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Favolaschia claudopus, a wood-inhabiting basidiomycete of the Mycenaceae family, is considered an invasive species that has recently spread from Oceania to Europe. The CIRM-BRFM 2984 strain of this fungus was originally isolated from a basidiome collected from the fallen limb of a decayed oak tree in Southwest France. The genome sequence of this strain shared characteristics with other Mycenaceae species, including a large genome size and enriched content of protein-coding genes. The genome sequence provided here will facilitate further investigation on the factors that contribute to the successful global dissemination of F. claudopus.

Yi Zhang,1,* Kefan Bi,2,* Linfu Zhou,3 Jie Wang,2 Lingtong Huang,4 Yan Sun,5 Guoping Peng,5 Wei Wu2 1Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People’s Republic of China; 2State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People’s Republic of China; 3Department of Biochemistry, Zhejiang University School of Medicine, Hangzhou, People’s Republic of China; 4Department of Critical Care Units, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People’s Republic of China; 5Department of Neurology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Wei Wu; Guoping Peng, Email zjuwuwei@zju.edu.cn; guopingpeng@zju.edu.cnAbstract: Alzheimer’s disease has escalated into a critical public health concern, marked by its neurodegenerative nature that progressively diminishes cognitive abilities. Recognized as a continuously advancing and presently incurable condition, AD underscores the necessity for early-stage diagnosis and interventions aimed at delaying the decline in mental function. Despite the proven efficacy of cerebrospinal fluid and positron emission tomography in diagnosing AD, their broader utility is constrained by significant costs and the invasive nature of these procedures. Consequently, the innovation of blood biomarkers such as Amyloid-beta, phosphorylated-tau, total-tau et al, distinguished by their high sensitivity, minimal invasiveness, accessibility, and cost-efficiency, emerges as a promising avenue for AD diagno

Multimodal analgesic strategy is pivotal for enhanced recovery after surgery. The objective of this trial was to assess the effect of subanesthetic esketamine vs. placebo combined with erector spinae plane block (ESPB) vs. intercostal nerve block (ICNB) on postoperative recovery following thoracoscopic lung resection. This randomized, controlled, 2×2 factorial trial was conducted at a university hospital in Suzhou, China. One hundred adult patients undergoing thoracoscopic lung surgery were randomized to one of four groups (esketamine-ESPB, esketamine-ICNB, placebo-ESPB, and placebo-ICNB) to receive i.v. esketamine 0.3 mg/kg or normal saline placebo combined with ESPB or ICNB using 0.375% ropivacaine 20 mL. All patients received flurbiprofen axetil and patient-controlled fentanyl. The primary outcome was quality of recovery (QoR) at 24 h postoperatively, assessed using the QoR-15 scale, with a minimal clinically important difference of 6.0. The median age was 57 years and 52% were female. No significant interaction effect was found between esketamine and regional blocks on QoR (P=0.215). The QoR-15 score at 24 h was 111.5±5.8 in the esketamine group vs. 105.4±4.5 in the placebo group (difference=6.1, 95% CI, 4.0-8.1; P<0.001); 109.7±6.2 in the ESPB group vs. 107.2±5.6 in the ICNB group (difference=2.5, 95% CI, 0.2-4.9; P=0.033; not statistically significant after Bonferroni correction). Additionally, esketamine resulted in higher QoR-15 scores at 48 h (difference=4.6) and hospital discharge (difference=1.6), while ESPB led to a higher QoR-15 score at 48 h (difference=3.0). For patients undergoing thoracoscopic lung resection, subanesthetic esketamine improved QoR after surgery, while ICNB can be used interchangeably with ESPB as a component of multimodal analgesia.

The study aims to determine the influence of academic performance to employability of the students in the field of education.The experimental research design will be structured for this study in which numeric score rating gathered the perception of the respondents observed in a structured pattern. This research design will deal at the problem of the study scientifically, to establish a clear cause and effect of Education Major students’ academic performance to their employability.The students’ perception on their employability in the field of education revealed that many disagreed their course is very important in their future work, and expressed that they are not more likely to work on a private school Majority of the students disagreed that they are more likely to work on a public school, and shared that their field of study is in-demand in terms of work opportunities in the education sector. Further, more students disagreed on the other indicators which are;  they are confident that their skills and knowledge will match their work and confident that they will work in teaching or education sector, teachers are more likely to teach students than doing admin works, student skills are honed enough to move in an education work environment and they chose their field of study based on work demands of the education sector, teachers are more likely to do admin works than teaching students, students are more ready to teach and experience work environment, and lastly students already planned where they are going to work.

The diversification of future aquaculture feed formulas is expected to rise due to the increasing fish feed production. Changes in formulas and the corresponding adjustments to processing parameters can create variations in the pellet physical quality (PPQ). Despite these potential changes in PPQ, the impact of varying PPQ on the physiological response of fish is often undervalued or overlooked. In this study, we investigated the effect of protein sources and extrusion processing conditions through PPQ on the gastrointestinal chyme emptying, feed intake and digestibility of spotted seabass. Four diets were formulated following a 2×2 factorial design, with two protein sources (fish meal [FM] and cottonseed protein concentrate [CPC]) and two processing conditions (PC-M, preconditioning water content 30 % and die temperature 120 °C vs. PC-H, preconditioning water content 22 % and die temperature 140 °C). Fish (46 g) were fed twice daily to apparent satiation. Feed intake, nutrient digestibility and fish performance were evaluated over a 20-day experimental period. At the end of the experiment, chyme was collected after fish were fed restrictively one meal (0.95 % BW/d) to determine gastrointestinal emptying and water fluxes. Chyme was quantitatively collected from stomach and intestine at 2, 4, 6, 8, 12 and 24 h postprandial. Altering protein source affected PPQ as well as nutrient digestibility. Compared to FM-pellets, CPC-pellet lowered nutrients and energy digestibility. In addition, CPC-pellets led to a decrease in postprandial plasma glucose level and hepatosomatic index than FM-pellets. Processing conditions steered PPQ and influenced the physiological response of fish. PC-M resulted in a 23 % increase in stomach water flux at 4 h postprandial and a 2 % rise in stomach chyme water content than PC-H. Moreover, PC-H led to higher protein and fat digestibility than PC-M. Both protein source and processing condition treatments did not differ in feed intake, gastric e

The construction of a thin, uniform, and robust solid electrolyte interphase (SEI) film on the surface of active materials is pivotal for enhancing the overall performance of lithium-ion batteries (LiBs). However, conventional electrolytes often fail to achieve the desired SEI characteristics. In this work, we introduced 1,3,6-hexanetrinitrile (HTCN) in the baseline electrolyte (BE) of 1.0 M LiPF6 in Ethylene Carbonate/Dimethyl Carbonate (EC/DMC) (3:7 by volume) with 5 wt.% fluoroethylene carbonate (FEC), denoted as BE-FH. By systematically investigating the influence of FEC: HTCN weight ratios on the electrochemical performance of graphite anodes, we identified an optimal composition (FEC:HTCN = 5:4 by weight, denoted as BE-FH54) that demonstrated greatly improved initial Coulombic efficiency, rate capability, and cycling stability compared with the baseline electrolyte. Deviations from the optimal FEC:HTCN ratio resulted in the formation of either small cracks or excessively thick SEI layers. The enhanced performance of BE-FH54-based LiB is mainly ascribed to the synergistic effect of FEC and HTCN in forming a robust, thin, homogeneous, and ion-conducting SEI. This research highlights the importance of rational electrolyte design in enhancing the electrochemical performance of graphite anodes in LiBs and provides insights into the role of nitrile-based additives in modulating the SEI properties.

