin Li,4 Haiyan Wu5 1Department of Radiology, Kunming Yan ‘an Hospital, Kunming, 650000, People’s Republic of China; 2Department of Ophthalmology, Affiliated Hospital of Yunnan University, Kunming, 650000, People’s Republic of China; 3Department of Neurology, Kunming Yan ‘an Hospital, Kunming, 650000, People’s Republic of China; 4Department of Neurosurgery, Kunming Yan ‘an Hospital, Kunming, 650000, People’s Republic of China; 5Department of Cardiovascular, Affiliated Hospital of Kunming University of Science and Technology, Kunming, 650000, People’s Republic of ChinaCorrespondence: Gang Wu, Tel/Fax +86 0871 632111408, Email yngangwu1126@126.comPurpose: To evaluate the stability of unruptured intracranial aneurysm (UIA) with high-resolution magnetic resonance imaging of the vessel wall (HR-VWI).Materials and Methods: A total of 92 UIA patients were enrolled. After MRA, HR-VWI imaging, the reconstruction of volume rendering (VR) and maximum intensity projection (MIP) were performed to observe the location and size of aneurysms, AR value (ratio of aneurysm height to aneurysmal diameter), SR value (ratio of maximum tumor depth to proximal parent artery diameter), and signal intensity were measured.Results: There were 7 aneurysms with UIA located in the anterior cerebral artery, 31 aneurysms with UIA in the middle cerebral artery, 1 aneurysm with UIA in the posterior cerebral artery, 18 aneurysms with UIA in the anterior communication, 5 aneurysms with UIA in the posterior communication, 34 aneurysms with UIA in the intracranial segment of the internal carotid artery and 3 aneurysms with UIA in the vertebral artery. Among them, 8 patients had more than two multiple aneurysms. The lesion size was 2– 38mm (6.3 ± 5.09). There are 46 aneurysms with wall enhancement: the maximum SR value was 7.03 and the minimum 1.2, and the maximum AR value was 7.5 and the minimum 1.0. Fifty-five aneurysms showed no enhancem

Wu G, Ma Z, Qian J, Liu B. Onco Targets Ther. 2015;8:2067–2074. We, the Editor and Publisher of OncoTargets and Therapy are retracting the published article. Since publication, concerns have been raised about the integrity of the data in the article. When approached for an explanation, the authors have been unable to address the concerns raised and have not been able to provide sufficient supporting information. As verifying the validity of published work is core to the integrity of the scholarly record, we are therefore retracting the article. The authors listed in this publication have been informed. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as “Retracted”.

Titanium (Ti) ion can stimulate osteoblast apoptosis and therefore have a high potential to play a negative role in the aseptic loosening of implants. Mitochondrial abnormalities are closely related to osteoblast dysfunction. However, the mitochondrial molecular mechanism of Ti ion induced osteoblastic cell apoptosis is still unclear. This study investigated in vitro mitochondrial oxidative stress (mtROS) mediated mitochondrial dysfunction involved in Ti ion-induced apoptosis of murine MC3T3-E1 osteoblastic cells. In addition to reducing mitochondrial membrane potential (MMP) and decreasing adenosine triglyceride production, exposure to Ti ions increased mitochondrial oxidative stress. Moreover, mitochondrial abnormalities significantly contributed to Ti ion induction of osteoblastic cellular apoptosis. A mitochondria-specific antioxidant, mitoquinone (MitoQ), alleviated Ti ion-induced mitochondrial dysfunction and apoptosis in osteoblastic cells, indicating that Ti ion mainly induces mitochondrial oxidative stress to produce a cytotoxic effect on osteoblasts. Here we show that the primary regulator of mitochondrial permeability transition pore (mPTP), cyclophilin D (CypD), is involved in mitochondrial dysfunction and osteoblast cell apoptosis induced by Ti ion. Overexpression of CypD exacerbates osteoblast apoptosis and impairs osteogenic function. Moreover, detrimental effects of CypD were rescued by cyclosporin A (CsA), an inhibitor of CypD, which shows its protective effect on mitochondrial and osteogenic osteoblast functions. Based on new insights into the mitochondrial mechanisms underlying Ti ion-induced apoptosis of osteoblastic cells, the findings of this study lay the foundation for the clinical use of CypD inhibitors to prevent or treat implant failure.

This paper investigates downlink power adaptation for the suborbital node in suborbital-ground communication systems, which are subject to extremely high reliability and ultra-low latency communications requirements. The problem is formulated as a power threshold-minimization problem, where interference from satellites is modeled as an accumulation of stochastic point processes on different orbit planes, and hybrid beamforming (HBF) is considered. To ensure Quality of Service (QoS) constraints, the finite blocklength regime is adopted. Numerical results show that the required transmit power of the suborbital node decreases as the elevation angle at the receiving station increases.

