Miracle-dz/UniFAR-FTUs huggingface dataset:
Building Atomic, Crash-Consistent Data Stores with Disaggregated Persistent Memory

Byte-addressable persistent memories (PM) has finally made their way into production. An important and pressing problem that follows is how to deploy them in existing datacenters. One viable approach is to attach PM as self-contained devices to the network as disaggregated persistent memory, or DPM. DPM requires no changes to existing servers in datacenters; without the need to include a processor, DPM devices are cheap to build; and by sharing DPM across compute servers, they offer great elasticity and efficient resource packing. This paper explores different ways to organize DPM and to build data stores with DPM. Specifically, we propose three architectures of DPM: 1) compute nodes directly access DPM (DPM-Direct); 2) compute nodes send requests to a coordinator server, which then accesses DPM to complete a request (DPM-Central); and 3) compute nodes directly access DPM for data operations and communicate with a global metadata server for the control plane (DPM-Sep). Based on these architectures, we built three atomic, crash-consistent data stores. We evaluated their performance, scalability, and CPU cost with micro-benchmarks and YCSB. Our evaluation results show that DPM-Direct has great small-size read but poor write performance; DPM-Central has the best write performance when the scale of the cluster is small but performs poorly when the scale increases; and DPM-Sep performs well overall.
Scale-Aware Trident Networks for Object Detection

Scale variation is one of the key challenges in object detection. In this work, we ﬁrst present a controlled experiment to investigate the effect of receptive ﬁelds for scale variation in object detection. Based on the ﬁndings from the exploration experiments, we propose a novel Trident Network (TridentNet) aiming to generate scale-speciﬁc feature maps with a uniform representational power. We construct a parallel multi-branch architecture in which each branch shares the same transformation parameters but with different receptive ﬁelds. Then, we adopt a scale-aware training scheme to specialize each branch by sampling object instances of proper scales for training. As a bonus, a fast approximation version of TridentNet could achieve signiﬁcant improvements without any additional parameters and computational cost compared with the vanilla detector. On the COCO dataset, our TridentNet with ResNet-101 backbone achieves state-of-the-art single-model results of 48.4 mAP. Codes are available at https://git.io/fj5vR.
Automated Data Slicing for Model Validation: A Big Data - AI Integration Approach

As machine learning systems become democratized, it becomes increasingly important to help users easily debug their models. However, current data tools are still primitive when it comes to helping users trace model performance problems all the way to the data. We focus on the particular problem of slicing data to identify subsets of the validation data where the model performs poorly. This is an important problem in model validation because the overall model performance can fail to reflect that of the smaller subsets, and slicing allows users to analyze the model performance on a more granular-level. Unlike general techniques (e.g., clustering) that can find arbitrary slices, our goal is to find interpretable slices (which are easier to take action compared to arbitrary subsets) that are problematic and large. We propose <inline-formula><tex-math notation="LaTeX">$\mathsf{Slice Finder}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">SliceFinder</mml:mi></mml:math><inline-graphic xlink:href="whang-ieq1-2916074.gif"/></alternatives></inline-formula>, which is an interactive framework for identifying such slices using statistical techniques. Applications include diagnosing model fairness and fraud detection, where identifying slices that are interpretable to humans is crucial. This research is part of a larger trend of Big data and Artificial Intelligence (AI) integration and opens many opportunities for new research.
NVS Machines: Learning Novel View Synthesis with Fine-grained View Control

We present an approach that learns to synthesize high-quality, novel views of 3D objects or scenes, while providing fine-grained and precise control over the 6-DOF viewpoint. The approach is self-supervised and only requires 2D images and associated view transforms for training. Our main contribution is a network architecture that leverages a transforming auto-encoder in combination with a depth-guided warping procedure to predict geometrically accurate unseen views. Leveraging geometric constraints renders direct supervision via depth or flow maps unnecessary. If large parts of the object are occluded in the source view, a purely learning based prior is used to predict the values for dis-occluded pixels. Our network furthermore predicts a per-pixel mask, used to fuse depth-guided and pixel-based predictions. The resulting images reflect the desired 6-DOF transformation and details are preserved. We thoroughly evaluate our architecture on synthetic and real scenes and under fine-grained and fixed-view settings. Finally, we demonstrate that the approach generalizes to entirely unseen images such as product images downloaded from the internet.
Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions

