Miracle-dz/UniFAR-FTUs huggingface dataset:
Decentralized Federated Averaging

Federated averaging (FedAvg) is a communication-efficient algorithm for distributed training with an enormous number of clients. In FedAvg, clients keep their data locally for privacy protection; a central parameter server is used to communicate between clients. This central server distributes the parameters to each client and collects the updated parameters from clients. FedAvg is mostly studied in centralized fashions, requiring massive communications between the central server and clients, which leads to possible channel blocking. Moreover, attacking the central server can break the whole system's privacy. Indeed, decentralization can significantly reduce the communication of the busiest node (the central one) because all nodes only communicate with their neighbors. To this end, in this paper, we study the decentralized FedAvg with momentum (DFedAvgM), implemented on clients that are connected by an undirected graph. In DFedAvgM, all clients perform stochastic gradient descent with momentum and communicate with their neighbors only. To further reduce the communication cost, we also consider the quantized DFedAvgM. The proposed algorithm involves the mixing matrix, momentum, client training with multiple local iterations, and quantization, introducing extra items in the Lyapunov analysis. Thus, the analysis of this paper is much more challenging than previous decentralized (momentum) SGD or FedAvg. We prove convergence of the (quantized) DFedAvgM under trivial assumptions; the convergence rate can be improved to sublinear when the loss function satisfies the PŁ property. Numerically, we find that the proposed algorithm outperforms FedAvg in both convergence speed and communication cost.
ESResNe(X)t-fbsp: Learning Robust Time-Frequency Transformation of Audio

Environmental Sound Classification (ESC) is a rapidly evolving field that recently demonstrated the advantages of application of visual domain techniques to the audio-related tasks. Previous studies indicate that the domain-specific modification of cross-domain approaches show a promise in pushing the whole area of ESC forward. In this paper, we present a new time-frequency transformation layer that is based on complex frequency B-spline (fbsp) wavelets. Being used with a high-performance audio classification model, the proposed fbsp-layer provides an accuracy improvement over the previously used Short-Time Fourier Transform (STFT) on standard datasets. We also investigate the influence of different pre-training strategies, including the joint use of two large-scale datasets for weight initialization: ImageNet and AudioSet. Our proposed model out-performs other approaches by achieving accuracies of 95.20 % on the ESC-50 and 89.14 % on the UrbanSound8K datasets. Additionally, we assess the increase of model robustness against additive white Gaussian noise and reduction of an effective sample rate introduced by the proposed layer and demonstrate that the fbsp-layer improves the model's ability to withstand signal perturbations, in comparison to STFT-based training. For the sake of reproducibility, our code is made available.
Motion Representations for Articulated Animation

We propose novel motion representations for animating articulated objects consisting of distinct parts. In a completely unsupervised manner, our method identifies object parts, tracks them in a driving video, and infers their motions by considering their principal axes. In contrast to the previous keypoint-based works, our method extracts meaningful and consistent regions, describing locations, shape, and pose. The regions correspond to semantically relevant and distinct object parts, that are more easily detected in frames of the driving video. To force decoupling of foreground from background, we model non-object related global motion with an additional affine transformation. To facilitate animation and prevent the leakage of the shape of the driving object, we disentangle shape and pose of objects in the region space. Our model1 can animate a variety of objects, surpassing previous methods by a large margin on existing benchmarks. We present a challenging new benchmark with high-resolution videos and show that the improvement is particularly pronounced when articulated objects are considered, reaching 96.6% user preference vs. the state of the art.
Grasp Synthesis for Novel Objects Using Heuristic-based and Data-driven Active Vision Methods

In this work, we present several heuristic-based and data-driven active vision strategies for viewpoint optimization of an arm-mounted depth camera for the purpose of aiding robotic grasping. These strategies aim to efficiently collect data to boost the performance of an underlying grasp synthesis algorithm. We created an open-source benchmarking platform in simulation (https://github.com/galenbr/2021ActiveVision), and provide an extensive study for assessing the performance of the proposed methods as well as comparing them against various baseline strategies. We also provide an experimental study with a real-world setup by utilizing an existing grasping planning benchmark in the literature. With these analyses, we were able to quantitatively demonstrate the versatility of heuristic methods that prioritize certain types of exploration, and qualitatively show their robustness to both novel objects and the transition from simulation to the real world. We identified scenarios in which our methods did not perform well and scenarios that are objectively difficult, and present a discussion on which avenues for future research show promise.
Logarithmic intersections of double ramification cycles

We describe a theory of logarithmic Chow rings and tautological subrings for logarithmically smooth algebraic stacks, via a generalisation of the notion of piecewise-polynomial functions. Using this machinery we prove that the double-double ramification cycle lies in the tautological subring of the (classical) Chow ring of the moduli space of curves, and that the logarithmic double ramification cycle is divisorial (as conjectured by Molcho, Pandharipande, and Schmitt).
Lightweight Detection of Out-of-Distribution and Adversarial Samples via Channel Mean Discrepancy