Multimodal Sentiment Analysis (MSA) has been a popular topic in natural language processing nowadays, at both sentence and aspect level. However, the existing approaches almost require large-size labeled datasets, which bring about large consumption of time and resources. Therefore, it is practical to explore the method for few-shot sentiment analysis in cross-modalities. Previous works generally execute on textual modality, using the prompt-based methods, mainly two types: hand-crafted prompts and learnable prompts. The existing approach in few-shot multi-modality sentiment analysis task has utilized both methods, separately. We further design a hybrid pattern that can combine one or more fixed hand-crafted prompts and learnable prompts and utilize the attention mechanisms to optimize the prompt encoder. The experiments on both sentence-level and aspect-level datasets prove that we get a significant outperformance.

Quadratic minimization problems with orthogonality constraints (QMPO) play an important role in many applications of science and engineering. However, some existing methods may suffer from low accuracy or heavy workload for large-scale QMPO. Krylov subspace methods are popular for large-scale optimization problems. In this work, we propose a block Lanczos method for solving the large-scale QMPO. In the proposed method, the original problem is projected into a small-sized one, and the Riemannian Trust-Region method is employed to solve the reduced QMPO. Convergence results on the optimal solution, the optimal objective function value, the multiplier and the KKT error are established. Moreover, we give the convergence speed of optimal solution, and show that if the block Lanczos process terminates, then an exact KKT solution is derived. Numerical experiments illustrate the numerical behavior of the proposed algorithm, and demonstrate that it is more powerful than many state-of-the-art algorithms for large-scale quadratic minimization problems with orthogonality constraints.

Session-based recommendation systems(SBRS) are more suitable for the current e-commerce and streaming media recommendation scenarios and thus have become a hot topic. The data encountered by SBRS is typically highly sparse, which also serves as one of the bottlenecks limiting the accuracy of recommendations. So Contrastive Learning(CL) is applied in SBRS owing to its capability of improving embedding learning under the condition of sparse data. However, existing CL strategies are limited in their ability to enforce finer-grained (e.g., factor-level) comparisons and, as a result, are unable to capture subtle differences between instances. More than that, these strategies usually use item or segment dropout as a means of data augmentation which may result in sparser data and thus ineffective self-supervised signals. By addressing the two aforementioned limitations, we introduce a novel multi-granularity CL framework. Specifically, two extra augmented embedding convolution channels with different granularities are constructed and the embeddings learned by them are compared with those learned from original view to complete the CL tasks. At factor-level, we employ Disentangled Representation Learning to obtain finer-grained data(e.g. factor-level embeddings), with which we can construct factor-level convolution channels. At item-level, the star graph is deployed as the augmented data and graph convolution on it can ensure the effectiveness of self-supervised signals. Compare the learned embeddings of these two views with the learned embeddings of the basic view to achieve CL at two granularities. Finally, the more precise item-level and factor-level embeddings obtained are referenced to generate personalized recommendations for the user. The proposed model is validated through extensive experiments on two benchmark datasets, showcasing superior performance compared to existing methods.

Limited intra-session information is the performance bottleneck of the early GNN based SBR models. Therefore, some GNN based SBR models have evolved to introduce additional inter-session information to facilitate the next-item prediction. However, we found that the introduction of inter-session information may bring interference to these models. The possible reasons are twofold. First, inter-session dependencies are not differentiated at the factor-level. Second, measuring inter-session weight by similarity is not enough. In this paper, we propose DEISI to solve the problems. For the first problem, DEISI differentiates the types of inter-session dependencies at the factor-level with the help of DRL technology. For the second problem, DEISI introduces stability as a new metric for weighting inter-session dependencies together with the similarity. Moreover, CL is used to improve the robustness of the model. Extensive experiments on three datasets show the superior performance of the DEISI model compared with the state-of-the-art models.

In recent years, the use of large convolutional kernels has become popular in designing convolutional neural networks due to their ability to capture long-range dependencies and provide large receptive fields. However, the increase in kernel size also leads to a quadratic growth in the number of parameters, resulting in heavy computation and memory requirements. To address this challenge, we propose a neighborhood attention (NA) module that upgrades the standard convolution with a self-attention mechanism. The NA module efficiently extracts long-range dependencies in a sliding window pattern, thereby achieving similar performance to large convolutional kernels but with fewer parameters. Building upon the NA module, we propose a lightweight single image super-resolution (SISR) network named TCSR. Additionally, we introduce an enhanced feed-forward network (EFFN) in TCSR to improve the SISR performance. EFFN employs a parameter-free spatial-shift operation for efficient feature aggregation. Our extensive experiments and ablation studies demonstrate that TCSR outperforms existing lightweight SISR methods and achieves state-of-the-art performance. Our codes are available at \url{https://github.com/Aitical/TCSR}., Comment: 9 pages, 9 figures

