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Machine learning has gained much attention and extended to the field of drug discovery. However, due to the uncertainties of the dataset, predictions should be quantitatively analyzed. Conformal prediction is a powerful method for quantifying these uncertainties, generating a predefined confidence level and a corresponding interval within which the true target is anticipated to fall. This paper aims to explore the effects of different chemical representations of SMILES structures for training (chemical descriptors, Morgan fingerprints), machine learning algorithms (k-nearest neighbor, support vector machine, random forest, extreme gradient boosting, and artificial neural network), and different normalization methods (k-nearest neighbor, Mondrian regression) in influencing the conformal prediction results. We find that Morgan fingerprint outperforms chemical descriptors, Mondrian regression outperforms knearest neighbor for one or several values of coverage, and the mean, median, and standard deviation of the output interval. None of the investigated machine learning methods extremely outperforms the other methods. Conformal predictive system, an alternative form of conformal prediction was also investigated to explore its usefulness in drug discovery.

Federated learning (FL) has emerged as a new paradigm for privacy-preserving collaborative training. Under domain skew, the current FL approaches are biased and face two fairness problems. 1) Parameter Update Conflict: data disparity among clients leads to varying parameter importance and inconsistent update directions. These two disparities cause important parameters to potentially be overwhelmed by unimportant ones of dominant updates. It consequently results in significant performance decreases for lower-performing clients. 2) Model Aggregation Bias: existing FL approaches introduce unfair weight allocation and neglect domain diversity. It leads to biased model convergence objective and distinct performance among domains. We discover a pronounced directional update consistency in Federated Learning and propose a novel framework to tackle above issues. First, leveraging the discovered characteristic, we selectively discard unimportant parameter updates to prevent updates from clients with lower performance overwhelmed by unimportant parameters, resulting in fairer generalization performance. Second, we propose a fair aggregation objective to prevent global model bias towards some domains, ensuring that the global model continuously aligns with an unbiased model. The proposed method is generic and can be combined with other existing FL methods to enhance fairness. Comprehensive experiments on Digits and Office-Caltech demonstrate the high fairness and performance of our method., Comment: Accepted by CVPR2024

Xinwei Zhou,1,* Ke Lin,2,* Liangmin Fu,1,* Fei Liu,3,* Hansen Lin,1 Yuhang Chen,1 Bowen Zhuang,2 Hui Liang,4 Qiong Deng,4 Zhu Wang,4 Wei Chen,1 Junhang Luo,1,5 Jiazheng Cao,6 Pengju Li1 1Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China; 2Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China; 3Department of Urology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China; 4Department of Urology, Affiliated Longhua People’s Hospital, Southern Medical University, Shenzhen, People’s Republic of China; 5Institute of Precision Medicine, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People’s Republic of China; 6Department of Urology, Jiangmen Central Hospital, Jiangmen, People’s Republic of China*These authors contributed equally to this workCorrespondence: Pengju Li, Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, No. 58, Zhongshan Road II, Guangzhou, 510080, People’s Republic of China, Tel +86-20-87618227, Email lipj25@mail.sysu.edu.cn Jiazheng Cao, Department of Urology, Jiangmen Central Hospital, Haibang Street 23, Jiangmen, 529030, People’s Republic of China, Tel +86-750-3165500, Email spotatos@163.comBackground: Triggering receptors expressed by myeloid cells-1 (TREM1) is a receptor belonging to the immunoglobulin superfamily and plays an important role in pro-inflammation in acute and chronic inflammatory disorders. However, the understanding of the immunomodulatory roles of TREM1 in the tumor microenvironment remains incomplete.Methods: The expression patterns of TREM1 mRNA in tumors and adjacent normal tissues were compared by

Embryonic mesenchymal cells are dispersed within an extracellular matrix but can coalesce to form condensates with key developmental roles. Cells within condensates undergo fate and morphological changes and induce cell fate changes in nearby epithelia to produce structures including hair follicles, feathers, or intestinal villi. Here, by imaging mouse and chicken embryonic skin, we find that mesenchymal cells undergo much of their dispersal in early interphase, in a stereotyped process of displacement driven by 3 hours of rapid and persistent migration followed by a long period of low motility. The cell division plane and the elevated migration speed and persistence of newly born mesenchymal cells are mechanosensitive, aligning with tissue tension, and are reliant on active WNT secretion. This behaviour disperses mesenchymal cells and allows daughters of recent divisions to travel long distances to enter dermal condensates, demonstrating an unanticipated effect of cell cycle subphase on core mesenchymal behaviour.