The disturbed gut microbiota is a key factor in activating the aflatoxin B1 (AFB1)-induced liver pyroptosis by promoting inflammatory hepatic injury; however, the pathogen associated molecular pattern (PAMP) from disturbed gut microbiota and its mechanism in activating liver pyroptosis remain undefined. By transplanting AFB1-originated fecal microbiota and sterile fecal microbial metabolites filtrate, we determined the association of PAMP in AFB1-induced liver pyroptosis. Notably, AFB1-originated sterile fecal microbial metabolites filtrate were more active in triggering liver pyroptosis in mice, as compared to parental fecal microbiota. This result supported a critical role of the metabolic homeostasis of gut microbiota in AFB1-induced liver pyroptosis, rather than an injurious response to direct exposure of AFB1 in liver. Among the gut-microbial metabolites, pipecolic acid and norepinephrine were proposed to bind TLR4 and NLRP3, the upstream proteins of pyroptosis signaling pathway. Besides, the activations of TLR4 and NLRP3 were linearly correlated with the concentrations of pipecolic acid and norepinephrine in the serum of mice. In silenced expression of TLR4 and NLRP3 in HepG2 cells, pipecolic acid or norepinephrine did not able to activate hepatocyte pyroptosis. These results demonstrated the necessity of gut microbial metabolism in sustaining liver homeostasis, as well as the potential to provide new insights into targeted intervention for AFB1 hepatotoxicity. © 2024 Elsevier B.V.

Materials with tunable thermal properties at room temperature have attracted great attention owing to their applications as solid-state thermal switches for thermal management. As a typical ferroelectric material, barium titanate (BaTiO3) is regarded as promising candidate for thermal switch because its ferroelectric polarization can be switched by external electric field. However, BaTiO3 presents a low-symmetry tetragonal phase at room temperature, calculating its thermal conductivity through first-principles calculations is rather time-consuming. By solving Boltzmann transport equation with the interatomic force constants from second-principles model, we develop a time-efficient strategy to calculate the thermal conductivity of tetragonal BaTiO3. The calculated thermal properties of BaTiO3 based on the strategy are consistent with those from first-principles calculations while the calculation time is greatly reduced. It is found that both in-plane and out-of-plane thermal conductivity can be adjusted by external electric field. A thermal switch with a switch ratio of 5.1 at room temperature is predicted based on the polarization switching under external electric field. This work not only provides an efficient approach to study thermal properties but also suggests a room-temperature thermal switch controlled by an electric field.

Urea electrolysis is an appealing topic for hydrogen production due to its ability to extract hydrogen at a lower potential. However, it is plagued by sluggish kinetics and noble-metal catalyst requirements. Herein, we developed nickel-iron-layered double hydroxide (NiFe-LDH) nanolayers with abundant oxygen vacancies (OV) via synergistically etching nickel foam with Fe3+ and Cl- ions, enabling the efficient conversion of urea into H2 and N2. The synthesized OV-NiFe-LDH exhibits a lower potential (1.30 vs. reversible hydrogen electrode, RHE) for achieving 10 mA cm−2 in the urea oxidation reaction (UOR), surpassing most recently reported Ni-based electrodes. OV provides favorable conductivity and a large surface area, which results in a 4.1-fold in electron transport and a 5.1-fold increase in catalyst reactive sites. Density Functional Theory (DFT) calculations indicate that OV can lower the adsorption energy of urea, and enhance the bonding strength of *CONHNH, giving rise to improved UOR. This study provides a viable path toward economical and efficient production of high-purity hydrogen. © 2024 Elsevier Inc.

Serving deep learning (DL) models on relational data has become a critical requirement across diverse commercial and scientific domains, sparking growing interest recently. In this visionary paper, we embark on a comprehensive exploration of representative architectures to address the requirement. We highlight three pivotal paradigms: The state-of-the-art DL-centric architecture offloads DL computations to dedicated DL frameworks. The potential UDF-centric architecture encapsulates one or more tensor computations into User Defined Functions (UDFs) within the relational database management system (RDBMS). The potential relation-centric architecture aims to represent a large-scale tensor computation through relational operators. While each of these architectures demonstrates promise in specific use scenarios, we identify urgent requirements for seamless integration of these architectures and the middle ground in-between these architectures. We delve into the gaps that impede the integration and explore innovative strategies to close them. We present a pathway to establish a novel RDBMS for enabling a broad class of data-intensive DL inference applications. © 2024 Copyright held by the owner/author(s).

Quantum geometry is a fundamental concept to characterize the local properties of quantum states. It is recently demonstrated that saturating certain quantum geometric bounds allows a topological Chern band to share many essential features with the lowest Landau level, facilitating fractionalized phases in moir\'e flat bands. In this work, we systematically extend the consequence and universality of saturated geometric bounds to arbitrary Landau levels by introducing a set of single-particle states, which we term as ``generalized Landau levels''. These generalized Landau levels exhibit exactly quantized values of integrated trace of quantum metric determined by their corresponding Landau level indices, regardless of the nonuniformity of their quantum geometric quantities. We derive all geometric quantities for individual and multiple generalized Landau levels, discuss their relations, and understand them in light of the theory of holomorphic curves and moving frames. We further propose a model by superposing few generalized Landau levels which is supposed to capture a large portion of the single-particle Hilbert space of a generic Chern band analogous to the first Landau level. Using this model, we employ exact diagonalization to identify a single-particle geometric criterion for permitting the non-Abelian Moore-Read phase, which is potentially useful for future engineering of moir\'e materials and beyond. We use a double twisted bilayer graphene model with only adjacent layer hopping term to show the existence of first generalized Landau level type narrow band and zero-field Moore-Read state at the second magic angle which serves as a promising starting point for more detailed future studies. We expect that generalized Landau levels will serve as a systematic tool for analyzing topological Chern bands and fractionalized phases therein., Comment: 40 pages, 11 figures

Electrochemical water-splitting is widely acknowledged as a renewable strategy for hydrogen production, but it is primarily constrained by the sluggish reaction kinetics of the anode oxygen evolution reaction (OER). In our study, we employ a fast room-temperature corrosion engineering strategy for the construction of a sulfur-doped Ni-Fe layered dihydroxide catalyst (S-NiFe LDH). With the assistance of a sulfur source, microsphere morphology with an ultra-thin lamellar surface cross-arrangement can be rapidly grown on the surface of an iron foam substrate, ensuring a substantial electrochemical interface. The composition of Ni species in the catalysts can be regulated by simply adjusting the amount of Ni2+ and reaction time. Functioning as an OER catalyst, the S-NiFe LDH demonstrates high activity and reaction kinetics, featuring a minimal overpotential of 120.0 mV to deliver a current density of 10 mA cm−2, a small Tafel slope of 39.5 mV dec−1 and a notable electrical double-layer capacitance (Cdl) of 31.3 mF cm−2. The remarkable electrocatalytic performance can be attributed to its distinctive three-dimensional (3D) structure and sulfur dopants, which effectively regulate the electrochemical interface and electronic structure of NiFe LDH. This work provides valuable insights for expeditious materials design.