While the history of machine learning so far largely encompasses a series of problems posed by researchers and algorithms that learn their solutions, an important question is whether the problems themselves can be generated by the algorithm at the same time as they are being solved. Such a process would in effect build its own diverse and expanding curricula, and the solutions to problems at various stages would become stepping stones towards solving even more challenging problems later in the process. The Paired Open-Ended Trailblazer (POET) algorithm introduced in this paper does just that: it pairs the generation of environmental challenges and the optimization of agents to solve those challenges. It simultaneously explores many different paths through the space of possible problems and solutions and, critically, allows these stepping-stone solutions to transfer between problems if better, catalyzing innovation. The term open-ended signifies the intriguing potential for algorithms like POET to continue to create novel and increasingly complex capabilities without bound. Our results show that POET produces a diverse range of sophisticated behaviors that solve a wide range of environmental challenges, many of which cannot be solved by direct optimization alone, or even through a direct-path curriculum-building control algorithm introduced to highlight the critical role of open-endedness in solving ambitious challenges. The ability to transfer solutions from one environment to another proves essential to unlocking the full potential of the system as a whole, demonstrating the unpredictable nature of fortuitous stepping stones. We hope that POET will inspire a new push towards open-ended discovery across many domains, where algorithms like POET can blaze a trail through their interesting possible manifestations and solutions.
Anaerobic Process for Bioenergy Recovery From Dairy Waste: Meta-Analysis and Enumeration of Microbial Community Related to Intermediates Production

Dairy wastes are widely studied for the hydrogen and methane production, otherwise the changes in microbial communities related to intermediate valuable products was not deeply investigated. Culture independent techniques are useful tools for exploring microbial communities in engineered system having new insights into their structure and function as well as potential industrial application. The deep knowledge of the microbiota involved in the anaerobic process of specific waste and by-products represents an essential step to better understand the entire process and the relation of each microbial population with biochemical intermediates and final products. Therefore, this study investigated the microbial communities involved in the laboratory-scale anaerobic digestion of a mixture of mozzarella cheese whey and buttermilk amended with 5% w/v of industrial animal manure pellets. Culture-independent methods by employing high-throughput sequencing and microbial enumerations highlighted that lactic acid bacteria, such as Lactobacillaceae and Streptococcaceae dominated the beginning of the process until about day 14 when a relevant increase in hydrogen production (more than 10 ml H2 gVS-1 from days 13 to 14) was observed. Furthermore, during incubation a gradual decrease of lactic acid bacteria was detected with a simultaneous increase of Clostridia, such as Clostridiaceae and Tissierellaceae families. Moreover, archaeal populations in the biosystem were strongly related to inoculum since the non-inoculated samples of the dairy waste mixture had a relative abundance of archaea less than 0.1%; whereas, in the inoculated samples of the same mixture several archaeal genera were identified. Among methanogenic archaea, Methanoculleus was the dominant genus during all the process especially when the methane production occurred, and its relative abundance increased up to 99% at the end of the incubation time highlighting that methane was formed from dairy wastes primarily by the hydrogenotrophic pathway in the reactors.
The Cerebellar Predictions for Social Interactions: Theory of Mind Abilities in Patients With Degenerative Cerebellar Atrophy

Recent studies have focused on the role of the cerebellum in the social domain, including in Theory of Mind (ToM). ToM, or the “mentalizing” process, is the ability to attribute mental states, such as emotion, intentions and beliefs, to others to explain and predict their behavior. It is a fundamental aspect of social cognition and crucial for social interactions, together with more automatic mechanisms, such as emotion contagion. Social cognition requires complex interactions between limbic, associative areas and subcortical structures, including the cerebellum. It has been hypothesized that the typical cerebellar role in adaptive control and predictive coding could also be extended to social behavior. The present study aimed to investigate the social cognition abilities of patients with degenerative cerebellar atrophy to understand whether the cerebellum acts in specific ToM components playing a role as predictive structure. To this aim, an ad hoc social cognition battery was administered to 27 patients with degenerative cerebellar pathology and 27 healthy controls. In addition, 3D T1-weighted and resting-state fMRI scans were collected to characterize the structural and functional changes in cerebello-cortical loops. The results evidenced that the patients were impaired in lower-level processes of immediate perception as well as in the more complex conceptual level of mentalization. Furthermore, they presented a pattern of GM reduction in cerebellar portions that are involved in the social domain such as crus I-II, lobule IX and lobule VIIIa. These areas showed decreased functional connectivity with projection cerebral areas involved in specific aspects of social cognition. These findings boost the idea that the cerebellar modulatory function on the cortical projection areas subtends the social cognition process at different levels. Particularly, regarding the lower-level processes, the cerebellum may act by implicitly matching the external information (i.e., expression of the eyes) with the respective internal representation to guarantee an immediate judgment about the mental state of others. Otherwise, at a more complex conceptual level, the cerebellum seems to be involved in the construction of internal models of mental processes during social interactions in which the prediction of sequential events plays a role, allowing us to anticipate the other person's behavior.
Oculomotor and Inhibitory Control in Dyslexia