Intelligent wireless networks have long been expected to have self-configuration and self-optimization capabilities to adapt to various environments and demands. In this paper, we develop a novel distributed hierarchical deep reinforcement learning (DHDRL) framework with two-tier control networks in different timescales to optimize the long-term spectrum efficiency (SE) of the downlink cell-free multiple-input single-output (MISO) network, consisting of multiple distributed access points (AP) and user terminals (UT). To realize the proposed two-tier control strategy, we decompose the optimization problem into two sub-problems, AP-UT association (AUA) as well as beamforming and power allocation (BPA), resulting in a Markov decision process (MDP) and Partially Observable MDP (POMDP). The proposed method consists of two neural networks. At the system level, a distributed high-level neural network is introduced to optimize wireless network structure on a large timescale. While at the link level, a distributed low-level neural network is proposed to mitigate inter-AP interference and improve the transmission performance on a small timescale. Numerical results show that our method is effective for high-dimensional problems, in terms of spectrum efficiency, signaling overhead as well as satisfaction probability, and generalize well to diverse multi-object problems., Comment: The paper has some updated

Yiwen Dong,1– 3 Litao Yao,2– 4,* Lei Cai,1 Mi Jin,1 Tymour Forouzanfar,2,3 Lianjun Wu,1 Jinsong Liu,1,* Gang Wu2,3 1School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, People’s Republic of China; 2Department of Oral and Maxillofacial Surgery/Pathology, Amsterdam UMC and Academic Center for Dentistry Amsterdam (ACTA), Vrije Universiteit Amsterdam, Amsterdam Movement Science, Amsterdam, Amsterdam, the Netherlands; 3Department of Oral Cell Biology, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam (UvA) and Vrije Universiteit Amsterdam (VU), Amsterdam, the Netherlands; 4Department of Dentistry, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China*These authors contributed equally to this workCorrespondence: Litao Yao, Department of Dentistry, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, People’s Republic of China, Zhejiang, Email yaolt@srrsh.com Jinsong Liu, School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, 325027, People’s Republic of China, Email jinsong0719@wmu.edu.cnIntroduction: An antibacterial and pro-osteogenic coaxially electrospun nanofiber guided bone regeneration (GBR) membrane was fabricated to satisfy the complicated and phased requirements of GBR process.Methods: In this study, we synthesize dual-functional coaxially electrospun nanofiber GBR membranes by encapsulating parathyroid hormone (PTH) in the core layer and magnesium oxide nanoparticles (MgONPs) in the shell layer (MgONPs-PCL/PTH-PCL). Herein, the physicochemical characterization of MgONPs-PCL/PTH-PCL, the release rates of MgONPs and PTH, and antibacterial efficiency of the new membrane were evaluated. Furthermore, the pro-osteogenicity of the membranes was assessed both in-vitro and in-vivo.Results: We successfully fabricated a coaxially electrospun nanofiber MgONPs-PCL/PTH-PCL membrane with the major

Recent advancements in language-model-based video understanding have been progressing at a remarkable pace, spurred by the introduction of Large Language Models (LLMs). However, the focus of prior research has been predominantly on devising a projection layer that maps video features to tokens, an approach that is both rudimentary and inefficient. In our study, we introduce a cutting-edge framework, VaQuitA, designed to refine the synergy between video and textual information. At the data level, instead of sampling frames uniformly, we implement a sampling method guided by CLIP-score rankings, which enables a more aligned selection of frames with the given question. At the feature level, we integrate a trainable Video Perceiver alongside a Visual-Query Transformer (abbreviated as VQ-Former), which bolsters the interplay between the input question and the video features. We also discover that incorporating a simple prompt, "Please be critical", into the LLM input can substantially enhance its video comprehension capabilities. Our experimental results indicate that VaQuitA consistently sets a new benchmark for zero-shot video question-answering tasks and is adept at producing high-quality, multi-turn video dialogues with users.

In recent years, Large Language Models (LLM) have emerged as pivotal tools in various applications. However, these models are susceptible to adversarial prompt attacks, where attackers can carefully curate input strings that mislead LLMs into generating incorrect or undesired outputs. Previous work has revealed that with relatively simple yet effective attacks based on discrete optimization, it is possible to generate adversarial prompts that bypass moderation and alignment of the models. This vulnerability to adversarial prompts underscores a significant concern regarding the robustness and reliability of LLMs. Our work aims to address this concern by introducing a novel approach to detecting adversarial prompts at a token level, leveraging the LLM's capability to predict the next token's probability. We measure the degree of the model's perplexity, where tokens predicted with high probability are considered normal, and those exhibiting high perplexity are flagged as adversarial. Additionaly, our method also integrates context understanding by incorporating neighboring token information to encourage the detection of contiguous adversarial prompt sequences. To this end, we design two algorithms for adversarial prompt detection: one based on optimization techniques and another on Probabilistic Graphical Models (PGM). Both methods are equipped with efficient solving methods, ensuring efficient adversarial prompt detection. Our token-level detection result can be visualized as heatmap overlays on the text sequence, allowing for a clearer and more intuitive representation of which part of the text may contain adversarial prompts.