In 2012, the Chinese government designated Wenzhou city as a testbed for policy experimentation aimed at institutionalizing informal lending practices. This study investigates how interest rates in the formal and informal credit markets interacted before and after this policy experimentation. Hence, we use the vector autoregression models and ordinary multiple regression method, which is based on the financial repression theory. We document large yield spreads between the formal and informal credit markets in Wenzhou before (2003–2011) and after (2013–2018) the reforms. We find an increase in the responsiveness of the informal sector to the formal sector, after the reforms. We argue that the informal financial system serves as a one-way substitute for the formal financial sector in Wenzhou. An analysis of the transaction-level data suggests thatmaturity, availability of collateralization, loan purpose, and the amount of loans determine informal lending rates. Thus, this study provides important policy implications for reforming China’s financial system

The loss function for bounding box regression (BBR) is essential to object detection. Its good definition will bring significant performance improvement to the model. Most existing works assume that the examples in the training data are high-quality and focus on strengthening the fitting ability of BBR loss. If we blindly strengthen BBR on low-quality examples, it will jeopardize localization performance. Focal-EIoU v1 was proposed to solve this problem, but due to its static focusing mechanism (FM), the potential of non-monotonic FM was not fully exploited. Based on this idea, we propose an IoU-based loss with a dynamic non-monotonic FM named Wise-IoU (WIoU). The dynamic non-monotonic FM uses the outlier degree instead of IoU to evaluate the quality of anchor boxes and provides a wise gradient gain allocation strategy. This strategy reduces the competitiveness of high-quality anchor boxes while also reducing the harmful gradient generated by low-quality examples. This allows WIoU to focus on ordinary-quality anchor boxes and improve the detector's overall performance. When WIoU is applied to the state-of-the-art real-time detector YOLOv7, the AP-75 on the MS-COCO dataset is improved from 53.03% to 54.50%. Code is available at https://github.com/Instinct323/wiou.

Terahertz (THz) band owning the abundant multi-ten-GHz bandwidth is capable to support Terabit-per-second wireless communications, which is a pillar technology for 6G and beyond systems. With sub-millimeter-long antennas, ultra-massive (UM) MIMO and intelligent surface (IS) systems with thousands of array elements are exploited to effectively combat the distance limitation and blockage problems, which compose a promising THz ultra-large antenna array (ULAA) system. As a combined effect of wavelength and array aperture, the resulting coverage of THz systems ranges from near-field to far-field, leading to a new paradigm of cross-field communications. Although channel models, communications theories, and networking strategies have been studied for far-field and near-field separately, the unified design of cross-field communications that achieve high spectral efficiency and low complexity is still missing. In this article, the challenges and features of THz ULAA cross-field communications are investigated. Furthermore, cross-field solutions in three perspectives are presented, including a hybrid spherical- and planar-wave channel model, cross-field channel estimation, and widely-spaced multi-subarray hybrid beamforming, where a subarray as a basic unit in THz ULAA systems is exploited. The approximation error of channel modeling accuracy, spectral efficiency, and estimation error of these designs are numerically evaluated. Finally, as a roadmap of THz ULAA cross-field communications, multiple open problems and potential research directions are elaborated.

Ultra-massive multiple-input multiple-output (UM-MIMO) is the enabler of Terahertz (THz) communications in next-generation wireless networks. In THz UM-MIMO systems, a new paradigm of cross-field communications spanning from near-field to far-field is emerging, since the near-field range expands with higher frequencies and larger array apertures. Precise beam alignment in cross-field is critical but challenging. Specifically, unlike far-field beams that rely only on the angle domain, the incorporation of dual-domain (angle and distance) training significantly increases overhead. A natural question arises of whether far-field beam training can be deployed for cross-field beam alignment. In this paper, this question is answered, by demonstrating that the far-field training enables sufficient signal-to-noise ratio (SNR) in both far- and near-field scenarios, while exciting all channel dimensions. Based on that, we propose a subarray-coordinated hierarchical (SCH) training with greatly reduced overhead. To further obtain high-precision beam designs, we propose a two-phase angle and distance beam estimator (TPBE). Extensive simulations demonstrate the effectiveness of the proposed methods. Compared to near-field exhaustive search, the SCH possesses 0.2\% training overhead. The TPBE achieves 0.01~degrees and 0.02~m estimation root-mean-squared errors for angle and distance. Furthermore, with the estimated beam directions, a near-optimal SNR with 0.11~dB deviation is attained after beam alignment.

Automated log analysis is crucial in modern software-intensive systems for facilitating program comprehension throughout software maintenance and engineering life cycles. Existing methods perform tasks such as log parsing and log anomaly detection by providing a single prediction value without interpretation. However, given the increasing volume of system events, the limited interpretability of analysis results hinders analysts' comprehension of program status and their ability to take appropriate actions. Moreover, these methods require substantial in-domain training data, and their performance declines sharply (by up to 62.5%) in online scenarios involving unseen logs from new domains, a common occurrence due to rapid software updates. In this paper, we propose LogPrompt, a novel interpretable log analysis approach for online scenarios. LogPrompt employs large language models (LLMs) to perform online log analysis tasks via a suite of advanced prompt strategies tailored for log tasks, which enhances LLMs' performance by up to 380.7% compared with simple prompts. Experiments on nine publicly available evaluation datasets across two tasks demonstrate that LogPrompt, despite requiring no in-domain training, outperforms existing approaches trained on thousands of logs by up to 55.9%. We also conduct a human evaluation of LogPrompt's interpretability, with six practitioners possessing over 10 years of experience, who highly rated the generated content in terms of usefulness and readability (averagely 4.42/5). LogPrompt also exhibits remarkable compatibility with open-source and smaller-scale LLMs, making it flexible for practical deployment. Code of LogPrompt is available at https://github.com/lunyiliu/LogPrompt., Comment: Accepted by ICPC 2024