Phyllosticta citricarpa is an important citrus-pathogen and a quarantine organism in the European Union. Its recently described relative, P. paracitricarpa, is very closely related and not listed as a quarantine organism. P. paracitricarpa is very difficult to distinguish from P. citricarpa, since its morphological features overlap and the barcoding gene sequences that were originally used to delimit them as distinct species have a low number of species-specific polymorphisms that have subsequently been shown to overlap between the two clades. Therefore, we performed extensive genomic analyses to determine whether the genetic variation between P. citricarpa and P. paracitricarpa strains should be considered to represent infraspecific variation within P. citricarpa, or whether it is indicative of distinct species. Using a phylogenomic analysis with 3,000 single copy ortholog genes and whole-genome comparisons, we determined that the variation between P. citricarpa and P. paracitricarpa can be considered as infraspecies variation within P. citricarpa. We also determined the level of variation in mitochondrial assemblies of several Phyllosticta species and concluded there are only minimal differences between the assemblies of P. citricarpa and P. paracitricarpa. Thus, using several orthogonal approaches, we here demonstrate that variation within the nuclear and mitochondrial genomes of other Phyllosticta species is larger than variation between genomes obtained from P. citricarpa and P. paracitricarpa strains. Thus, P. citricarpa and P. paracitricarpa should be considered as conspecific.

Quantong Wu,1 Fuchao Xu,2 Jie Wang,3 Ming Jiang2 1Department of Anesthesiology, Nanjing Drum Tower Hospital Clinical College, Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China; 2Emergency Department, Nanjing Drum Tower Hospital Clinical College, Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China; 3Neurosurgery department, Nanjing Drum Tower Hospital Clinical College, Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of ChinaCorrespondence: Ming Jiang, Department of Anesthesiology, Nanjing Drum Tower Hospital Clinical College, Nanjing Medical University, Gulou District, Nanjing, Jiangsu, People’s Republic of China, Email 2021122013@stu.njmu.edu.cnPurpose: To systematically evaluate the effectiveness and safety of ketamine in preventing propofol injection pain (PIP).Patients and Methods: The electronic databases including PubMed, Embase, Web of Science, and Cochrane Library were searched from their inception until 2 August 2023. Randomized controlled trials (RCT) comparing ketamine with placebo or other interventions to alleviate PIP in adults were included. Fixed-effects or random-effects models were used to calculate pooled risk ratios (RR) and corresponding 95% confidence intervals (CI) based on the heterogeneity of the studies included.Results: Thirteen RCTs involving 2105 patients were included. In terms of reducing the incidence of PIP, ketamine is more effective than placebo (RR = 0.43, 95% CI = [0.34, 0.55], P < 0.00001), lidocaine (RR = 0.70, 95% CI = [0.55, 0.90], P = 0.005), dexmedetomidine (RR = 0.52, 95% CI = [0.40, 0.66], P < 0.00001), and thiopental (RR = 0.25, 95% CI = [0.08, 0.83], P = 0.02). In reducing the incidence of severe PIP, ketamine is superior to placebo (RR = 0.12, 95% CI = [0.08, 0.19], P < 0.00001), and lidocaine (RR = 0.34, 95% CI = [0.21, 0.56], P < 0.0001), except dexmedetomidine (RR = 0.20, 95% CI = [0.04, 1.13], P = 0.07), and thiopental (RR = 0.33, 95% CI

Revealing the oxidation behavior of superalloys is crucial for optimizing material properties and extending service life. This study investigated the oxidation behavior of superalloy GH4738 under stress states at 850 degrees C. High-throughput specimens were fabricated to withstand different stresses at the same time. Isothermal oxidation samples were analyzed using the mass gain method to obtain oxidation kinetic curves. The results show that the external stress below 200 MPa could improve the oxidation resistance of the GH4738. With tensile stress increasing, the oxide layer becomes thinner, denser and more complete, while internal oxidation decreases. The tensile stress alters the structure of the external oxide layer from a two-layer to a three-layer configuration. The Cr2O3 oxide layer inhibits the outward diffusion of Ti, leading to Ti enrichment at the oxide-matrix interface and altering the oxidation mechanism of GH4738., Funding Agencies|Shandong Provincial Natural Science Foundation for Youths; National Key R&D Program of China [2021YFB3700401]; Zhejiang Provincial Natural Science Foundation [LQ21E030002]; Youth Innovation team Project of Higher Education Institutions in Shandong Province [2022KJ272]; [ZR2022QE234]

This study investigates the effect of spatial release from masking (SRM) in bilateral bone conduction (BC) stimulation at the mastoid. Nine adults with normal hearing were tested to determine SRM based on speech recognition thresholds (SRTs) in simulated spatial configurations ranging from 0 to 180 degrees. These configurations were based on nonindividualized head-related transfer functions. The participants were subjected to sound stimulation through either air conduction (AC) via headphones or BC. The results indicated that both the angular separation between the target and the masker, and the modality of sound stimulation, significantly influenced speech recognition performance. As the angular separation between the target and the masker increased up to 150 degrees, both BC and AC SRTs decreased, indicating improved performance. However, performance slightly deteriorated when the angular separation exceeded 150 degrees. For spatial separations less than 75 degrees, BC stimulation provided greater spatial benefits than AC, although this difference was not statistically significant. For separations greater than 75 degrees, AC stimulation offered significantly more spatial benefits than BC. When speech and noise originated from the same side of the head, the "better ear effect" did not significantly contribute to SRM. However, when speech and noise were located on opposite sides of the head, this effect became dominant in SRM., Funding Agencies|Overseas Experts Initiative of Department of Technology and Science of Guangdong Province