Detecting out-of-distribution (OOD) and adversarial samples is essential when deploying classiﬁcation models in real-world applications. We introduce Channel Mean Discrepancy (CMD), a model-agnostic distance metric for evaluating the statistics of features extracted by classiﬁcation models, inspired by integral probability metrics. CMD compares the feature statistics of incoming samples against feature statistics estimated from previously seen training samples with minimal overhead. We experimentally demonstrate that CMD magnitude is signiﬁcantly smaller for legitimate samples than for OOD and adversarial samples. We propose a simple method to reliably differentiate be-tween legitimate samples from OOD and adversarial samples using CMD, requiring only a single forward pass on a pre-trained classiﬁcation model per sample. We further demonstrate how to achieve single image detection by us-ing a lightweight model for channel sensitivity tuning, an improvement on other statistical detection methods. Preliminary results show that our simple yet effective method outperforms several state-of-the-art approaches to detecting OOD and adversarial samples across various datasets and attack methods with high efﬁciency and generalizability.
Uncovering Phase Change Memory Energy Limits by Sub‐Nanosecond Probing of Power Dissipation Dynamics

Phase change memory (PCM) is one of the leading candidates for neuromorphic hardware and has recently matured as a storage class memory. Yet, energy and power consumption remain key challenges for this technology because part of the PCM device must be self‐heated to its melting temperature during reset. Here, it is shown that this reset energy can be reduced by nearly two orders of magnitude by minimizing the pulse width. A high‐speed measurement setup is utilized to probe the energy consumption in PCM cells with varying pulse width (0.3–40 nanoseconds) and uncover the power dissipation dynamics. A key finding is that the switching power (P) remains unchanged for pulses wider than a short thermal time constant of the PCM (τth < 1 ns in 50 nm diameter device), resulting in a decrease of energy (E = P × τ) as the pulse width τ is reduced in that range. Thermal confinement during short pulses is achieved by limiting the heat diffusion time. The improved programming scheme reduces reset energy density below 0.1 nJ µm−2, over an order of magnitude lower than state‐of‐the‐art PCM, potentially changing the roadmap of future data storage technology and paving the way toward energy‐efficient neuromorphic hardware.
A Systematic Survey on Android API Usage for Data-driven Analytics with Smartphones

Recent industrial and academic research has focused on data-driven analytics with smartphones by collecting user interaction, context, and device systems data through Application Programming Interfaces (APIs) and sensors. The Android operating system provides various APIs to collect such mobile usage and sensor data for third-party developers. Usage Statistics API (US API) and Accessibility Service API (AS API) are representative Android APIs for collecting app usage data and are used for various research purposes, as they can collect fine-grained interaction data (e.g., app usage history and user interaction type). Furthermore, other sensor APIs help to collect a user’s context and device state data, along with AS/US APIs. This review investigates mobile usage and sensor data-driven research using AS/US APIs by categorizing the research purposes and the data types. In this article, the surveyed studies are classified as follows: five themes and 21 subthemes and a four-layer hierarchical data classification structure. This allows us to identify a data usage trend and derive insight into data collection according to research purposes. Several limitations and future research directions of mobile usage and sensor data-driven analytics research are discussed, including the impact of changes in the Android API versions on research, the privacy and data quality issues, and the mitigation of reproducibility risks with standardized data typology.
Improving Emotion Perception in Children with Autism Spectrum Disorder with Computer-Based Training and Hearing Amplification

Individuals with Autism Spectrum Disorder (ASD) experience challenges with social communication, often involving emotional elements of language. This may stem from underlying auditory processing difficulties, especially when incoming speech is nuanced or complex. This study explored the effects of auditory training on social perception abilities of children with ASD. The training combined use of a remote-microphone hearing system and computerized emotion perception training. At baseline, children with ASD had poorer social communication scores and delayed mismatch negativity (MMN) compared to typically developing children. Behavioral results, measured pre- and post-intervention, revealed increased social perception scores in children with ASD to the extent that they outperformed their typically developing peers post-intervention. Electrophysiology results revealed changes in neural responses to emotional speech stimuli. Post-intervention, mismatch responses of children with ASD more closely resembled their neurotypical peers, with shorter MMN latencies, a significantly heightened P2 wave, and greater differentiation of emotional stimuli, consistent with their improved behavioral results. This study sets the foundation for further investigation into connections between auditory processing difficulties and social perception and communication for individuals with ASD, and provides a promising indication that combining amplified hearing and computer-based targeted social perception training using emotional speech stimuli may have neuro-rehabilitative benefits.
Occupational Radiation Dose to Eye Lenses in CT-Guided Interventions Using MDCT-Fluoroscopy