Anomaly detection in multivariate time series data is of paramount importance for ensuring the efficient operation of large-scale systems across diverse domains. However, accurately detecting anomalies in such data poses significant challenges. Existing approaches, including forecasting and reconstruction-based methods, struggle to address these challenges effectively. To overcome these limitations, we propose a novel anomaly detection framework named ImDiffusion, which combines time series imputation and diffusion models to achieve accurate and robust anomaly detection. The imputation-based approach employed by ImDiffusion leverages the information from neighboring values in the time series, enabling precise modeling of temporal and inter-correlated dependencies, reducing uncertainty in the data, thereby enhancing the robustness of the anomaly detection process. ImDiffusion further leverages diffusion models as time series imputers to accurately capturing complex dependencies. We leverage the step-by-step denoised outputs generated during the inference process to serve as valuable signals for anomaly prediction, resulting in improved accuracy and robustness of the detection process. We evaluate the performance of ImDiffusion via extensive experiments on benchmark datasets. The results demonstrate that our proposed framework significantly outperforms state-of-the-art approaches in terms of detection accuracy and timeliness. ImDiffusion is further integrated into the real production system in Microsoft and observe a remarkable 11.4% increase in detection F1 score compared to the legacy approach. To the best of our knowledge, ImDiffusion represents a pioneering approach that combines imputation-based techniques with time series anomaly detection, while introducing the novel use of diffusion models to the field., Comment: To appear in VLDB 2024.Code: https://github.com/17000cyh/IMDiffusion.git

Plant intracellular nucleotide-binding leucine-rich repeat receptors (NLRs) detect pathogen effectors to trigger immune responses(1). Indirect recognition of a pathogen effector by the dicotyledonous Arabidopsis thaliana coiled-coil domain containing NLR (CNL) ZAR1 induces the formation of a large hetero-oligomeric protein complex, termed the ZAR1 resistosome, which functions as a calcium channel required for ZAR1-mediated immunity(2-4). Whether the resistosome and channel activities are conserved among plant CNLs remains unknown. Here we report the cryo-electron microscopy structure of the wheat CNL Sr35(5) in complex with the effector AvrSr35(6) of the wheat stem rust pathogen. Direct effector binding to the leucine-rich repeats of Sr35 results in the formation of a pentameric Sr35-AvrSr35 complex, which we term the Sr35 resistosome. Wheat Sr35 and Arabidopsis ZAR1 resistosomes bear striking structural similarities, including an arginine cluster in the leucine-rich repeats domain not previously recognized as conserved, which co-occurs and forms intramolecular interactions with the 'EDVID' motif in the coiled-coil domain. Electrophysiological measurements show that the Sr35 resistosome exhibits non-selective cation channel activity. These structural insights allowed us to generate new variants of closely related wheat and barley orphan NLRs that recognize AvrSr35. Our data support the evolutionary conservation of CNL resistosomes in plants and demonstrate proof of principle for structure-based engineering of NLRs for crop improvement.

We experimentally report fiber Bragg gratings (FBGs) in a single mode step-index polymer optical fiber (POF) with a core made of TOPAS and cladding made of ZEONEX using 520 nm femtosecond pulses and a point-by-point (PbP) inscription method. With different pulse energies between 9.7 nJ and 11.2 nJ, 12 FBGs are distributed along the cores of two pieces of POFs with negative averaged effective index change up to ~6 × 10−4 in the TOPAS. For POF 1 with FBGs 1–6, the highest reflectivity 45.1% is obtained with a pulse energy of 10.6 nJ. After inscription, good grating stability is reported. Thanks to the post-annealing at 125 °C for 24 h, after cooling the grating reflectivity increases by ~10%. For POF 2 with FBGs 7–12, similar FBG data are obtained showing good reproducibility. Then, the FBGs are annealed at 125 °C for 78 h, and the average reflectivity of the FBGs during the annealing process increases by ~50% compared to that before the annealing, which could be potentially applied to humidity insensitive high temperature measurement.