We present a statistical study of the properties of diffuse HI in ten nearby galaxies, comparing the HI detected by the single-dish telescope FAST (FEASTS program) and the interferometer VLA (THINGS program), respectively. The THINGS' observation missed HI with a median of 23% due to the short-spacing problem of interferometry and limited sensitivity. We extract the diffuse HI by subtracting the dense HI, which is obtained from the THINGS data with a uniform flux-density threshold, from the total HI detected by FAST. Among the sample, the median diffuse-HI fraction is 34%, and more diffuse HI is found in galaxies exhibiting more prominent tidal-interaction signatures. The diffuse HI we detected seems to be distributed in disk-like layers within a typical thickness of $1\,\text{kpc}$, different from the more halo-like diffuse HI detected around NGC 4631 in a previous study. Most of the diffuse HI is cospatial with the dense HI and has a typical column density of $10^{17.7}$-$10^{20.1}\,\text{cm}^{-2}$. The diffuse and dense HI exhibits a similar rotational motion, but the former lags by a median of 25% in at least the inner disks, and its velocity dispersions are typically twice as high. Based on a simplified estimation of circum-galactic medium properties and assuming pressure equilibrium, the volume density of diffuse HI appears to be constant within each individual galaxy, implying its role as a cooling interface. Comparing with existing models, these results are consistent with a possible link between tidal interactions, the formation of diffuse HI, and gas accretion., Comment: 45 pages, 23 figures. In press at ApJ. Data will be released at the FEASTS site upon publication

We investigate emission line galaxies across cosmic time by combining the modified L-Galaxies semi-analytical galaxy formation model with the JiuTian cosmological simulation. We improve the tidal disruption model of satellite galaxies in L-Galaxies to address the time dependence problem. We utilise the public code CLOUDY to compute emission line ratios for a grid of HII region models. The emission line models assume the same initial mass function as that used to generate the spectral energy distribution of semi-analytical galaxies, ensuring a coherent treatment for modelling the full galaxy spectrum. By incorporating these emission line ratios with galaxy properties, we reproduce observed luminosity functions for H$\alpha$, H$\beta$, [OII], and [OIII] in the local Universe and at high redshifts. We also find good agreement between model predictions and observations for auto-correlation and cross-correlation functions of [OII]-selected galaxies, as well as their luminosity dependence. The bias of emission line galaxies depends on both luminosity and redshift. At lower redshifts, it remains constant with increasing luminosity up to around $\sim 10^{42.5}\rm \, erg\,s^{-1}$ and then rises steeply for higher luminosities. The transition luminosity increases with redshift and becomes insignificant above $z$=1.5. Generally, galaxy bias shows an increasing trend with redshift. However, for luminous galaxies, the bias is higher at low redshifts, as the strong luminosity dependence observed at low redshifts diminishes at higher redshifts. We provide a fitting formula for the bias of emission line galaxies as a function of luminosity and redshift, which can be utilised for large-scale structure studies with future galaxy surveys., Comment: 22 pages, 18 figures, 5 tables, including 3 Appendix; Accepted by MNRAS

The Voids-within-Voids-within-Voids (VVV) project used dark-matter-only simulations to study the abundance and structure of dark matter haloes over the full mass range populated in the standard $\Lambda\mathrm{CDM}$ cosmology. Here we explore how baryonic effects modify these results for $z=0$ halo masses in the range $10^4$ to $10^7~\mathrm{M_\odot}$, below the threshold for galaxy formation. Our main study focuses on three simulations from identical initial conditions at $z=127$, one following dark matter only, one including non-radiative gas, and one additionally including the baryonic physics relevant in this halo mass range (cooling and photoheating). In the non-radiative simulation, above $10^{5.5}~\mathrm{M_\odot}$, halo abundance and internal structure are very similar to the dark-matter-only simulation, and the baryon to dark matter ratio is everywhere close to the cosmic value. At lower mass, this ratio drops and haloes are less concentrated and less massive in the non-radiative case. Test simulations at higher resolution show this to be mainly a resolution effect; the expected drop in baryon content due to residual pressure effects only becomes substantial for $z=0$ haloes below $\sim 10^{2.7}~\mathrm{M_\odot}$. However, gas is heated by reionization at $z=6$ in our ``full physics'' run, and this results in almost complete expulsion of gas from all haloes in our simulated mass range. This suppresses the halo mass function by $\sim 30 \%$, lowers halo concentration, and consequently weakens the dark matter annihilation signal by $\sim 40-60 \%$., Comment: 12+2 pages, 9 figures

The Five-hundred-meter Aperture Spherical radio Telescope (FAST) has the largest aperture and a 19-beam L-band receiver, making it powerful for investigating the neutral hydrogen atomic gas (HI) in the universe. We present HiFAST (https://hifast.readthedocs.io), a dedicated, modular, and self-contained calibration and imaging pipeline for processing the HI data of FAST. The pipeline consists of frequency-dependent noise diode calibration, baseline fitting, standing wave removal using an FFT-based method, flux density calibration, stray radiation correction, and gridding to produce data cubes. These modules can be combined as needed to process the data from most FAST observation modes: tracking, drift scanning, On-The-Fly mapping, and most of their variants. With HiFAST, the RMS noises of the calibrated spectra from all 19 beams were only slightly (~ 5%) higher than the theoretical expectation. The results for the extended source M33 and the point sources are consistent with the results from Arecibo. The moment maps (0,1 and 2) of M33 agree well with the results from the Arecibo Galaxy Environment Survey (AGES) with a fractional difference of less than 10%. For a common sample of 221 sources with signal-to-noise ratio S/N >10 from the Arecibo Legacy Fast ALFA (ALFALFA) survey, the mean value of fractional difference in the integrated flux density, $S_{\mathrm{int}}$, between the two datasets is approximately 0.005 %, with a dispersion of 15.4%. Further checks on the integrated flux density of 23 sources with seven observations indicate that the variance in the flux density of the source with luminous objects ($S_\mathrm{int}$ $ > 2.5$ Jy km s$^{-1}$) is less than 5%. Our tests suggest that the FAST telescope, with the efficient, precise, and user-friendly pipeline HiFAST, will yield numerous significant scientific findings in the investigation of the HI in the universe., Comment: Accepted by SCPMA. 21 pages, 14 figures. The pipeline is accessible at https://hifast.readthedocs.io

INTRODUCTION: Metabotropic glutamate receptor 5 (mGluR5) is involved in regulating integrative brain function and synaptic transmission. Aberrant mGluR5 signaling and relevant synaptic failure play a key role in the pathophysiological mechanism of Alzheimer's disease (AD). METHODS: Ten cognitively impaired (CI) individuals and 10 healthy controls (HCs) underwent 18FSynVesT-1 and 18FPSS232 positron emission tomography (PET)/magnetic resonance to assess synaptic density and mGluR5 availability. The associations between mGluR5 availability and synaptic density were examined. A mediation analysis was performed to investigate the possible mediating effects of mGluR5 availability and synaptic loss on the relationship between amyloid deposition and cognition. RESULTS: CI patients exhibited lower mGluR5 availability and synaptic density in the medial temporal lobe than HCs. Regional synaptic density was closely associated with regional mGluR5 availability. mGluR5 availability and synaptic loss partially mediated the relationship between amyloid deposition and cognition. CONCLUSIONS: Reductions in mGluR5 availability and synaptic density exhibit similar spatial patterns in AD and are closely linked. Highlights - Cognitively impaired patients exhibited lower mGluR5 availability and synaptic density in the medial temporal lobe than HCs. - Reductions in mGluR5 availability and synaptic density exhibit similar spatial patterns in AD. - Regional synaptic density was closely associated with regional mGluR5 availability. - mGluR5 availability and synaptic loss partially mediated the relationship between amyloid deposition and global cognition. - With further research, modulating mGluR5 availability might be a potential therapeutic strategy for improving synaptic function in AD.