Safety alignment of Large Language Models (LLMs) can be compromised with manual jailbreak attacks and (automatic) adversarial attacks. Recent studies suggest that defending against these attacks is possible: adversarial attacks generate unlimited but unreadable gibberish prompts, detectable by perplexity-based filters; manual jailbreak attacks craft readable prompts, but their limited number due to the necessity of human creativity allows for easy blocking. In this paper, we show that these solutions may be too optimistic. We introduce AutoDAN, an interpretable, gradient-based adversarial attack that merges the strengths of both attack types. Guided by the dual goals of jailbreak and readability, AutoDAN optimizes and generates tokens one by one from left to right, resulting in readable prompts that bypass perplexity filters while maintaining high attack success rates. Notably, these prompts, generated from scratch using gradients, are interpretable and diverse, with emerging strategies commonly seen in manual jailbreak attacks. They also generalize to unforeseen harmful behaviors and transfer to black-box LLMs better than their unreadable counterparts when using limited training data or a single proxy model. Furthermore, we show the versatility of AutoDAN by automatically leaking system prompts using a customized objective. Our work offers a new way to red-team LLMs and understand jailbreak mechanisms via interpretability., Comment: Version 2 updates: Added comparison of three more evaluation methods and their reliability check using human labeling. Added results for jailbreaking Llama2 (individual behavior) and included complexity and hyperparameter analysis. Revised objectives for prompt leaking. Other minor changes made

A greedy randomized augmented Kaczmarz (GRAK) method was proposed in [Z.-Z. Bai and W.-T. WU, SIAM J. Sci. Comput., 43 (2021), pp. A3892-A3911] for large and sparse inconsistent linear systems. However, one has to construct two new index sets via computing residual vector with respect to the augmented linear system in each iteration. Thus, the computational overhead of this method is large for extremely large-scale problems. Moreover, there is no reliable stopping criterion for this method. In this work, we are interested in solving large-scale sparse or dense inconsistent linear systems, and try to enhance the numerical performance of the GRAK method. First, we propose an accelerated greedy randomized augmented Kaczmarz method. Theoretical analysis indicates that it converges faster than the GRAK method under very weak assumptions. Second, in order to further release the overhead, we propose a semi-randomized augmented Kaczmarz method with simple random sampling. Third, to the best of our knowledge, there are no practical stopping criteria for all the randomized Kaczmarz-type methods till now. To fill-in this gap, we introduce a practical stopping criterion for Kaczmarz-type methods, and show its rationality from a theoretical point of view. Numerical experiments are performed on both real-world and synthetic data sets, which demonstrate the efficiency of the proposed methods and the effectiveness of our stopping criterion.

Contrastive learning has emerged as a prevailing paradigm for high-level vision tasks, which, by introducing properly negative samples, has also been exploited for low-level vision tasks to achieve a compact optimization space to account for their ill-posed nature. However, existing methods rely on manually predefined and task-oriented negatives, which often exhibit pronounced task-specific biases. To address this challenge, our paper introduces an innovative method termed 'learning from history', which dynamically generates negative samples from the target model itself. Our approach, named Model Contrastive Learning for Image Restoration (MCLIR), rejuvenates latency models as negative models, making it compatible with diverse image restoration tasks. We propose the Self-Prior guided Negative loss (SPN) to enable it. This approach significantly enhances existing models when retrained with the proposed model contrastive paradigm. The results show significant improvements in image restoration across various tasks and architectures. For example, models retrained with SPN outperform the original FFANet and DehazeFormer by 3.41 dB and 0.57 dB on the RESIDE indoor dataset for image dehazing. Similarly, they achieve notable improvements of 0.47 dB on SPA-Data over IDT for image deraining and 0.12 dB on Manga109 for a 4x scale super-resolution over lightweight SwinIR, respectively. Code and retrained models are available at https://github.com/Aitical/MCLIR., Comment: Camera Ready Version. Accepted to The 38th Annual AAAI Conference on Artificial Intelligence (AAAI 2024)

