Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance of Meshed Tree Protocol in Data Center Networks

Data Center Networks (DCNs) are the communications backbone of data centers. Data centers support thousands of servers, which pose serious DCN architectural and design challenges. Operational demands on DCN protocols to adapt to the new architectures and topologies are met by using multiple off-the-shelf protocols and retrofitting them to the new needs. While major research efforts focus on DCN architectures and topologies, few efforts are directed to address the growing DCN operational and configuration complexity and troubleshooting and management of DCNs. In this work we demonstrate that given the highly structured and symmetrical DCN topologies an extremely simple protocol can replace the multiple protocols that operationally overload DCN routers. We demonstrate our claims by comparing the performance of our proposed Multi Root Meshed Tree Protocol (MR-MTP) to the popular protocol set comprising of Border Gateway Protocol (BGP) for routing, Equal Cost Multipath Protocol (ECMP) for load balancing and Bidirectional Forwarding Detection (BFD) to speed up convergence in a folded-Clos topology. The performance tests were conducted on the Global Environment for Network Innovation (GENI) testbed. Operational MR-MTP C code is available in Github along with all the test scripts. MR-MTP is backward compatible to the Internet Protocol (IP) and Ethernet. From our results it will be clear that we can achieve highly efficient DCN operations and significantly improve performance by rethinking our fundamental approach to routing and IP packet forwarding.
The mRNA decapping machinery targets LBD3/ASL9 to mediate apical hook and lateral root development in Arabidopsis

Multicellular organisms perceive and transduce multiple cues to optimize development. Key transcription factors drive developmental changes, but RNA processing also contributes to tissue development. Here, we report that multiple decapping deficient mutants share developmental defects in apical hook, primary and lateral root growth. More specifically, LATERAL ORGAN BOUNDARIES DOMAIN 3 (LBD3)/ASYMMETRIC LEAVES 2-LIKE 9 (ASL9) transcripts accumulate in decapping deficient plants and can be found in complexes with decapping components. Accumulation of ASL9 inhibits apical hook, primary root growth and lateral root formation. Interestingly, exogenous auxin application restores lateral roots formation in both ASL9 over-expressors and mRNA decay-deficient mutants. Likewise, mutations in the cytokinin transcription factors type-B ARABIDOPSIS RESPONSE REGULATORS (B-ARRs) ARR10 and ARR12 restore the developmental defects caused by over-accumulation of capped ASL9 transcript upon ASL9 overexpression. Most importantly, loss-of-function of asl9 partially restores apical hook and lateral root formation in decapping deficient mutants. Thus, the mRNA decay machinery directly targets ASL9 transcripts for decay, possibly to interfere with cytokinin/auxin responses, during development.
Patient Acceptance of Self-Monitoring on a Smartwatch in a Routine Digital Therapy: A Mixed-Methods Study

Self-monitoring of mood and lifestyle habits is the cornerstone of many therapies, but it is still hindered by persistent issues including inaccurate records, gaps in the monitoring, patient burden, and perceived stigma. Smartwatches have the potential to deliver enhanced self-reports, but their acceptance in clinical mental health settings is unexplored and rendered difficult by a complex theoretical landscape and need for a longitudinal perspective. We present the Mood Monitor smartwatch application for mood and lifestyle habits self-monitoring. We investigated patient acceptance of the app within a routine 8-week digital therapy. We recruited 35 patients of the UK’s National Health Service and evaluated their acceptance through three online questionnaires and a post-study interview. We assessed the clinical feasibility of the Mood Monitor by comparing clinical, usage, and acceptance metrics obtained from the 35 patients with a smartwatch with those from an additional 34 patients without a smartwatch (digital treatment as usual). Findings showed that the smartwatch app was highly accepted by patients, revealed which factors facilitated and impeded this acceptance, and supported clinical feasibility. We provide guidelines for the design of self-monitoring on a smartwatch and reflect on the conduct of human-computer interaction research evaluating user acceptance of mental health technologies.
DSDGen: Extracting Documentation to Comprehend Fork Merges