Integrated ultra-massive multiple-input multiple-output (UM-MIMO) and intelligent reflecting surface (IRS) systems are promising for 6G and beyond Terahertz (0.1-10 THz) communications, to effectively bypass the barriers of limited coverage and line-of-sight blockage. However, excessive dimensions of UM-MIMO and IRS enlarge the near-field region, while strong THz channel sparsity in far-field is detrimental to spatial multiplexing. Moreover, channel estimation (CE) requires recovering the large-scale channel from severely compressed observations due to limited RF-chains. To tackle these challenges, a hybrid spherical- and planar-wave channel model (HSPM) is developed for the cascaded channel of the integrated system. The spatial multiplexing gains under near-field and far-field regions are analyzed, which are found to be limited by the segmented channel with a lower rank. Furthermore, a compressive sensing-based CE framework is developed, including a sparse channel representation method, a separate-side estimation (SSE) and a dictionary-shrinkage estimation (DSE) algorithms. Numerical results verify the effectiveness of the HSPM, the capacity of which is only $5\times10^{-4}$ bits/s/Hz deviated from that obtained by the ground-truth spherical-wave-model, with 256 elements. While the SSE achieves improved accuracy for CE than benchmark algorithms, the DSE is more attractive in noisy environments, with 0.8 dB lower normalized-mean-square-error than SSE.

Reconfigurable intelligent surfaces (RISs) have emerged as a prospective technology for next-generation wireless networks due to their potential in coverage and capacity enhancement. Previous works on achievable rate analysis of RIS-assisted communication systems have mainly focused on the rich-scattering environment where Rayleigh and Rician channel models can be applied. This work studies the ergodic achievable rate of RIS-assisted multiple-input multiple-output communication systems in millimeter-wave band with limited scattering under the Saleh-Valenzuela channel model. Firstly, we derive an upper bound of the ergodic achievable rate by means of majorization theory and Jensen's inequality. The upper bound shows that the ergodic achievable rate increases logarithmically with the number of antennas at the base station (BS) and user, the number of the reflection units at the RIS, and the eigenvalues of the steering matrices associated with the BS, user and RIS. Then, we aim to maximize the ergodic achievable rate by jointly optimizing the transmit covariance matrix at the BS and the reflection coefficients at the RIS. Specifically, the transmit covariance matrix is optimized by the water-filling algorithm and the reflection coefficients are optimized using the Riemannian conjugate gradient algorithm. Simulation results validate the effectiveness of the proposed optimization algorithms., Comment: 30 pages, 11 figures

Due to the complicated background and noise of infrared images, infrared small target detection is one of the most difficult problems in the field of computer vision. In most existing studies, semantic segmentation methods are typically used to achieve better results. The centroid of each target is calculated from the segmentation map as the detection result. In contrast, we propose a novel end-to-end framework for infrared small target detection and segmentation in this paper. First, with the use of UNet as the backbone to maintain resolution and semantic information, our model can achieve a higher detection accuracy than other state-of-the-art methods by attaching a simple anchor-free head. Then, a pyramid pool module is used to further extract features and improve the precision of target segmentation. Next, we use semantic segmentation tasks that pay more attention to pixel-level features to assist in the training process of object detection, which increases the average precision and allows the model to detect some targets that were previously not detectable. Furthermore, we develop a multi-task framework for infrared small target detection and segmentation. Our multi-task learning model reduces complexity by nearly half and speeds up inference by nearly twice compared to the composite single-task model, while maintaining accuracy. The code and models are publicly available at https://github.com/Chenastron/MTUNet.

Zhe Shi,* Qiang Zhong,* Yuhang Chen, Jian Gao, Xin Pan, Qiang Lian, Rong Chen, Pinkai Wang, Jian Wang, Zhanjun Shi, Hao Cheng Department of Orthopedics, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong Province, 510515, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhanjun Shi; Hao Cheng Tel +86 2062787924Email shizhanjun111@gmail.com; haocool317@gmail.comPurpose: Given that autologous bone graft for bone defects is limited by insufficient supply and morbidity at the donor site, developing biomimetic graft materials as an alternative has gained consistent attention. However, obstacles in designing bone-mimetic materials that could integrate the biomimetic nature of the bone extracellular matrix, osteogenic cells, and osteoinductive ingredients with a fast and convenient strategy still exist.Methods: This study designed and fabricated a mesenchymal stem cell (MSC)-laden, nanohydroxyapatite (HAP), and nanosilicate (SN)-loaded bone mimetic and injectable gelatin-methacryloyl hydrogel (GelMA-HAP-SN) system for bone tissue engineering, and systemically investigated the osteogenic capacity of GelMA-HAP-SN in vitro and in vivo.Results: Introducing HAP enhanced the compositional similarity to the natural bone extracellular matrix, and SN loading endowed the hydrogel with injectable and osteogenic ability. As a result, the GelMA-HAP-SN hydrogel demonstrated an increase in cellular viability, proliferation, and spreading behavior. The GelMA-HAP-SN hydrogel also amplified the embedded MSCs’ osteogenic biomarkers’ expression and matrix mineralization. Furthermore, the MSC-encapsulated GelMA-HAP-SN hydrogel was injected into rats’ critical-sized calvaria defect, and micro-CT and histomorphometry staining results further confirmed its excellent bone regeneration ability.Conclusion: These MSC-loaded GelMA-HAP-SN hydrogels are potential graft materials for bone defect treatment.Keywords: G