In computed tomography (CT)-guided interventions (CTIs), physicians are close to a source of scattered radiation. The physician and staff are at high risk of radiation-induced injury (cataracts). Thus, dose-reducing measures for physicians are important. However, few previous reports have examined radiation doses to physicians in CTIs. This study evaluated the radiation dose to the physician and medical staff using multi detector (MD)CT-fluoroscopy, and attempted to understand radiation-protection and -reduction methods. The procedures were performed using an interventional radiology (IVR)-CT system. We measured the occupational radiation dose (physician and nurse) using a personal dosimeter in real-time, gathered CT-related parameters (fluoroscopy time, mAs, CT dose index (CTDI), and dose length product (DLP)), and performed consecutive 232 procedures in CT-guided biopsy. Physician doses (eye lens, neck, and hand; μSv, average ± SD) in our CTIs were 39.1 ± 36.3, 23.1 ± 23.7, and 28.6 ± 31.0, respectively. Nurse doses (neck and chest) were lower (2.3 ± 5.0 and 2.4 ± 4.4, respectively) than the physician doses. There were significant correlations between the physician doses (eye and neck) and related factors, such as CT-fluoroscopy mAs (eye dose: r = 0.90 and neck dose: r = 0.83). We need to understand the importance of reducing/optimizing the dose to the physician and medical staff in CTIs. Our study suggests that physician and staff doses were not significant when the procedures were performed with the appropriate radiation protection and low-dose techniques.
Monitoring Accumulated Training and Match Load in Football: A Systematic Review

(1) Background: Training load monitoring has become a relevant research-practice gap to control training and match demands in team sports. However, there are no systematic reviews about accumulated training and match load in football. (2) Methods: Following the preferred reporting item for systematic reviews and meta-analyses (PRISMA), a systematic search of relevant English-language articles was performed from earliest record to March 2020. The search included descriptors relevant to football, training load, and periodization. (3) Results: The literature search returned 7972 articles (WoS = 1204; Pub-Med = 869, SCOPUS = 5083, and SportDiscus = 816). After screening, 36 full-text articles met the inclusion criteria and were reviewed. Eleven of the included articles analyzed weekly training load distribution; fourteen, the weekly training load and match load distribution; and eleven were about internal and external load relationships during training. The reviewed articles were based on short-telemetry systems (n = 12), global positioning tracking systems (n = 25), local position measurement systems (n = 3), and multiple-camera systems (n = 3). External load measures were quantified with distance and covered distance in different speed zones (n = 27), acceleration and deceleration (n = 13) thresholds, accelerometer metrics (n = 11), metabolic power output (n = 4), and ratios/scores (n = 6). Additionally, the internal load measures were reported with perceived exertion (n = 16); heart-rate-based measures were reported in twelve studies (n = 12). (4) Conclusions: The weekly microcycle presented a high loading variation and a limited variation across a competitive season. The magnitude of loading variation seems to be influenced by the type of week, player’s starting status, playing positions, age group, training mode and contextual variables. The literature has focused mainly on professional men; future research should be on the youth and female accumulated training/match load monitoring.
A Numerical Study on the Effect of Variable Wear Coefficient on Fretting Wear Characteristics

Fretting wear is a common phenomenon that happens between contact parts when there is an oscillatory relative movement. To investigate wear characteristics history in the fretting process, the finite element method (FEM) is commonly applied to simulate the fretting by considering the wear in the model. In most literature publications, the wear coefficient is considered as a constant, which is not a real case based on the experimental results. To consider the variation of wear coefficient, a double-linear model is applied in this paper, and the tribologically transformed structure (TTS) phase is considered in the study of the wear coefficient variation model. By using these models for variable wear coefficient for both flat and cylinder, the difference of wear characteristics, plastic strain, and stress between variable wear coefficient model (VWCM) and constant wear coefficient model (CWCM) are analyzed. The results show that the variable wear coefficient has no significant effect on the wear characteristic at the end of the process in the gross sliding regime. However, in the partial slip regime, the effect of variable wear coefficient on wear characteristics is significant. Due to the difference in contact geometry in the fretting process between VWCM and CWCM, the tangential and shear stress and equivalent plastic strain also show differences during the fretting process.
Ginsenoside Absorption Rate and Extent Enhancement of Black Ginseng (CJ EnerG) over Red Ginseng in Healthy Adults