Developers use the forking mechanisms of modern social-coding platforms to evolve and maintain their systems. Using such mechanisms often leads to a larger number of independent variants with individual features or bug fixes that the developers may want to merge after a longer period of co-evolution. At this point, they may have forgotten (or never had) knowledge about differences between the variants. Tackling this problem, we built on the idea of on-demand documentation to develop a technique that automatically extracts and presents information for merging a class from two forks. We implemented our technique as a prototype called DSD-Gen and evaluated it through an experimental simulation with 10 students who should comprehend two real-world merge requests. Using DSDGen instead of code diffs only, more of the students could correctly comprehend the merges (6 / 10 versus 2 / 10) within a similar time. The students actively inspected the additional information provided by DSDGen and used it to comprehend the differences between the forked classes. So, DSDGen can help developers recover information for comprehending the differences caused by fork co-evolution during merges, with our results indicating opportunities for future research and improvements.
Explainable Reinforcement Learning: A Survey and Comparative Review

Explainable reinforcement learning (XRL) is an emerging subfield of explainable machine learning that has attracted considerable attention in recent years. The goal of XRL is to elucidate the decision-making process of reinforcement learning (RL) agents in sequential decision-making settings. Equipped with this information, practitioners can better understand important questions about RL agents (especially those deployed in the real world), such as what the agents will do and why. Despite increased interest, there exists a gap in the literature for organizing the plethora of papers — especially in a way that centers the sequential decision-making nature of the problem. In this survey, we propose a novel taxonomy for organizing the XRL literature that prioritizes the RL setting. We propose three high-level categories: feature importance, learning process and Markov decision process, and policy-level. We overview techniques according to this taxonomy, highlighting challenges and opportunities for future work. We conclude by using these gaps to motivate and outline a roadmap for future work.
Biais de genre dans un système de traduction automatique neuronale : une étude des mécanismes de transfert cross-langue [Gender bias in a neural machine translation system: a study of crosslingual transfer mechanisms]

RÉSUMÉ. Cet article a pour objectif de mettre en évidence les biais de genre dans les systèmes de traduction automatique et de rechercher leurs causes en étudiant les différentes manières dont l’information de genre peut circuler entre le décodeur et l’encodeur. Pour cela, nous décrivons un corpus minimal et contrôlé pour mesurer l’intensité de ces biais dans les traductions de l’anglais vers le français et du français vers l’anglais. Grâce à des méthodes de sondage et des interventions sur les représentations internes de l’encodeur, nos expériences montrent que l’information de genre est distribuée sur l’ensemble des représentations des tokens sources et cibles et que la sélection du genre en langue cible résulte d’une multiplicité d’interactions entre les diverses unités impliquées dans la traduction. ABSTRACT. This paper describes a study on gender bias in French/English neural machine translation (MT) systems. We introduce a controlled corpus to measure the intensity of such biases in the two translation directions (from and into English). This corpus also allows us to investigate the information flow in a encoder-decoder architecture and to identify how gender information can be transfered between languages. Considering both probing as well as interventions on the internal representations of the MT system, we show that gender information is encoded in all token representations built by the encoder and the decoder and that there are multiple paths to transfer gender.
Dielectric behavior of functional paint/PMN-PT nanocomposite films

Abstract The detailed functionality of 0-3 paint/lead magnesium niobate-lead titanate (PMN-PT) composite films is investigated in terms of their dielectric and electric transport characteristics. The Paint/PMN-PT composite films have been fabricated by a conventional cost effective paint brushing technique. The properties investigated include, dielectric constants, ε' and ε'' as a function of temperature, frequency and composition. From the foregoing parameters, it is indicated that the dielectric constants increase with an increase of filler content, and temperature, providing information on the functionality of the films. The results reveal that the AC conducitivity, σAC, obeys the relation σAC = Aωs, and exponent s, was found to decrease by increasing the temperature.
Analysis of Performance Improvements and Bias Associated with the Use of Human Mobility Data in COVID-19 Case Prediction Models