Many of the satellites of galactic-mass systems such as the Miky Way, Andromeda and Centaurus A show evidence of coherent motions to a larger extent than most of the systems predicted by the standard cosmological model. It is an open question if correlations in satellite orbits are present in systems of different masses. Here , we report an analysis of the kinematics of satellite galaxies around massive galaxy groups. Unlike what is seen in Milky Way analogues, we find an excess of diametrically opposed pairs of satellites that have line-of-sight velocity offsets from the central galaxy of the same sign. This corresponds to a $\pmb{6.0\sigma}$ ($\pmb{p}$-value $\pmb{=\ 9.9\times10^{-10}}$) detection of non-random satellite motions. Such excess is predicted by up-to-date cosmological simulations but the magnitude of the effect is considerably lower than in observations. The observational data is discrepant at the $\pmb{4.1\sigma}$ and $\pmb{3.6\sigma}$ level with the expectations of the Millennium and the Illustris TNG300 cosmological simulations, potentially indicating that massive galaxy groups assembled later in the real Universe. The detection of velocity correlations of satellite galaxies and tension with theoretical predictions is robust against changes in sample selection. Using the largest sample to date, our findings demonstrate that the motions of satellite galaxies represent a challenge to the current cosmological model., Comment: 28 pages, 9 figures, accepted for publication in Nature Astronomy

Environment plays a pivotal role in shaping the evolution of satellite galaxies. Analyzing the properties related to the cold gas phase of satellites provides insights into unravelling the complexity of environmental effects. We use the hydro-dynamical simulations Illustris TNG and Eagle, and the semi-analytic models (SAMs) GAEA and L-Galaxies, in comparison with recent observations from the Westerbork Synthesis Radio Telescope (WSRT), to investigate the properties of satellite galaxies hosted by halos with mass $M_{200}>10^{12.8}M_\odot$, and within projected regions $\le 1.1$ virial radius $R_{200}$. Generally, satellite galaxies selected from semi-analytic models have more HI than those selected from hydro-dynamical simulations across all projected radii, e.g. more than 30% of satellites in the two hydro-simulations are HI depleted, while this fraction is almost zero in SAMs. Furthermore, both hydro-dynamical simulations and SAMs reproduce the observed decrease of HI content and specific star-formation rate (sSFR) towards the halo centre. However, the trend is steeper in two hydro-dynamical simulations TNG and EAGLE, resulting in a better agreement with the observational data, especially in more massive halos. By comparing the two version of GAEA, we find that the inclusion of ram-pressure stripping of cold gas significantly improves the predictions on HI fractions. The refined hot gas stripping method employed in one of the two L-Galaxies models also yields improved results., Comment: 13 pages, 8 figures, 2 table. Accepted by MNRAS

Yafei Zhou,1,2 Rui Zhou,1,2 Wenjun Huang,1,2 Jie Wang,1,2 Congshan Jiang,1,2 Anmao Li,1,2 Christopher LH Huang,3 Yanmin Zhang1,2,4 1Key Laboratory of Precision Medicine to Pediatric Diseases of Shaanxi Province, Affiliated Children’s Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China; 2Shaanxi Institute for Pediatric Diseases, Affiliate Children’s Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China; 3Physiological Laboratory, University of Cambridge, Cambridge, UK; 4Department of Cardiology, Xi’an Children’s Hospital, Affiliated Children’s Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of ChinaCorrespondence: Yanmin Zhang, Key Laboratory of Precision Medicine to Pediatric Diseases of Shaanxi Province, Affiliate Children’s Hospital of Xi’an Jiaotong University, No. 69 Xi Ju Yuan Xiang, Xi’an, Shaanxi, 710000, People’s Republic of China, Tel +86 029-87692527, Fax +86 029-87692000, Email ymzh628@126.comBackground and Objectives: Gene expression, morphology, and electrophysiological combination are essential for assessing the dynamic development of human induced pluripotent stem cell-derived atrial- and ventricular-like cardiomyocytes (iPS-AM and iPS-VM, respectively).Methods: For iPS-AM/VM differentiation, we performed the small molecule-based temporal modulation of the retinoic acid and bone morphogenetic protein signaling pathways. We investigated the gene expression and morphology using immunofluorescence, quantitative real-time polymerase chain reaction, flow cytometry, and transmission electron microscopy as well as registered electrophysiological functions using a whole-cell patch clamp on days 20, 30, and 60 post-differentiations.Results: Pan-cardiomyocyte marker, including troponin T2 (TNNT2) and alpha-actinin-2 (ACTN2), expressions increased both in iPS-AMs and iPS-VMs. Similarly, the mRNA expression of both iPS-AM-specific markers, ie, na

Ruoxi Cheng,1,&ast; Jie Wang,1,&ast; Qing Wu,1 Peng Peng,1 Guolin Liao,1 Xiuping Luo,1 Zhihai Liang,2 Jiean Huang,1,&ast; Mengbin Qin1,&ast; 1Department of Gastroenterology, the Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, People’s Republic of China; 2Department of Gastroenterology, the First Affiliated Hospital of Guangxi Medical University, Nanning, 530007, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Jiean Huang; Mengbin Qin, Department of Gastroenterology, the Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, People’s Republic of China, Email hjagxmu@163.com; dr.mmbin@hotmail.comPurpose: This study was aimed at exploring the use of the acute gastrointestinal injury (AGI) grade and sensitive biomarkers to investigate gastrointestinal (GI) injury in early stage of acute pancreatitis (AP).Patients and Methods: The AGI grade was used to evaluate intestinal function. Any GI injury above grade I (grades II–IV) was considered as severe. An AP rat model was created by retrograde injection of 4% sodium taurocholate. The pancreatic and intestinal histopathology scores were calculated by hematoxylin-eosin staining. Human and rat sera were assessed using ELISA. Tight junction (TJ) proteins were detected by Western blotting.Results: In clinical study, the GI injury rate in mild acute pancreatitis (MAP), moderate severe acute pancreatitis (MSAP), and severe acute pancreatitis (SAP) groups was 26.8%, 78.4%, and 94.8%, respectively (P < 0.05). Diamine oxidase (DAO), histidine decarboxylase (HDC), and matrix metalloproteinase 8 (MMP8) serum levels were higher in AP patients than in healthy people (P < 0.05). Patients with GI injury had higher serum levels of DAO, HDC, and MMP8 than those without GI injury (P < 0.05). In animal experiments, the serum levels of DAO, HDC, and MMP8 were higher in the AP group than in normal and sham-operated (SO