Red ginseng (RG) and black ginseng (BG, CJ EnerG) were prepared from fresh ginseng using one and nine cycles of steaming and drying, respectively. This process reduces the molecular weight (MW) of ginsenoside-active compounds in ginseng by removing sugar moieties from their dammaranes. We compared the pharmacokinetic characteristics of ginsenosides between BG comprising mainly low-MW ginsenosides (Rg3, Rg5, Rk1, and Rh1) and RG that predominantly contains high-MW ginsenosides (Rb1, Rb2, Rc, Rd, Re, and Rg1). The safety profiles and tolerability were also studied using a randomized, double-blind, single-dose, crossover clinical trial. A combination of Rb1, Rg1, and Rg3, well-known representative and functional RG components, exhibited a 1 h faster absorption rate (Tmax) and 58% higher exposure (24 h area under the concentration–time curve, AUC24) in BG than in RG. Furthermore, the combination of Rg3, Rg5, and Rk1, the major and most efficient components in BG, displayed 824% higher absorption (AUC24) in BG than in RG. The total ginsenoside showed a 5 h rapid intestinal absorption (Tmax) and 79% greater systemic exposure (AUC24) in BG than in RG. No clinically significant findings were observed in terms of safety or tolerability. Thus, BG extract was more effective than RG extract.
The Dynamic Responses of Oil Palm Leaf and Root Metabolome to Phosphorus Deficiency

Inorganic phosphate (Pi) starvation is an important abiotic constraint that affects plant cellular homeostasis, especially in tropical regions with high acidic soil and less solubilizable Pi. In the current work, oil palm seedlings were hydroponically maintained under optimal Pi-supply and no Pi-supply conditions for 14 days, and metabolites were measured by gas chromatography–mass spectrometry (GC–MS), from leaves and roots, after seven and 14 days of treatment, to investigate biochemical pathways in relation to P-utilizing strategy. After seven days of limited Pi, plant leaves showed increased levels of most soluble sugars, and after 14 days, the sugars’ level decrease, except for erythritol, mannose, fructose, and glucose, which showed the highest levels. Rather in root samples, there were different but overlapping alterations, mainly on sugars, amino acids, and organic acids. The leaf sample was shown to have the highest response of sugars with myo-inositol playing a vital role in the redistribution of sugars, while maltose levels increased, indicating active degradation of starch in the root. High levels of glycerol and stearate in both roots and leaves suggest the metabolism of storage lipids for cellular energy during Pi-deficient conditions.
Role of Probiotics in Modulating Human Gut Microbiota Populations and Activities in Patients with Colorectal Cancer—A Systematic Review of Clinical Trials

Background: Growing attention has been given to the role of nutrition and alterations of microbial diversity of the gut microbiota in colorectal cancer (CRC) pathogenesis. It has been suggested that probiotics and synbiotics modulate enteric microbiota and therefore may be used as an intervention to reduce the risk of CRC. The aim of this study was to evaluate the influence of probiotics/synbiotics administration on gut microbiota in patients with CRC. Methods: PubMed, Scopus, and Web of Science were searched between December 2020 and January 2021. Randomized controlled trials (RCTs) recruiting adults with CRC, who have taken probiotics/synbiotics for at least 6 days were included. Changes in gut microbiota and selected biochemical and inflammatory parameters (i.e., hsCRP, IL-2, hemoglobin) were retrieved. Results: The search resulted in 198 original research articles and a final 6 were selected as being eligible, including 457 subjects. The median age of patients was 65.4 years old and they were characterized by the median BMI value: 23.8 kg/m2. The literature search revealed that probiotic/synbiotic administration improved enteric microbiota by increasing the abundance of beneficial bacteria such as Lactobacillus, Eubacterium, Peptostreptococcus, Bacillus and Bifidobacterium, and decreased the abundance of potentially harmful bacteria such as Fusobacterium, Porhyromonas, Pseudomonas and Enterococcus. Additionally, probiotic/synbiotic intervention improved release of antimicrobials, intestinal permeability, tight junction function in CRC patients. Conclusions: The use of probiotics/synbiotics positively modulates enteric microbiota, improves postoperative outcomes, gut barrier function and reduces inflammatory parameters in patients suffering from CRC.
Potential of Probiotic Frozen Blackcurrant Products: Consumer Preference, Physicochemical Characterization, and Cell Viability

Blackcurrant is a healthy, affordable, and traditionally gardened berry that, thus far, has been underused in food applications. From the consumers’ point of view, the acidic taste of blackcurrants is a challenge; therefore, these berries have mainly been utilized for sugary juice production. This research study aimed to develop a frozen vegan blackcurrant product with pleasant sensory properties and potential probiotic function. A candidate probiotic, Lactoplantibacillus plantarum Q823, was used in the manufacturing process. The physicochemical properties, nutritional composition, and consumer preference for the developed product were assessed, as was the viability of L. plantarum Q823 during storage time and in an in vitro gastrointestinal model. Consumers (n = 71) perceived the developed product to be pleasant. L. plantarum Q823 had high viability counts (log colony forming units (cfu) g−1 7.0 ± 0.38) in the final product, although the viability of L. plantarum Q823 during storage time needs to be enhanced to obtain a probiotic product. Thus, within an optimized formulation, blackcurrant berries represent a potential raw material for functional frozen food products.