The COVID-19 pandemic has mainstreamed human mobility data into the public domain, with research focused on understanding the impact of mobility reduction policies as well as on regional COVID-19 case prediction models. Nevertheless, current research on COVID-19 case prediction tends to focus on performance improvements, masking relevant insights about when mobility data does not help, and more importantly, why, so that it can adequately inform local decision making. In this article, we carry out a systematic analysis to reveal the conditions under which human mobility data provides (or not) an enhancement over individual regional COVID-19 case prediction models that do not use mobility as a source of information. Our analysis—focused on U.S. county-based COVID-19 case prediction models—shows that (1) at most, 60% of counties improve their performance after adding mobility data; (2) the performance improvements are modest, with median correlation improvements of approximately 0.13; (3) improvements were lower for counties with higher Black, Hispanic, and other non-White populations as well as low-income and rural populations, pointing to potential bias in the mobility data negatively impacting predictive performance; and (4) different mobility datasets, predictive models, and training approaches bring about diverse performance improvements.
Relation with Free Objects for Action Recognition

Relevant objects are widely used for aiding human action recognition in still images. Such objects are founded by a dedicated and pre-trained object detector in all previous methods. Such methods have two drawbacks. First, training an object detector requires intensive data annotation. This is costly and sometimes unaffordable in practice. Second, the relation between objects and humans are not fully taken into account in training. This work proposes a systematic approach to address the two problems. We propose two novel network modules. The first is an object extraction module that automatically finds relevant objects for action recognition, without requiring annotations. Thus, it is free. The second is a human-object relation module that models the pairwise relation between humans and objects, and enhances their features. Both modules are trained in the action recognition network, end-to-end. Comprehensive experiments and ablation studies on three datasets for action recognition in still images demonstrate the effectiveness of the proposed approach. Our method yields state-of-the-art results. Specifically, on the HICO dataset, it achieves 44.9% mAP, which is 12% relative improvement over the previous best result. In addition, this work makes an observational contribution that it is no longer necessary to rely on a pre-trained object detector for this task. Relevant objects can be found via end-to-end learning with only action labels. This is encouraging for action recognition in the wild. Models and code will be released.
In vivo visualization of propagating α -synuclein pathologies in mouse and marmoset models by a bimodal imaging probe, C05-05

Deposition of intracellular α -synuclein fibrils is implicated in neurodegenerative parkinsonian disorders, while high-contrast in vivo detection of α -synuclein depositions has been unsuccessful in animal models and humans. Here, we have developed a bimodal imaging probe, C05-05, for visualizing α -synuclein inclusions in the brains of living animals modeling α -synuclein propagation. In vivo optical and PET imaging of a mouse model demonstrated visualization of α - synuclein aggregates by C05-05, revealing a dynamic propagation of fibrillogenesis along neural pathways followed by disruptions of these structures. Moreover, longitudinal 18 F-C05-05-PET of a marmoset model captured widespread dissemination of fibrillary pathologies accompanied by neurodegeneration detected by dopamine transporter PET. In addition, in vitro assays demonstrated the high-affinity binding of 18 F-C05-05 to α -synuclein versus other protein pathologies in human brain tissues. Collectively, we propose a new imaging technology enabling etiological and therapeutic assessments of α -synuclein pathogenesis at non-clinical levels, highlighting the applicability of C05-05 to clinical PET.
Survey on Narrative Structure: from Linguistic Theories to Automatic Extraction Approaches

Narration is an essential element in the transmission of written and oral stories and corresponds to both who is telling the story and how the story is told. Philosophers and structuralists have analyzed and defined different narrative structures. Recently, researchers in Machine Learning (ML) or Natural Language Processing (NLP) have been particularly interested on the extraction and understanding of narrative structure in different collections. On this work, we present a survey on theories, research and techniques around narrative structure: from linguistic theories to automatic approaches used for the extraction and analysis of narrative structure in different multimedia collections and annotation tools.
Serotonin therapies for opioid-induced dysphagia and respiratory depression: sex differences in a rat electromyography model