Liqin Yuan, Lin Cui, Jie Wang, Li Gong Department of Gynecology, Rizhao People’s Hospital, Rizhao, Shandong Province, 276800, People’s Republic of ChinaCorrespondence: Li Gong, Rizhao People’s Hospital, Rizhao, Shandong Province, 276800, People’s Republic of China, Tel +86-18663351821, Email gongli0768@163.comPurpose: Meigs’ syndrome is a rare gynecological disease characterized by the triad of benign ovarian tumor, ascites, and pleural effusion. Ovarian malignancies should be highly suspected in a postmenopausal woman with a pelvic mass, ascites, hydrothorax, and an elevated carbohydrate antigen 125 (CA125) level. It can be challenging to make a preoperative diagnosis of Meigs’ syndrome. In this report, we present a case of Meigs’ syndrome caused by an ovarian fibrothecoma and review the relevant literature to raise awareness and avoid misdiagnosis.Case Presentation: An 82-year-old woman with a 2-week history of abdominal distension was admitted to the Department of Gynecology. Ultrasound and thoracoabdominal computed tomography scans showed a left-sided hypoechoic mass in the pelvic cavity with bilateral pleural effusion and massive ascites. The CA125 concentration was 1040 U/mL (normal, 0– 35 U/mL). With a working diagnosis of ovarian malignancy, the patient underwent ultrasound-guided fine-needle puncture of the pelvic mass and paracentesis to drain the ascites. The fine-needle puncture and paracentesis fluid analysis results revealed that the ascites did not contain any tumor cells, and the pelvic mass was identified as a spindle cell tumor. Immunohistochemistry confirmed that it was a sex-cord stromal tumor. Total abdominal hysterectomy and bilateral adnexectomy were performed under general anesthesia. The pathology results confirmed the mass to have been an ovarian fibrothecoma. At the 2-month postoperative follow-up, the ascites and hydrothorax had resolved and not recurred, and the CA125 level was normal.Conclusion: Despite the high suspicio

Reinforcement Learning (RL)-based recommender systems have demonstrated promising performance in meeting user expectations by learning to make accurate next-item recommendations from historical user-item interactions. However, existing offline RL-based sequential recommendation methods face the challenge of obtaining effective user feedback from the environment. Effectively modeling the user state and shaping an appropriate reward for recommendation remains a challenge. In this paper, we leverage language understanding capabilities and adapt large language models (LLMs) as an environment (LE) to enhance RL-based recommenders. The LE is learned from a subset of user-item interaction data, thus reducing the need for large training data, and can synthesise user feedback for offline data by: (i) acting as a state model that produces high quality states that enrich the user representation, and (ii) functioning as a reward model to accurately capture nuanced user preferences on actions. Moreover, the LE allows to generate positive actions that augment the limited offline training data. We propose a LE Augmentation (LEA) method to further improve recommendation performance by optimising jointly the supervised component and the RL policy, using the augmented actions and historical user signals. We use LEA, the state and reward models in conjunction with state-of-the-art RL recommenders and report experimental results on two publicly available datasets.

We present a new framework to address the non-convex robust hypothesis testing problem, wherein the goal is to seek the optimal detector that minimizes the maximum of worst-case type-I and type-II risk functions. The distributional uncertainty sets are constructed to center around the empirical distribution derived from samples based on Sinkhorn discrepancy. Given that the objective involves non-convex, non-smooth probabilistic functions that are often intractable to optimize, existing methods resort to approximations rather than exact solutions. To tackle the challenge, we introduce an exact mixed-integer exponential conic reformulation of the problem, which can be solved into a global optimum with a moderate amount of input data. Subsequently, we propose a convex approximation, demonstrating its superiority over current state-of-the-art methodologies in literature. Furthermore, we establish connections between robust hypothesis testing and regularized formulations of non-robust risk functions, offering insightful interpretations. Our numerical study highlights the satisfactory testing performance and computational efficiency of the proposed framework., Comment: 26 pages, 2 figures

Retrieval-augmented generation (RAG) can enhance the generation quality of large language models (LLMs) by incorporating external token databases. However, retrievals from large databases can constitute a substantial portion of the overall generation time, particularly when retrievals are periodically performed to align the retrieved content with the latest states of generation. In this paper, we introduce PipeRAG, a novel algorithm-system co-design approach to reduce generation latency and enhance generation quality. PipeRAG integrates (1) pipeline parallelism to enable concurrent retrieval and generation processes, (2) flexible retrieval intervals to maximize the efficiency of pipeline parallelism, and (3) a performance model to automatically balance retrieval quality and latency based on the generation states and underlying hardware. Our evaluation shows that, by combining the three aforementioned methods, PipeRAG achieves up to 2.6$\times$ speedup in end-to-end generation latency while improving generation quality. These promising results showcase the effectiveness of co-designing algorithms with underlying systems, paving the way for the adoption of PipeRAG in future RAG systems.

Recent advancements in data-driven approaches, such as Neural Operator (NO), have demonstrated their effectiveness in reducing the solving time of Partial Differential Equations (PDEs). However, one major challenge faced by these approaches is the requirement for a large amount of high-precision training data, which needs significant computational costs during the generation process. To address this challenge, we propose a novel PDE dataset generation algorithm, namely Differential Operator Action in Solution space (DiffOAS), which speeds up the data generation process and enhances the precision of the generated data simultaneously. Specifically, DiffOAS obtains a few basic PDE solutions and then combines them to get solutions. It applies differential operators on these solutions, a process we call 'operator action', to efficiently generate precise PDE data points. Theoretical analysis shows that the time complexity of DiffOAS method is one order lower than the existing generation method. Experimental results show that DiffOAS accelerates the generation of large-scale datasets with 10,000 instances by 300 times. Even with just 5% of the generation time, NO trained on the data generated by DiffOAS exhibits comparable performance to that using the existing generation method, which highlights the efficiency of DiffOAS.

In the realm of automated robotic surgery and computer-assisted interventions, understanding robotic surgical activities stands paramount. Existing algorithms dedicated to surgical activity recognition predominantly cater to pre-defined closed-set paradigms, ignoring the challenges of real-world open-set scenarios. Such algorithms often falter in the presence of test samples originating from classes unseen during training phases. To tackle this problem, we introduce an innovative Open-Set Surgical Activity Recognition (OSSAR) framework. Our solution leverages the hyperspherical reciprocal point strategy to enhance the distinction between known and unknown classes in the feature space. Additionally, we address the issue of over-confidence in the closed set by refining model calibration, avoiding misclassification of unknown classes as known ones. To support our assertions, we establish an open-set surgical activity benchmark utilizing the public JIGSAWS dataset. Besides, we also collect a novel dataset on endoscopic submucosal dissection for surgical activity tasks. Extensive comparisons and ablation experiments on these datasets demonstrate the significant outperformance of our method over existing state-of-the-art approaches. Our proposed solution can effectively address the challenges of real-world surgical scenarios. Our code is publicly accessible at https://github.com/longbai1006/OSSAR., Comment: To appear in IEEE ICRA 2024

Cutting planes (cuts) play an important role in solving mixed-integer linear programs (MILPs), as they significantly tighten the dual bounds and improve the solving performance. A key problem for cuts is when to stop cuts generation, which is important for the efficiency of solving MILPs. However, many modern MILP solvers employ hard-coded heuristics to tackle this problem, which tends to neglect underlying patterns among MILPs from certain applications. To address this challenge, we formulate the cuts generation stopping problem as a reinforcement learning problem and propose a novel hybrid graph representation model (HYGRO) to learn effective stopping strategies. An appealing feature of HYGRO is that it can effectively capture both the dynamic and static features of MILPs, enabling dynamic decision-making for the stopping strategies. To the best of our knowledge, HYGRO is the first data-driven method to tackle the cuts generation stopping problem. By integrating our approach with modern solvers, experiments demonstrate that HYGRO significantly improves the efficiency of solving MILPs compared to competitive baselines, achieving up to 31% improvement.