Quantization has become a mainstream compression technique for reducing model size, computational requirements, and energy consumption for modern deep neural networks (DNNs). With improved numerical support in recent hardware, including multiple variants of integer and floating point, mixed-precision quantization has become necessary to achieve high-quality results with low model cost. Prior mixed-precision methods have performed either a post-training quantization search, which compromises on accuracy, or a differentiable quantization search, which leads to high memory usage from branching. Therefore, we propose the first one-shot mixed-precision quantization search that eliminates the need for retraining in both integer and low-precision floating point models. We evaluate our search (FLIQS) on multiple convolutional and vision transformer networks to discover Pareto-optimal models. Our approach improves upon uniform precision, manual mixed-precision, and recent integer quantization search methods. With integer models, we increase the accuracy of ResNet-18 on ImageNet by 1.31% and ResNet-50 by 0.90% with equivalent model cost over previous methods. Additionally, for the first time, we explore a novel mixed-precision floating-point search and improve MobileNetV2 by up to 0.98% compared to prior state-of-the-art FP8 models. Finally, we extend FLIQS to simultaneously search a joint quantization and neural architecture space and improve the ImageNet accuracy by 2.69% with similar model cost on a MobileNetV2 search space., Comment: Accepted to AutoML 2024

Accurate depth estimation under out-of-distribution (OoD) scenarios, such as adverse weather conditions, sensor failure, and noise contamination, is desirable for safety-critical applications. Existing depth estimation systems, however, suffer inevitably from real-world corruptions and perturbations and are struggled to provide reliable depth predictions under such cases. In this paper, we summarize the winning solutions from the RoboDepth Challenge -- an academic competition designed to facilitate and advance robust OoD depth estimation. This challenge was developed based on the newly established KITTI-C and NYUDepth2-C benchmarks. We hosted two stand-alone tracks, with an emphasis on robust self-supervised and robust fully-supervised depth estimation, respectively. Out of more than two hundred participants, nine unique and top-performing solutions have appeared, with novel designs ranging from the following aspects: spatial- and frequency-domain augmentations, masked image modeling, image restoration and super-resolution, adversarial training, diffusion-based noise suppression, vision-language pre-training, learned model ensembling, and hierarchical feature enhancement. Extensive experimental analyses along with insightful observations are drawn to better understand the rationale behind each design. We hope this challenge could lay a solid foundation for future research on robust and reliable depth estimation and beyond. The datasets, competition toolkit, workshop recordings, and source code from the winning teams are publicly available on the challenge website., Comment: Technical Report; 65 pages, 34 figures, 24 tables; Code at https://github.com/ldkong1205/RoboDepth

Deep models have achieved significant process on single image super-resolution (SISR) tasks, in particular large models with large kernel ($3\times3$ or more). However, the heavy computational footprint of such models prevents their deployment in real-time, resource-constrained environments. Conversely, $1\times1$ convolutions bring substantial computational efficiency, but struggle with aggregating local spatial representations, an essential capability to SISR models. In response to this dichotomy, we propose to harmonize the merits of both $3\times3$ and $1\times1$ kernels, and exploit a great potential for lightweight SISR tasks. Specifically, we propose a simple yet effective fully $1\times1$ convolutional network, named Shift-Conv-based Network (SCNet). By incorporating a parameter-free spatial-shift operation, it equips the fully $1\times1$ convolutional network with powerful representation capability while impressive computational efficiency. Extensive experiments demonstrate that SCNets, despite its fully $1\times1$ convolutional structure, consistently matches or even surpasses the performance of existing lightweight SR models that employ regular convolutions. The code and pre-trained models can be found at https://github.com/Aitical/SCNet., Comment: Accepted by Machine Intelligence Research, DOI: 10.1007/s11633-024-1401-z

Spectral clustering is one of the most popular unsupervised machine learning methods. Constructing similarity matrix is crucial to this type of method. In most existing works, the similarity matrix is computed once for all or is updated alternatively. However, the former is difficult to reflect comprehensive relationships among data points, and the latter is time-consuming and is even infeasible for large-scale problems. In this work, we propose a restarted clustering framework with self-guiding and block diagonal representation. An advantage of the strategy is that some useful clustering information obtained from previous cycles could be preserved as much as possible. To the best of our knowledge, this is the first work that applies restarting strategy to spectral clustering. The key difference is that we reclassify the samples in each cycle of our method, while they are classified only once in existing methods. To further release the overhead, we introduce a block diagonal representation with Nystr\"{o}m approximation for constructing the similarity matrix. Theoretical results are established to show the rationality of inexact computations in spectral clustering. Comprehensive experiments are performed on some benchmark databases, which show the superiority of our proposed algorithms over many state-of-the-art algorithms for large-scale problems. Specifically, our framework has a potential boost for clustering algorithms and works well even using an initial guess chosen randomly., Comment: 36 pages