Opioids are well-known to cause respiratory depression, but despite clinical evidence of dysphagia, the effects of opioids on swallow excitability and motor pattern are unknown. We sought to test the effects of the clinically-relevant opioid buprenorphine on pharyngeal swallow and respiratory drive in male and female rats. We also evaluated utility of serotonin 5-HT1A agonists (8-OH-DPAT and buspirone) to improve swallowing and breathing outcomes following buprenorphine administration. Experiments were performed on 44 freely breathing Sprague Dawley rats anesthetized with sodium pentobarbital. Bipolar fine wire electrodes were inserted into the mylohyoid, thyroarytenoid, posterior cricoarytenoid, thyropharyngeus and diaphragm muscles to measure electromyographic (EMG) activity of swallowing and breathing behaviors. We evaluated the hypotheses that swallow varies by stimulus, opioids depress swallow and breathing, and that 5-HT1A agonists improve these depressions. Our results largely confirmed the hypotheses: 1) Swallow-related muscle activity was larger during swallows elicited by oral water infusion plus esophageal distension than by either stimulus alone. 2) Buprenorphine depressed swallow in both sexes, but most significantly in females. 3) Female animals were more susceptible to buprenorphine-induced respiratory arrest. 4) 8-OH-DPAT rescued breathing following buprenorphine-induced respiratory arrest, and pre-treatment with the partial 5-HT1A agonist buspirone prevented buprenorphine-induced respiratory arrest in female animals. 5) 8- OH-DPAT enhanced swallow-related mylohyoid drive, but did not restore excitability of the swallow pattern generator following total suppression by buprenorphine. Our results highlight sex-specific and behavior-specific effects of buprenorphine and provide pre-clinical evidence of a 5HT1A agonist for the treatment of respiratory depression and dysphagia. New & Noteworthy This is the first manuscript to evaluate sex-specific effects of opioid administration on pharyngeal swallow. We expand on a small but growing number of studies that report a lower threshold for opioid-induced respiratory depression in females than males, and are the first to produce this effect with the partial mu-opioid-receptor agonist buprenorphine. Our study is the first to demonstrate that activation of 5-HT1A receptors can improve swallow and breathing outcomes following systemic buprenorphine administration.
Switching Go̅-Martini for Investigating Protein Conformational Transitions and Associated Protein–Lipid Interactions

Proteins are dynamic biomolecules that can transform between different conformational states when exerting physiological functions, which is difficult to simulate by using all-atom methods. Coarse-grained Gō-like models are widely-used to investigate large-scale conformational transitions, which usually adopt implicit solvent models and therefore cannot explicitly capture the interaction between proteins and surrounding molecules, such as water and lipid molecules. Here, we present a new method, named Switching Gō-Martini, to simulate large-scale protein conformational transitions between different states, based on the switching Gō method and the coarse-grained Martini 3 force field. The method is straight-forward and efficient, as demonstrated by the benchmarking applications for multiple protein systems, including glutamine binding protein (GlnBP), adenylate kinase (AdK), and β2-adrenergic receptor (β2AR). Moreover, by employing the Switching Gō-Martini method, we can not only unveil the conformational transition from the E2Pi-PL state to E1 state of the Type 4 P-type ATPase (P4-ATPase) flippase ATP8A1-CDC50, but also provide insights into the intricate details of lipid transport.
Olfactomedin-4+ neutrophils exacerbate intestinal epithelial damage and worsen host survival after Clostridioides difficile infection