Efficient analysis of point clouds holds paramount significance in real-world 3D applications. Currently, prevailing point-based models adhere to the PointNet++ methodology, which involves embedding and abstracting point features within a sequence of spatially overlapping local point sets, resulting in noticeable computational redundancy. Drawing inspiration from the streamlined paradigm of pixel embedding followed by regional pooling in Convolutional Neural Networks (CNNs), we introduce a novel, uncomplicated yet potent architecture known as PointGL, crafted to facilitate efficient point cloud analysis. PointGL employs a hierarchical process of feature acquisition through two recursive steps. First, the Global Point Embedding leverages straightforward residual Multilayer Perceptrons (MLPs) to effectuate feature embedding for each individual point. Second, the novel Local Graph Pooling technique characterizes point-to-point relationships and abstracts regional representations through succinct local graphs. The harmonious fusion of one-time point embedding and parameter-free graph pooling contributes to PointGL's defining attributes of minimized model complexity and heightened efficiency. Our PointGL attains state-of-the-art accuracy on the ScanObjectNN dataset while exhibiting a runtime that is more than 5 times faster and utilizing only approximately 4% of the FLOPs and 30% of the parameters compared to the recent PointMLP model. The code for PointGL is available at https://github.com/Roywangj/PointGL.

Learning neural operators for solving partial differential equations (PDEs) has attracted great attention due to its high inference efficiency. However, training such operators requires generating a substantial amount of labeled data, i.e., PDE problems together with their solutions. The data generation process is exceptionally time-consuming, as it involves solving numerous systems of linear equations to obtain numerical solutions to the PDEs. Many existing methods solve these systems independently without considering their inherent similarities, resulting in extremely redundant computations. To tackle this problem, we propose a novel method, namely Sorting Krylov Recycling (SKR), to boost the efficiency of solving these systems, thus significantly accelerating data generation for neural operators training. To the best of our knowledge, SKR is the first attempt to address the time-consuming nature of data generation for learning neural operators. The working horse of SKR is Krylov subspace recycling, a powerful technique for solving a series of interrelated systems by leveraging their inherent similarities. Specifically, SKR employs a sorting algorithm to arrange these systems in a sequence, where adjacent systems exhibit high similarities. Then it equips a solver with Krylov subspace recycling to solve the systems sequentially instead of independently, thus effectively enhancing the solving efficiency. Both theoretical analysis and extensive experiments demonstrate that SKR can significantly accelerate neural operator data generation, achieving a remarkable speedup of up to 13.9 times.

In an era of digital ubiquity, efficient resource management and decision-making are paramount across numerous industries. To this end, we present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI Solver, which aims to mitigate the scarcity of real-world mathematical programming instances, and to surpass the capabilities of traditional optimization techniques. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. Furthermore, we introduce a training framework leveraging augmentation policies to maintain solvers' utility in dynamic environments. Besides the data generation and augmentation, our proposed approaches also include novel ML-driven policies for personalized solver strategies, with an emphasis on applications like graph convolutional networks for initial basis selection and reinforcement learning for advanced presolving and cut selection. Additionally, we detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance. Compared with traditional solvers such as Cplex and SCIP, our ML-augmented OptVerse AI Solver demonstrates superior speed and precision across both established benchmarks and real-world scenarios, reinforcing the practical imperative and effectiveness of machine learning techniques in mathematical programming solvers.

GitHub issue resolving recently has attracted significant attention from academia and industry. SWE-bench is proposed to measure the performance in resolving issues. In this paper, we propose CodeR, which adopts a multi-agent framework and pre-defined task graphs to Repair & Resolve reported bugs and add new features within code Repository. On SWE-bench lite, CodeR is able to solve 28.00% of issues, in the case of submitting only once for each issue. We examine the performance impact of each design of CodeR and offer insights to advance this research direction.

Fine urban change segmentation using multi-temporal remote sensing images is essential for understanding human-environment interactions in urban areas. Despite advances in remote sensing data for urban monitoring, coarse-grained classification systems and the lack of continuous temporal observations hinder the application of deep learning to urban change analysis. To address this, we introduce FUSU, a multi-source, multi-temporal change segmentation dataset for Fine-grained Urban Semantic Understanding. FUSU features the most detailed land use classification system to date, with 17 classes and 30 billion pixels of annotations. It includes bi-temporal high-resolution satellite images with 20-50 cm ground sample distance and monthly optical and radar satellite time series, covering 847 km2 across five urban areas in China. The fine-grained pixel-wise annotations and high spatial-temporal resolution data provide a robust foundation for deep learning models to understand urbanization and land use changes. To fully leverage FUSU, we propose a unified time-series architecture for both change detection and segmentation and then benchmark FUSU on various methods for several tasks. Dataset and code will be available at: https://github.com/yuanshuai0914/FUSU.

Optimal transport has been very successful for various machine learning tasks; however, it is known to suffer from the curse of dimensionality. Hence, dimensionality reduction is desirable when applied to high-dimensional data with low-dimensional structures. The kernel max-sliced (KMS) Wasserstein distance is developed for this purpose by finding an optimal nonlinear mapping that reduces data into $1$ dimensions before computing the Wasserstein distance. However, its theoretical properties have not yet been fully developed. In this paper, we provide sharp finite-sample guarantees under milder technical assumptions compared with state-of-the-art for the KMS $p$-Wasserstein distance between two empirical distributions with $n$ samples for general $p\in[1,\infty)$. Algorithm-wise, we show that computing the KMS $2$-Wasserstein distance is NP-hard, and then we further propose a semidefinite relaxation (SDR) formulation (which can be solved efficiently in polynomial time) and provide a relaxation gap for the SDP solution. We provide numerical examples to demonstrate the good performance of our scheme for high-dimensional two-sample testing., Comment: 34 pages, 7 figures, 4 tables

Deep reinforcement learning (DRL) algorithms can suffer from modeling errors between the simulation and the real world. Many studies use adversarial learning to generate perturbation during training process to model the discrepancy and improve the robustness of DRL. However, most of these approaches use a fixed parameter to control the intensity of the adversarial perturbation, which can lead to a trade-off between average performance and robustness. In fact, finding the optimal parameter of the perturbation is challenging, as excessive perturbations may destabilize training and compromise agent performance, while insufficient perturbations may not impart enough information to enhance robustness. To keep the training stable while improving robustness, we propose a simple but effective method, namely, Adaptive Adversarial Perturbation (A2P), which can dynamically select appropriate adversarial perturbations for each sample. Specifically, we propose an adaptive adversarial coefficient framework to adjust the effect of the adversarial perturbation during training. By designing a metric for the current intensity of the perturbation, our method can calculate the suitable perturbation levels based on the current relative performance. The appealing feature of our method is that it is simple to deploy in real-world applications and does not require accessing the simulator in advance. The experiments in MuJoCo show that our method can improve the training stability and learn a robust policy when migrated to different test environments. The code is available at https://github.com/Lqm00/A2P-SAC.