Previous research has suggested that people with dyslexia may have an impairment of inhibitory control. The oculomotor system is vulnerable to interference at various levels of the system, from high level cognitive control to peripheral neural pathways. Therefore, in this work we examined two forms of oculomotor inhibition and two forms of oculomotor interference at high and low levels of the control system. This study employed a prosaccade, antisaccade, and a recent distractor eye movement task (akin to a spatial negative priming) in order to explore high level cognitive control and the inhibition of a competing distractor. To explore low-level control we examined the frequency of microsaccades and post-saccade oscillations. The findings demonstrated that dyslexics have an impairment of volitional inhibitory control, reflected in the antisaccade task. In contrast, inhibitory control at the location of a competing distractor was equivalent in the dyslexic and non-dyslexic groups. There was no difference in the frequency of microsaccades between the two groups. However, the dyslexic group generated larger microsaccades prior to the target onset in the prosaccade and the antisaccade tasks.The groups did not differ in the frequency or in the morphology of the post-saccade oscillations. These findings reveal that the word reading and attentional difficulties of dyslexic readers cannot be attributed to an impairment in the inhibition of a visual distractor or interference from low-level oculomotor instability. We propose that the inhibitory impairment in dyslexia occurs at a higher cognitive level, perhaps in relation to the process of attentional disengagement.
Reconsidering the Mind-Wandering Reader: Predictive Processing, Probability Designs, and Enculturation

Studies on mind-wandering frequently use reading as an experimental task. In these studies, reading is conceived as a cognitive process that potentially offers a contrast to mind-wandering, because it seems to be task-related, goal-directed and stimulus-dependent. More recent work attempts to avoid the dichotomy of successful cognitive processes and processes of mind-wandering found in earlier studies. We approach the issue from the perspective that texts provoke modes of cognitive involvement different from the information processing and recall account that underlies many early mind-wandering studies. After all, reading itself is an umbrella term for activities that are characterized by a variety of phenomenological and functional signatures. We conceptualize reading and mind-wandering in relation to each other through the framework of enculturated predictive processing, which is informed by research in literary studies. Earlier accounts think of reading predominantly in terms of the construction of situation models that organize textual information. By contrast, enculturated predictive processing foregrounds shifting stances readers can take in relation to the text. Characters featuring in literary texts might be mind-wandering themselves, or passages in literary style might make the construction of a clearly defined situational model impossible. Furthermore, we take into account that texts often elicit mind-wandering through the construction of task-relevant and attention-driven virtual scenarios in readers' minds. This more complex account of reading can enrich recent attempts to understand mind-wandering as a complex, multi-dimensional phenomenon. The study of mind-wandering can benefit, we argue, from a closer attention to the process of reading and to the texts it deploys as stimuli. The emerging perspective from enculturated predictive processing and literary studies makes distinctions in reading that in turn enable research on mind-wandering to ask more precise questions about (1) different kinds of mind-wandering, (2) different modes of reading, and (3) how and where they interconnect across time.
Plumbagin Ameliorates Collagen-Induced Arthritis by Regulating Treg/Th17 Cell Imbalances and Suppressing Osteoclastogenesis

Objective: Plumbago zeylanica L. (with plumbagin as its active ingredients) has been used for centuries to treat conditions such as joint swelling, fractures, and bacterial infections, suggesting that it possesses anti-inflammatory and immunosuppressive properties. In the present study, we evaluated the potential anti-arthritic activity and related mechanisms of plumbagin. Methods: Collagen-induced arthritis (CIA) was initiated in Wistar rats with collagen type II. Plumbagin (2 and 6 mg/kg) was orally administered to rats with CIA from day 12 to day 32 post immunization. The effects of plumbagin on arthritis progression were assessed by paw swelling, clinical scoring, and histologic analysis. The percentage of Treg and Th17 were defined by flow cytometry or immunofluorescence (IF) staining. Bone erosion and resorption were assessed by micro-CT and histomorphometric analysis. Osteoclast differentiation was further determined by in vitro osteoclastogenesis assay. The molecular docking assay was used to determine the potential binding site of plumbagin. Results: Treatment with plumbagin significantly inhibited arthritis development, as well as suppressed the local and systemic inflammation. Plumbagin reciprocally regulated pro-inflammatory Th17 cell and immunosuppressive Treg cell populations. In addition, plumbagin protected inflammation-induced bone loss by inhibiting osteoclast formation and activity. Plumbagin markedly suppressed RANKL-stimulated osteoclast-specific gene expression by repressing NF-κB signaling activation and MAP kinase phosphorylation. Further study via molecular docking assay demonstrated that plumbagin bound to MET169 of JNK kinase and LYS138 and SER183 of p38 kinase. Conclusion: Plumbagin not only attenuates the immune-induced arthritis by inhibiting inflammation, but also protects bone erosion by directly inhibiting osteoclast formation and activity. These data suggest plumbagin is a promising new candidate drug for treating inflammatory joint diseases.
Zika Virus: Origins, Pathological Action, and Treatment Strategies