Neutrophils are key first responders to Clostridioides difficile infection (CDI). Excessive tissue and blood neutrophils are associated with worse histopathology and adverse outcomes, however their functional role during CDI remains poorly defined. Utilizing intestinal epithelial cell (IEC)-neutrophil co-cultures and a pre-clinical animal model of CDI, we show that neutrophils exacerbate C. difficile-induced IEC injury. We utilized cutting-edge single-cell transcriptomics to illuminate neutrophil subtypes and biological pathways that could exacerbate CDI-associated IEC damage. As such, we have established the first transcriptomics atlas of bone marrow (BM), blood, and colonic neutrophils after CDI. We found that CDI altered the developmental trajectory of BM and blood neutrophils towards populations that exhibit gene signatures associated with pro-inflammatory responses and neutrophil-mediated tissue damage. Similarly, the transcriptomic signature of colonic neutrophils was consistent with hyper-inflammatory and highly differentiated cells that had amplified expression of cytokine-mediated signaling and degranulation priming genes. One of the top 10 variable features in colonic neutrophils was the gene for neutrophil glycoprotein, Olfactomedin 4 (OLFM4). CDI enhanced OLFM4 mRNA and protein expression in neutrophils, and OLFM4+ cells aggregated to areas of severe IEC damage. Compared to uninfected controls, both humans and mice with CDI had higher concentrations of circulating OLFM4; and in mice, OLFM4 deficiency resulted in faster recovery and better survival after infection. Collectively, these studies provide novel insights into neutrophil-mediated pathology after CDI and highlight the pathogenic role of OLFM4+ neutrophils in regulating CDI-induced IEC damage. One Sentence Summary Utilizing single-cell transcriptomics, IEC-epithelial co-cultures, and pre-clinical models of CDI, we have identified a subset of neutrophils that are marked by OLFM4 expression as pathogenic determinants of IEC barrier damage after CDI.
High-level prediction errors in low-level visual cortex

Perception and behaviour are significantly moulded by expectations derived from our prior knowledge. Hierarchical predictive processing theories provide a principled account of the neural mechanisms underpinning these processes, casting perception as a hierarchical inference process. While numerous studies have shown stronger neural activity for surprising inputs, in line with this account, it is unclear what predictions are made across the cortical hierarchy, and therefore what kind of surprise drives this upregulation of activity. Here we leveraged fMRI and visual dissimilarity metrics derived from a deep neural network to arbitrate between two hypotheses: prediction errors may signal a local mismatch between input and expectation at each level of the cortical hierarchy, or prediction errors may incorporate feedback signals and thereby inherit complex tuning properties from higher areas. Our results are in line with this second hypothesis. Prediction errors in both low- and high-level visual cortex primarily scaled with high-level, but not low-level, visual surprise. This scaling with high-level surprise in early visual cortex strongly diverges from feedforward tuning, indicating a shift induced by predictive contexts. Mechanistically, our results suggest that high-level predictions may help constrain perceptual interpretations in earlier areas thereby aiding perceptual inference. Combined, our results elucidate the feature tuning of visual prediction errors and bolster a core hypothesis of hierarchical predictive processing theories, that predictions are relayed top-down to facilitate perception.
ByteGAP: A Non-continuous Distributed Graph Computing System using Persistent Memory

Graph computing systems play a critical role in a variety of industrial applications. This study examines ByteDance’s graph computing system workload, which challenges the conventional notion of a one-shot, lightweight graph computing task that can scale to trillions of edges. The workload includes both small and large-scale tasks separated by a 1000-second runtime threshold. The majority of the workload is dominated by small-scale tasks submitted arbitrarily, but with high time-sensitive requirements. Large-scale tasks make up the bulk of computing resources and occur periodically. Therefore, the graph computing system must be capable of pausing running tasks and prioritizing more critical ones. In this paper, we introduce ByteGAP, a non-continuous graph computing system that leverages PMEM’s unique features, such as durability, byte-addressability, memory-like access, lower latency, and high capacity. The non-continuous approach uses checkpointing mechanisms to achieve effective fault detection and recovery. ByteGAP provides two key contributions: (1) lightweight distributed checkpointing based on PMEM, (2) efficient dual-mode PMEM management for optimizing PMEM read and write operations. Moreover, we present a comprehensive evaluation method that demonstrates the system’s ability to handle the challenges associated with large-scale computing tasks. The findings lay the foundation for future research in distributed graph computing systems and advocate for a non-continuous approach to graph computing.