The corpus cavernosum is the most important structure for penile erection, and its dysfunction causes many physiological and psychological problems. However, its cellular heterogeneity and signalling networks at the molecular level are poorly understood because of limited access to samples. Here, we profile 64,993 human cavernosal single-cell transcriptomes from three males with normal erection and five organic erectile dysfunction patients. Cell communication analysis reveals that cavernosal fibroblasts are central to the paracrine signalling network and regulate microenvironmental homeostasis. Combining with immunohistochemical staining, we reveal the cellular heterogeneity and describe a detailed spatial distribution map for each fibroblast, smooth muscle and endothelial subcluster in the corpus cavernosum. Furthermore, comparative analysis and related functional experiments identify candidate regulatory signalling pathways in the pathological process. Our study provides an insight into the human corpus cavernosum microenvironment and a reference for potential erectile dysfunction therapies.

The corpus cavernosum is the most important structure for penile erection, and its dysfunction causes many physiological and psychological problems. However, its cellular heterogeneity and signalling networks at the molecular level are poorly understood because of limited access to samples. Here, we profile 64,993 human cavernosal single-cell transcriptomes from three males with normal erection and five organic erectile dysfunction patients. Cell communication analysis reveals that cavernosal fibroblasts are central to the paracrine signalling network and regulate microenvironmental homeostasis. Combining with immunohistochemical staining, we reveal the cellular heterogeneity and describe a detailed spatial distribution map for each fibroblast, smooth muscle and endothelial subcluster in the corpus cavernosum. Furthermore, comparative analysis and related functional experiments identify candidate regulatory signalling pathways in the pathological process. Our study provides an insight into the human corpus cavernosum microenvironment and a reference for potential erectile dysfunction therapies.

The genetics of relapsed pediatric acute myeloid leukemia (AML) has yet to be comprehensively defined. Here, we present the spectrum of genomic alterations in 136 relapsed pediatric AMLs. We identified recurrent exon 13 tandem duplications (TD) in upstream binding transcription factor (UBTF) in 9% of relapsed AML cases. UBTF-TD AMLs commonly have normal karyotype or trisomy 8 with cooccurring WT1 mutations or FLT3-ITD but not other known oncogenic fusions. These UBTF-TD events are stable during disease progression and are present in the founding clone. In addition, we observed that UBTF-TD AMLs account for approximately 4% of all de novo pediatric AMLs, are less common in adults, and are associated with poor outcomes and MRD positivity. Expression of UBTF-TD in primary hematopoietic cells is sufficient to enhance serial clonogenic activity and to drive a similar transcriptional program to UBTF-TD AMLs. Collectively, these clinical, genomic, and functional data establish UBTF-TD as a new recurrent mutation in AML.SignificanceWe defined the spectrum of mutations in relapsed pediatric AML and identified UBTF-TDs as a new recurrent genetic alteration. These duplications are more common in children and define a group of AMLs with intermediate-risk cytogenetic abnormalities, FLT3-ITD and WT1 alterations, and are associated with poor outcomes. See related commentary by Hasserjian and Nardi, p. 173. This article is highlighted in the In This Issue feature, p. 171.

Although mRNA vaccine efficacy against severe coronavirus disease 2019 remains high, variant emergence has prompted booster immunizations. However, the effects of repeated exposures to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigens on memory T cells are poorly understood. Here, we utilize major histocompatibility complex multimers with single-cell RNA sequencing to profile SARS-CoV-2-responsive T cells ex vivo from humans with one, two or three antigen exposures, including vaccination, primary infection and breakthrough infection. Exposure order determined the distribution between spike-specific and non-spike-specific responses, with vaccination after infection leading to expansion of spike-specific T cells and differentiation to CCR7-CD45RA+ effectors. In contrast, individuals after breakthrough infection mount vigorous non-spike-specific responses. Analysis of over 4,000 epitope-specific T cell antigen receptor (TCR) sequences demonstrates that all exposures elicit diverse repertoires characterized by shared TCR motifs, confirmed by monoclonal TCR characterization, with no evidence for repertoire narrowing from repeated exposure. Our findings suggest that breakthrough infections diversify the T cell memory repertoire and current vaccination protocols continue to expand and differentiate spike-specific memory.

Randomized block Kaczmraz method plays an important role in solving large-scale linear system. One of the key points of this type of methods is how to effectively select working rows. However, in most of the state-of-the-art randomized block Kaczmarz-type methods, one has to scan all the rows of the coefficient matrix in advance for computing probabilities or paving, or to compute the residual vector of the linear system in each iteration to determine the working rows. Thus, we have to access all the rows of the data matrix in these methods, which are unfavorable for big-data problems. Moreover, to the best of our knowledge, how to efficiently choose working rows in randomized block Kaczmarz-type methods for multiple linear systems is still an open problem. In order to deal with these problems, we propose semi-randomized block Kaczmarz methods with simple random sampling for linear systems with single and multiple right-hand sides, respectively. In these methods, there is no need to scan or pave all the rows of the coefficient matrix, nor to compute probabilities and the residual vector of the linear system in each step. Specifically, one can update all the solutions of a large-scale linear system with multiple right-hand sides simultaneously. The convergence of the proposed methods is considered. Numerical experiments on both real-world and synthetic data sets show that the proposed methods are superior to many state-of-the-art randomized Kaczmarz-type methods for large-scale linear systems.