Recent advancements in Surgical Visual Question Answering (Surgical-VQA) and related region grounding have shown great promise for robotic and medical applications, addressing the critical need for automated methods in personalized surgical mentorship. However, existing models primarily provide simple structured answers and struggle with complex scenarios due to their limited capability in recognizing long-range dependencies and aligning multimodal information. In this paper, we introduce Surgical-LVLM, a novel personalized large vision-language model tailored for complex surgical scenarios. Leveraging the pre-trained large vision-language model and specialized Visual Perception LoRA (VP-LoRA) blocks, our model excels in understanding complex visual-language tasks within surgical contexts. In addressing the visual grounding task, we propose the Token-Interaction (TIT) module, which strengthens the interaction between the grounding module and the language responses of the Large Visual Language Model (LVLM) after projecting them into the latent space. We demonstrate the effectiveness of Surgical-LVLM on several benchmarks, including EndoVis-17-VQLA, EndoVis-18-VQLA, and a newly introduced EndoVis Conversations dataset, which sets new performance standards. Our work contributes to advancing the field of automated surgical mentorship by providing a context-aware solution.

Batched sparse (BATS) code is a network coding solution for multi-hop wireless networks with packet loss. Achieving a close-to-optimal rate relies on an optimal degree distribution. Technical challenges arise from the sensitivity of this distribution to the often empirically obtained rank distribution at the destination node. Specifically, if the empirical distribution overestimates the channel, BATS codes experience a significant rate degradation, leading to unstable rates across different runs and hence unpredictable transmission costs. Confronting this unresolved obstacle, we introduce a formulation for distributionally robust optimization in degree optimization. Deploying the resulting degree distribution resolves the instability of empirical rank distributions, ensuring a close-to-optimal rate, and unleashing the potential of applying BATS codes in real-world scenarios., Comment: 8 pages, accepted by 2024 IEEE International Symposium on Information Theory

Generalizing to longer sentences is important for recent Transformer-based language models. Besides algorithms manipulating explicit position features, the success of Transformers without position encodings (NoPE) provides a new way to overcome the challenge. In this paper, we study the length generalization property of NoPE. We find that although NoPE can extend to longer sequences than the commonly used explicit position encodings, it still has a limited context length. We identify a connection between the failure of NoPE's generalization and the distraction of attention distributions. We propose a parameter-efficient tuning for searching attention heads' best temperature hyper-parameters, which substantially expands NoPE's context size. Experiments on long sequence language modeling, the synthetic passkey retrieval task and real-world long context tasks show that NoPE can achieve competitive performances with state-of-the-art length generalization algorithms. The source code is publicly accessible

Multimodal video sentiment analysis aims to integrate multiple modal information to analyze the opinions and attitudes of speakers. Most previous work focuses on exploring the semantic interactions of intra- and inter-modality. However, these works ignore the reliability of multimodality, i.e., modalities tend to contain noise, semantic ambiguity, missing modalities, etc. In addition, previous multimodal approaches treat different modalities equally, largely ignoring their different contributions. Furthermore, existing multimodal sentiment analysis methods directly regress sentiment scores without considering ordinal relationships within sentiment categories, with limited performance. To address the aforementioned problems, we propose a trustworthy multimodal sentiment ordinal network (TMSON) to improve performance in sentiment analysis. Specifically, we first devise a unimodal feature extractor for each modality to obtain modality-specific features. Then, an uncertainty distribution estimation network is customized, which estimates the unimodal uncertainty distributions. Next, Bayesian fusion is performed on the learned unimodal distributions to obtain multimodal distributions for sentiment prediction. Finally, an ordinal-aware sentiment space is constructed, where ordinal regression is used to constrain the multimodal distributions. Our proposed TMSON outperforms baselines on multimodal sentiment analysis tasks, and empirical results demonstrate that TMSON is capable of reducing uncertainty to obtain more robust predictions., Comment: 14 pages, 9 figures, Accepted by IEEE Transactions on Circuits and Systems for Video Technology

Deep learning models have become a powerful tool in knee angle estimation for lower limb prostheses, owing to their adaptability across various gait phases and locomotion modes. Current methods utilize Multi-Layer Perceptrons (MLP), Long-Short Term Memory Networks (LSTM), and Convolutional Neural Networks (CNN), predominantly analyzing motion information from the thigh. Contrary to these approaches, our study introduces a holistic perspective by integrating whole-body movements as inputs. We propose a transformer-based probabilistic framework, termed the Angle Estimation Probabilistic Model (AEPM), that offers precise angle estimations across extensive scenarios beyond walking. AEPM achieves an overall RMSE of 6.70 degrees, with an RMSE of 3.45 degrees in walking scenarios. Compared to the state of the art, AEPM has improved the prediction accuracy for walking by 11.31%. Our method can achieve seamless adaptation between different locomotion modes. Also, this model can be utilized to analyze the synergy between the knee and other joints. We reveal that the whole body movement has valuable information for knee movement, which can provide insights into designing sensors for prostheses. The code is available at https://github.com/penway/Beyond-Gait-AEPM., Comment: 8 pages, 6 figures, This work has been submitted to the IEEE-RAL for possible publication

In recent years, there has been significant attention given to the robustness assessment of neural networks. Robustness plays a critical role in ensuring reliable operation of artificial intelligence (AI) systems in complex and uncertain environments. Deep learning's robustness problem is particularly significant, highlighted by the discovery of adversarial attacks on image classification models. Researchers have dedicated efforts to evaluate robustness in diverse perturbation conditions for image recognition tasks. Robustness assessment encompasses two main techniques: robustness verification/ certification for deliberate adversarial attacks and robustness testing for random data corruptions. In this survey, we present a detailed examination of both adversarial robustness (AR) and corruption robustness (CR) in neural network assessment. Analyzing current research papers and standards, we provide an extensive overview of robustness assessment in image recognition. Three essential aspects are analyzed: concepts, metrics, and assessment methods. We investigate the perturbation metrics and range representations used to measure the degree of perturbations on images, as well as the robustness metrics specifically for the robustness conditions of classification models. The strengths and limitations of the existing methods are also discussed, and some potential directions for future research are provided., Comment: Corrected typos and grammatical errors in Section 5