Zhe Shi,* Qiang Zhong,* Yuhang Chen, Jian Gao, Xin Pan, Qiang Lian, Rong Chen, Pinkai Wang, Jian Wang, Zhanjun Shi, Hao Cheng Department of Orthopedics, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong Province, 510515, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhanjun Shi; Hao Cheng Tel +86 2062787924Email shizhanjun111@gmail.com; haocool317@gmail.comPurpose: Given that autologous bone graft for bone defects is limited by insufficient supply and morbidity at the donor site, developing biomimetic graft materials as an alternative has gained consistent attention. However, obstacles in designing bone-mimetic materials that could integrate the biomimetic nature of the bone extracellular matrix, osteogenic cells, and osteoinductive ingredients with a fast and convenient strategy still exist.Methods: This study designed and fabricated a mesenchymal stem cell (MSC)-laden, nanohydroxyapatite (HAP), and nanosilicate (SN)-loaded bone mimetic and injectable gelatin-methacryloyl hydrogel (GelMA-HAP-SN) system for bone tissue engineering, and systemically investigated the osteogenic capacity of GelMA-HAP-SN in vitro and in vivo.Results: Introducing HAP enhanced the compositional similarity to the natural bone extracellular matrix, and SN loading endowed the hydrogel with injectable and osteogenic ability. As a result, the GelMA-HAP-SN hydrogel demonstrated an increase in cellular viability, proliferation, and spreading behavior. The GelMA-HAP-SN hydrogel also amplified the embedded MSCs’ osteogenic biomarkers’ expression and matrix mineralization. Furthermore, the MSC-encapsulated GelMA-HAP-SN hydrogel was injected into rats’ critical-sized calvaria defect, and micro-CT and histomorphometry staining results further confirmed its excellent bone regeneration ability.Conclusion: These MSC-loaded GelMA-HAP-SN hydrogels are potential graft materials for bone defect treatment.Keywords: G

ComOpT is an open-source research tool for coverage-driven testing of autonomous driving systems, focusing on planning and control. Starting with (i) a meta-model characterizing discrete conditions to be considered and (ii) constraints specifying the impossibility of certain combinations, ComOpT first generates constraint-feasible abstract scenarios while maximally increasing the coverage of k-way combinatorial testing. Each abstract scenario can be viewed as a conceptual equivalence class, which is then instantiated into multiple concrete scenarios by (1) randomly picking one local map that fulfills the specified geographical condition, and (2) assigning all actors accordingly with parameters within the range. Finally, ComOpT evaluates each concrete scenario against a set of KPIs and performs local scenario variation via spawning a new agent that might lead to a collision at designated points. We use ComOpT to test the Apollo~6 autonomous driving software stack. ComOpT can generate highly diversified scenarios with limited test budgets while uncovering problematic situations such as inabilities to make simple right turns, uncomfortable accelerations, and dangerous driving patterns. ComOpT participated in the 2021 IEEE AI Autonomous Vehicle Testing Challenge and won first place among more than 110 contending teams.

As random walk is a powerful tool in many graph processing, mining and learning applications, this paper proposes an efficient in-memory random walk engine named ThunderRW. Compared with existing parallel systems on improving the performance of a single graph operation, ThunderRW supports massive parallel random walks. The core design of ThunderRW is motivated by our profiling results: common RW algorithms have as high as 73.1% CPU pipeline slots stalled due to irregular memory access, which suffers significantly more memory stalls than the conventional graph workloads such as BFS and SSSP. To improve the memory efficiency, we first design a generic step-centric programming model named Gather-Move-Update to abstract different RW algorithms. Based on the programming model, we develop the step interleaving technique to hide memory access latency by switching the executions of different random walk queries. In our experiments, we use four representative RW algorithms including PPR, DeepWalk, Node2Vec and MetaPath to demonstrate the efficiency and programming flexibility of ThunderRW. Experimental results show that ThunderRW outperforms state-of-the-art approaches by an order of magnitude, and the step interleaving technique significantly reduces the CPU pipeline stall from 73.1% to 15.0%.

The Terahertz band is envisioned to meet the demanding 100 Gbps data rates for 6G wireless communications. Aiming at combating the distance limitation problem with low hardware-cost, ultra-massive MIMO with hybrid beamforming is promising. However, relationships among wavelength, array size and antenna spacing give rise to the inaccuracy of planar-wave channel model (PWM), while an enlarged channel matrix dimension leads to excessive parameters of applying spherical-wave channel model (SWM). Moreover, due to the adoption of hybrid beamforming, channel estimation (CE) needs to recover high-dimensional channels from severely compressed channel observation. In this paper, a hybrid spherical- and planar-wave channel model (HSPM) is investigated and proved to be accurate and efficient by adopting PWM within subarray and SWM among subarray. Furthermore, a two-phase HSPM CE mechanism is developed. A deep convolutional-neural-network (DCNN) is designed in the first phase for parameter estimation of reference subarrays, while geometric relationships of the remaining channel parameters between reference subarrays are leveraged to complete CE in the second phase. Extensive numerical results demonstrate the HSPM is accurate at various communication distances, array sizes and carrier frequencies.The DCNN converges fast and achieves high accuracy with 5.2 dB improved normalized-mean-square-error compared to literature methods, and owns substantially low complexity.