Graphic layout designs play an essential role in visual communication. Yet handcrafting layout designs is skill-demanding, time-consuming, and non-scalable to batch production. Generative models emerge to make design automation scalable but it remains non-trivial to produce designs that comply with designers' multimodal desires, i.e., constrained by background images and driven by foreground content. We propose LayoutDETR that inherits the high quality and realism from generative modeling, while reformulating content-aware requirements as a detection problem: we learn to detect in a background image the reasonable locations, scales, and spatial relations for multimodal foreground elements in a layout. Our solution sets a new state-of-the-art performance for layout generation on public benchmarks and on our newly-curated ad banner dataset. We integrate our solution into a graphical system that facilitates user studies, and show that users prefer our designs over baselines by significant margins. Our code, models, dataset, graphical system, and demos are available at https://github.com/salesforce/LayoutDETR.

Trust-region subproblem (TRS) is an important problem arising in many applications such as numerical optimization, Tikhonov regularization of ill-posed problems, and constrained eigenvalue problems. In recent decades, extensive works focus on how to solve the trust-region subproblem efficiently. To the best of our knowledge, there are few results on perturbation analysis of the trust-region subproblem. In order to fill in this gap, we focus on first-order perturbation theory of the trust-region subproblem. The main contributions of this paper are three-fold. First, suppose that the TRS is in \emph{easy case}, we give a sufficient condition under which the perturbed TRS is still in easy case. Second, with the help of the structure of the TRS and the classical eigenproblem perturbation theory, we perform first-order perturbation analysis on the Lagrange multiplier and the solution of the TRS, and define their condition numbers. Third, we point out that the solution and the Lagrange multiplier could be well-conditioned even if TRS is in {\it nearly hard case}. The established results are computable, and are helpful to evaluate ill-conditioning of the TRS problem beforehand. Numerical experiments show the sharpness of the established bounds and the effectiveness of the proposed strategies.

Natural language understanding (NLU) has two core tasks: intent classification and slot filling. The success of pre-training language models resulted in a significant breakthrough in the two tasks. One of the promising solutions called BERT can jointly optimize the two tasks. We note that BERT-based models convert each complex token into multiple sub-tokens by wordpiece algorithm, which generates a mismatch between the lengths of the tokens and the labels. This leads to BERT-based models do not do well in label prediction which limits model performance improvement. Many existing models can be compatible with this issue but some hidden semantic information is discarded in the fine-tuning process. We address the problem by introducing a novel joint method on top of BERT which explicitly models the multiple sub-tokens features after wordpiece tokenization, thereby contributing to the two tasks. Our method can well extract the contextual features from complex tokens by the proposed sub-words attention adapter (SAA), which preserves overall utterance information. Additionally, we propose an intent attention adapter (IAA) to obtain the full sentence features to aid users to predict intent. Experimental results confirm that our proposed model is significantly improved on two public benchmark datasets. In particular, the slot filling F1 score is improved from 96.1 to 98.2 (2.1% absolute) on the Airline Travel Information Systems (ATIS) dataset.

We present BotSIM, a data-efficient end-to-end Bot SIMulation toolkit for commercial text-based task-oriented dialog (TOD) systems. BotSIM consists of three major components: 1) a Generator that can infer semantic-level dialog acts and entities from bot definitions and generate user queries via model-based paraphrasing; 2) an agenda-based dialog user Simulator (ABUS) to simulate conversations with the dialog agents; 3) a Remediator to analyze the simulated conversations, visualize the bot health reports and provide actionable remediation suggestions for bot troubleshooting and improvement. We demonstrate BotSIM's effectiveness in end-to-end evaluation, remediation and multi-intent dialog generation via case studies on two commercial bot platforms. BotSIM's "generation-simulation-remediation" paradigm accelerates the end-to-end bot evaluation and iteration process by: 1) reducing manual test cases creation efforts; 2) enabling a holistic gauge of the bot in terms of NLU and end-to-end performance via extensive dialog simulation; 3) improving the bot troubleshooting process with actionable suggestions. A demo of our system can be found at https://tinyurl.com/mryu74cd and a demo video at https://youtu.be/qLi5iSoly30. We have open-sourced the toolkit at https://github.com/salesforce/botsim, Comment: Paper accepted by the EMNLP 2022 System Demo Track; We have open-sourced the toolkit at https://github.com/salesforce/botsim