The Zika virus (ZIKV) global epidemic prompted the World Health Organization to declare it a 2016 Public Health Emergency of International Concern. The overwhelming experience over the past several years teaches us that ZIKV and the associated neurological complications represent a long-term world-wide challenge to public health. Although the number of ZIKV cases in the Western Hemisphere has dropped since 2016, the need for basic research and anti-ZIKV drug development remains strong. Re-emerging viruses like ZIKV are an ever-present threat in the 21st century where fast transcontinental travel lends itself to viral epidemics. Here, we first present the origin story for ZIKV and review the rapid progress researchers have made toward understanding of the ZIKV pathology and in the design, re-purposing, and testing–particularly in vivo–drug candidates for ZIKV prophylaxis and therapy ZIKV. Quite remarkably, a short, but intensive, drug-repurposing effort has already resulted in several readily available FDA-approved drugs that are capable of effectively combating the virus in infected adult mouse models and, most importantly, in both preventing maternal-fetal transmission and severe microcephaly in newborns in pregnant mouse models.
Targeting the Bcl-2 Family in B Cell Lymphoma

Although lymphoma is a very heterogeneous group of biologically complex malignancies, tumor cells across all B cell lymphoma subtypes share a set of underlying traits that promote the development and sustain malignant B cells. One of these traits, the ability to evade apoptosis, is essential for lymphoma development. Alterations in the Bcl-2 family of proteins, the key regulators of apoptosis, is a hallmark of B cell lymphoma. Significant efforts have been made over the last 30 years to advance knowledge of the biology, molecular mechanisms, and therapeutic potential of targeting Bcl-2 family members. In this review, we will highlight the complexities of the Bcl-2 family, including our recent discovery of overexpression of the anti-apoptotic Bcl-2 family member Bcl-w in lymphomas, and describe recent advances in the field that include the development of inhibitors of anti-apoptotic Bcl-2 family members for the treatment of B cell lymphomas and their performance in clinical trials.
Novel Resilience Phenotypes Using Feed Intake Data From a Natural Disease Challenge Model in Wean-to-Finish Pigs

The objective of this study was to extract novel phenotypes related to disease resilience using daily feed intake data from growing pigs under a multifactorial natural disease challenge that was designed to mimic a commercial environment with high disease pressure to maximize expression of resilience. Data used were the first 1,341 crossbred wean-to-finish pigs from a research facility in Québec, Canada. The natural challenge was established under careful veterinary oversight by seeding the facility with diseased pigs from local health-challenged farms, targeting various viral and bacterial diseases, and maintaining disease pressure by entering batches of 60–75 pigs in a continuous flow system. Feed intake (FI) is sensitive to disease, as pigs tend to eat less when they become ill. Four phenotypes were extracted from the individual daily FI data during finishing as novel measures of resilience. The first two were daily variability in FI or FI duration, quantified by the root mean square error (RMSE) from the within individual regressions of FI or duration at the feeder (DUR) on age (RMSEFI and RMSEDUR). The other two were the proportion of off-feed days, classified based on negative residuals from a 5% quantile regression (QR) of daily feed intake or duration data on age across all pigs (QRFI and QRDUR). Mortality and treatment rate had a heritability of 0.13 (±0.05) and 0.29 (±0.07), respectively. Heritability estimates for RMSEFI, RMSEDUR, QRFI, and QRDUR were 0.21 (±0.07) 0.26 (±0.07), 0.15 (±0.06), and 0.23 (±0.07), respectively. Genetic correlations of RMSE and QR measures with mortality and treatment rate ranged from 0.37 to 0.85, with QR measures having stronger correlations with both. Estimates of genetic correlations of RMSE measures with production traits were typically low, but often favorable (e.g., −0.31 between RMSEFI and finishing ADG). Although disease resilience was our target, fluctuations in FI and duration can be caused by many factors other than disease and should be viewed as overall indicators of general resilience to a variety of stressors. In conclusion, daily variation in FI or duration at the feeder can be used as heritable measures of resilience.