While object detection modules are essential functionalities for any autonomous vehicle, the performance of such modules that are implemented using deep neural networks can be, in many cases, unreliable. In this paper, we develop abstraction-based monitoring as a logical framework for filtering potentially erroneous detection results. Concretely, we consider two types of abstraction, namely data-label abstraction and post-algorithm abstraction. Operated on the training dataset, the construction of data-label abstraction iterates each input, aggregates region-wise information over its associated labels, and stores the vector under a finite history length. Post-algorithm abstraction builds an abstract transformer for the tracking algorithm. Elements being associated together by the abstract transformer can be checked against consistency over their original values. We have implemented the overall framework to a research prototype and validated it using publicly available object detection datasets., Comment: Work in progress report

We study the hop-constrained s-t path enumeration (HcPE) problem, which takes a graph $G$, two distinct vertices $s,t$ and a hop constraint $k$ as input, and outputs all paths from $s$ to $t$ whose length is at most $k$. The state-of-the-art algorithms suffer from severe performance issues caused by the costly pruning operations during enumeration for the workloads with the large search space. Consequently, these algorithms hardly meet the real-time constraints of many online applications. In this paper, we propose PathEnum, an efficient index-based algorithm towards real-time HcPE. For an input query, PathEnum first builds a light-weight index aiming to reduce the number of edges involved in the enumeration, and develops efficient index-based approaches for enumeration, one based on depth-first search and the other based on joins. We further develop a query optimizer based on a join-based cost model to optimize the search order. We conduct experiments with 15 real-world graphs. Our experiment results show that PathEnum outperforms the state-of-the-art approaches by orders of magnitude in terms of the query time, throughput and response time.

Terahertz (THz) communications with multi-GHz bandwidth are envisioned as a key technology for 6G systems. Ultra-massive (UM) MIMO with hybrid beamforming architectures are widely investigated to provide a high array gain to overcome the huge propagation loss. However, most of the existing hybrid beamforming architectures can only utilize the multiplexing offered by the multipath components, i.e., inter-path multiplexing, which is very limited due to the spatially sparse THz channel. In this paper, a widely-spaced multi-subarray (WSMS) hybrid beamforming architecture is proposed, which improves the multiplexing gain by exploiting a new type of intra-path multiplexing provided by the spherical-wave propagation among k widely-spaced subarrays, in addition to the inter-path multiplexing. The resulting multiplexing gain of WSMS architecture is k times of the existing architectures. To harness WSMS hybrid beamforming, a novel design problem is formulated by optimizing the number of subarrays, subarray spacing, and hybrid beamforming matrices to maximize the spectral efficiency, which is decomposed into two subproblems. An optimal closed-form solution is derived for the first hybrid beamforming subproblem, while a dominant-line-of-sight-relaxation algorithm is proposed for the second array configuration subproblem. Extensive simulation results demonstrate that the WSMS architecture and proposed algorithms substantially enhance the spectral efficiency and energy efficiency., Comment: 30 pages

Zhe Shi,1,* Yichuan Xu,1,* Ruzha Mulatibieke,2 Qiang Zhong,1 Xin Pan,1 Yuhang Chen,1 Qiang Lian,1 Xin Luo,3 Zhanjun Shi,1 Qingan Zhu1 1Department of Orthopedics, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 2Department of Plastic Surgery, The Third Affiliated Hospital of Sun Yet-Sen University, Guangzhou, People’s Republic of China; 3Rehabilitation Medical School, Guangzhou International Economics College, Guangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhanjun Shi; Qingan Zhu Tel +86 2062787924; +86 20622787195Email nfgk@sohu.com; qinganzhu@gmail.comPurpose: Autologous bone grafts are the gold standard for treating bone defects. However, limited bone supply and morbidity at the donor site restrict its extensive use. Therefore, developing bone graft materials as an alternative to autologous grafts has gained considerable attention. Injectable hydrogels endowed with osteogenic potential have the ability to fill irregular bone defects using minimally invasive procedures and have thus been attracting researchers’ attention. However, from a clinical perspective, most fabrication methods employed for the current injectable osteogenic hydrogels are difficult and inconvenient. In the current study, we fabricated an injectable osteogenic hydrogel using a simple and convenient strategy.Materials and Methods: Gelatin-methacryloyl (GelMA) pre-polymer was synthetized. Nano silicate (SN) and stromal cell-derived factor-1 alpha (SDF-1α) were introduced into the pre-polymer to achieve injectability, controlled release property, excellent osteogenic ability, and efficient stem cell homing.Results: The GelMA-SN-SDF-1α demonstrated excellent injectability via a 17-G needle at room temperature. The loaded SDF-1α exhibited a long-term controlled release pattern and efficiently stimulated MSC migration and homing. The GelMA-SN