The generalized Lanczos trust-region (GLTR) method is one of the most popular approaches for solving large-scale trust-region subproblem (TRS). Recently, Jia and Wang [Z. Jia and F. Wang, \emph{SIAM J. Optim., 31 (2021), pp. 887--914}] considered the convergence of this method and established some {\it a prior} error bounds on the residual, the solution and the Largrange multiplier. In this paper, we revisit the convergence of the GLTR method and try to improve these bounds. First, we establish a sharper upper bound on the residual. Second, we give a new bound on the distance between the approximation and the exact solution, and show that the convergence of the approximation has nothing to do with the associated spectral separation. Third, we present some non-asymptotic bounds for the convergence of the Largrange multiplier, and define a factor that plays an important role on the convergence of the Largrange multiplier. Numerical experiments demonstrate the effectiveness of our theoretical results., Comment: 28 pages, 17 figures

We propose a method to detect individualized highlights for users on given target videos based on their preferred highlight clips marked on previous videos they have watched. Our method explicitly leverages the contents of both the preferred clips and the target videos using pre-trained features for the objects and the human activities. We design a multi-head attention mechanism to adaptively weigh the preferred clips based on their object- and human-activity-based contents, and fuse them using these weights into a single feature representation for each user. We compute similarities between these per-user feature representations and the per-frame features computed from the desired target videos to estimate the user-specific highlight clips from the target videos. We test our method on a large-scale highlight detection dataset containing the annotated highlights of individual users. Compared to current baselines, we observe an absolute improvement of 2-4% in the mean average precision of the detected highlights. We also perform extensive ablation experiments on the number of preferred highlight clips associated with each user as well as on the object- and human-activity-based feature representations to validate that our method is indeed both content-based and user-specific., Comment: 14 pages, 5 figures, 7 tables

Concrete-filled double-skin steel tubes (CFDSTs) are comprised of two concentrically placed steel tubes with the annulus between the tubes filled with concrete. A number of researches have established the prospect of CFDST columns as ideal alternatives for conventional reinforced concrete (RC) columns subjected to extreme events such as earthquakes, fires and vehicular collisions. Since structural columns are the most critical axial load-bearing members in structures, after experiencing blast loading the residual axial strength of key columns is crucial in preventing catastrophic damage of structural systems. Current knowledge on the residual axial load-bearing performance of CFDST columns is very limited. This paper studies the post-blast performance of CFDST columns after subjected close-in explosions, where the residual axial load-bearing capacity of blast-damaged columns is quantified using a high-fidelity physics-based (HFPB) numerical modelling tool. CFDST columns are built in the nonlinear dynamic analysis program LS-DYNA and are comprehensively validated against experimental results in the aspect of column damage profiles and residual axial capacity of blast-damage columns. Extensive parametric studies are then carried out to investigate the effect of several key design parameters, i.e., charge weight, standoff distance, column diameter, nominal steel ratio, column height and axial load ratios, on the residual axial capacity of CFDST columns after subjected close-in blast loads. The results demonstrated good residual axial load-bearing performance of CFDST columns after subjected to close-in detonations of man-portable improvised explosive devices (MPIEDs). Based on the numerical analysis results, an empirical formula is derived to predict the residual axial load-bearing capacity of CFDST columns after experiencing close-in explosions. The proposed empirical formula can be utilized to quickly evaluate the vulnerability of close-in blast damaged CFDST columns

Susac syndrome (SuS) is a rare neuroinflammatory disease that manifests with a triad of hearing loss, branch retinal artery occlusions, and encephalopathy. Patients with SuS are frequently misdiagnosed because the clinical trial is incompletely present at disease onset. In this report, we present a case of a 29-year-old man manifesting sleepiness, epilepsy, urinary dysfunction, and hemiparesis at the initial stage. Magnetic resonance imaging (MRI) revealed multiple abnormal signals located in the lateral paraventricular, corpus callosal, and pons. In addition, the patient had sustained elevation of CSF pressure and protein. ADEM was considered according to the clinical and radiographic findings. However, symptoms were not significantly improved after methylprednisolone therapy. He showed a vision decline in the third month after the disease onset. It was considered from intracranial hypertension or optic neuritis, and therefore retinal arteriolar impairment was ignored. As the disease progresses, cognitive decline was presented. Brain MRI exhibits multiple significant hyperintensities on the DWI sequence with speck-like gadolinium enhancement. Thus, PACNS was diagnosed. The SuS was not made until the presence of hearing decline in the 4 months after the disease onset. The case will be helpful for clinicians to better recognize the atypical initial manifestation of SuS.