Zhe Shi,1,* Yichuan Xu,1,* Ruzha Mulatibieke,2 Qiang Zhong,1 Xin Pan,1 Yuhang Chen,1 Qiang Lian,1 Xin Luo,3 Zhanjun Shi,1 Qingan Zhu1 1Department of Orthopedics, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 2Department of Plastic Surgery, The Third Affiliated Hospital of Sun Yet-Sen University, Guangzhou, People’s Republic of China; 3Rehabilitation Medical School, Guangzhou International Economics College, Guangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhanjun Shi; Qingan Zhu Tel +86 2062787924; +86 20622787195Email nfgk@sohu.com; qinganzhu@gmail.comPurpose: Autologous bone grafts are the gold standard for treating bone defects. However, limited bone supply and morbidity at the donor site restrict its extensive use. Therefore, developing bone graft materials as an alternative to autologous grafts has gained considerable attention. Injectable hydrogels endowed with osteogenic potential have the ability to fill irregular bone defects using minimally invasive procedures and have thus been attracting researchers’ attention. However, from a clinical perspective, most fabrication methods employed for the current injectable osteogenic hydrogels are difficult and inconvenient. In the current study, we fabricated an injectable osteogenic hydrogel using a simple and convenient strategy.Materials and Methods: Gelatin-methacryloyl (GelMA) pre-polymer was synthetized. Nano silicate (SN) and stromal cell-derived factor-1 alpha (SDF-1α) were introduced into the pre-polymer to achieve injectability, controlled release property, excellent osteogenic ability, and efficient stem cell homing.Results: The GelMA-SN-SDF-1α demonstrated excellent injectability via a 17-G needle at room temperature. The loaded SDF-1α exhibited a long-term controlled release pattern and efficiently stimulated MSC migration and homing. The GelMA-SN

The pervasive adoption of Deep Learning (DL) and Graph Processing (GP) makes it a de facto requirement to build large-scale clusters of heterogeneous accelerators including GPUs and FPGAs. The OpenCL programming framework can be used on the individual nodes of such clusters but is not intended for deployment in a distributed manner. Fortunately, the original OpenCL semantics naturally fit into the programming environment of heterogeneous clusters. In this paper, we propose a heterogeneity-aware OpenCL-like (HaoCL) programming framework to facilitate the programming of a wide range of scientific applications including DL and GP workloads on large-scale heterogeneous clusters. With HaoCL, existing applications can be directly deployed on heterogeneous clusters without any modifications to the original OpenCL source code and without awareness of the underlying hardware topologies and configurations. Our experiments show that HaoCL imposes a negligible overhead in a distributed environment, and provides near-linear speedups on standard benchmarks when computation or data size exceeds the capacity of a single node. The system design and the evaluations are presented in this demo paper., Comment: Accepted by ICDCS 2020

Although deep learning has achieved remarkable successes over the past years, few reports have been published about applying deep neural networks to Wireless Sensor Networks (WSNs) for image targets recognition where data, energy, computation resources are limited. In this work, a Cost-Effective Domain Generalization (CEDG) algorithm has been proposed to train an efficient network with minimum labor requirements. CEDG transfers networks from a publicly available source domain to an application-specific target domain through an automatically allocated synthetic domain. The target domain is isolated from parameters tuning and used for model selection and testing only. The target domain is significantly different from the source domain because it has new target categories and is consisted of low-quality images that are out of focus, low in resolution, low in illumination, low in photographing angle. The trained network has about 7M (ResNet-20 is about 41M) multiplications per prediction that is small enough to allow a digital signal processor chip to do real-time recognitions in our WSN. The category-level averaged error on the unseen and unbalanced target domain has been decreased by 41.12%.

Single image inverse problem is a notoriously challenging ill-posed problem that aims to restore the original image from one of its corrupted versions. Recently, this field has been immensely influenced by the emergence of deep-learning techniques. Deep Image Prior (DIP) offers a new approach that forces the recovered image to be synthesized from a given deep architecture. While DIP is quite an effective unsupervised approach, it is deprecated in real-world applications because of the requirement of human assistance. In this work, we aim to find the best-recovered image without the assistance of humans by adding a stopping criterion, which will reach maximum when the iteration no longer improves the image quality. More specifically, we propose to add a pseudo noise to the corrupted image and measure the pseudo-noise component in the recovered image by the orthogonality between signal and noise. The accuracy of the orthogonal stopping criterion has been demonstrated for several tested problems such as denoising, super-resolution, and inpainting, in which 38 out of 40 experiments are higher than 95%.

Background: Prunella vulgaris L. has been an important medicinal plant for the treatment of thyroid gland malfunction and mastitis in China for over 2000 years. There is an urgent need to select effective wavelengths for greenhouse cultivation of P. vulgaris as light is a very important factor in P. vulgaris growth. Here, we described the effects of natural light (control) and UV solar exclusion on the morphological and physiological traits, secondary metabolites contents and antioxidant activities of P. vulgaris. Results: The results showed that UV solar exclusion resulted in remarkable alterations to morphological and biomass traits; significantly reduced the chlorophyll a, chlorophyll b and total chlorophyll contents; significantly enhanced the ratio of chlorophyll a to b; and significantly increased the carotenoid and anthocyanin contents in P. vulgaris. UV solar exclusion significantly increased the catalase (CAT) and peroxidase (POD) activities, increased superoxide dismutase (SOD) and ascorbate peroxidase (APX) activities and slightly decreased the glutathione (GSH) content. UV solar exclusion significantly increased the soluble sugar and H2O2 contents and increased the soluble protein content but significantly decreased the proline content and slightly decreased the MDA content. The secondary metabolite contents (total phenolics, rosmarinic acid, caffeic acid, hyperoside, ursolic acid and oleanolic acid) and in vitro antioxidative properties (DPPH· and ABTS·+scavenging activities) were significantly increased in P. vulgaris spicas under UV solar exclusion. Additionally, the total polysaccharide and total flavonoids contents were slightly increased by UV solar exclusion. The salviaflaside content was significantly reduced by UV solar exclusion. Conclusion: Our study demonstrated that P. vulgaris activates several antioxidant defence systems against oxidative damage caused by UV solar exclusion.

BACKGROUND: Prunella vulgaris L. is a medical plant cultivated in sloping, sun-shaded areas in China. Recently, owing to air-environmental stress, especially drought stress strongly inhibits plant growth and development, the appropriate fertilizer supply can alleviate these effects. However, these is little information about their effects on P. vulgaris growing in arid and semi-arid areas with limited water and fertilizer supply. RESULTS: In this study, water stress decreased the photosynthetic pigment contents, inhibited photosynthetic efficiency, induced photodamage in photosystem 2 (PS2), and decreased leaf instantaneous WUE (WUEi). The decreased net photosynthetic rate (Pn) under medium drought stress compared with the control might result from stomatal limitations. However, fertilizer supply improved photosynthetic capacity by increasing the photosynthetic pigment contents and enhancing photosynthetic efficiency under water deficit. Moreover, medium fertilization also increased WUEi under the two water conditions, but fertilizer supply did little to alleviate the PS2 photodamage caused by drought stress. Hence, drought stress was the primary limitation in the photosynthetic process of P. vulgaris seedlings, while the photosynthetic characteristics of the seedlings exhibited positive responses to fertilizer supply. CONCLUSIONS: Appropriate fertilizer supply is recommended to improve photosynthetic efficiency, enhance WUEi and alleviate photodamage under drought stress.

Prunella vulgaris L. (Labiatae) contains a variety of structurally diverse natural products, primarily rosmarinic acid (RA), ursolic acid (UA) and oleanolic acid (OA), which possess a wide array of biological properties. In the present study, P. vulgaris was harvested at three developmental stages (vegetative, full-flowering and mature-fruiting stages), dissected into stem and leaf tissues and assayed for chemical contents using high performance liquid chromatography. Significant changes in the concentrations of the major secondary metabolites (RA, UA and OA) were observed at the different development stages. The highest concentrations of RA, UA and OA were found at the full-flowering stage (15.83 mg/g dry weight (DW) RA, 1.77 mg/g DW UA and 0.65 mg/g DW OA). Among the different aerial parts of the plant, the concentrations of RA, UA and OA were higher in the leaves than in the stems at the different developmental stages. These results suggest that the full-flowering stage is characterized by the highest concentrations of bioactive compounds. Therefore, this stage may be the optimum point for harvesting P. vulgaris plants. In additional, the leaves of P. vulgaris demonstrated higher RA, UA and OA concentrations than the stems, suggesting higher utilization potential.

A thermo-responsive drug delivery system was reported based on grafting of stimuli-responsive poly(N-isopropylacrylamide) (PNIPA) on the surface of graphene oxide (GO) via atom transfer radical polymerization. The successful synthesis of PNIPA attached on GO (GO-PNIPA) was confirmed by X-ray photoelectron spectrum, X-ray diffraction, atomic force microscope, field-emission scanning electron microscopy, and transmission electron microscopy measurements. Control of drug release through the composite GO-PNIPA was performed by measuring the uptake and release of ibuprofen (IBU). It was found the delivery system demonstrated a much high IBU storage of 280 wt%, attributing to the formation of the hydrogen bonding between the polymers on the GO surface and IBU as well as the large number of internal cavities of the PNIPA chains. In vitro test of IBU release exhibited a narrow pronounced transition at around 22 degrees C, indicating an attractive thermo-sensitive release property of this delivery system. The strategy may pave the way for the use of GO in numerous applications, from drug delivery to thermally responsive micro- and nano-devices.