Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding Recurrent Neural Architectures by Analyzing and Synthesizing Long Distance Dependencies in Benchmark Sequential Datasets

In order to build efficient deep recurrent neural architectures, it isessential to analyze the complexity of long distance dependencies(LDDs) of the dataset being modeled. In this context, in this pa-per, we present detailed analysis of the complexity and the degreeof LDDs (orLDD characteristics) exhibited by various sequentialbenchmark datasets. We observe that the datasets sampled from asimilar process or task (e.g. natural language, or sequential MNIST,etc) display similar LDD characteristics. Upon analysing the LDDcharacteristics, we were able to analyze the factors influencingthem; such as (i) number of unique symbols in a dataset, (ii) sizeof the dataset, (iii) number of interacting symbols within a givenLDD, and (iv) the distance between the interacting symbols. Wedemonstrate that analysing LDD characteristics can inform theselection of optimal hyper-parameters for SOTA deep recurrentneural architectures. This analysis can directly contribute to thedevelopment of more accurate and efficient sequential models. Wealso introduce the use of Strictlyk-Piecewise languages as a pro-cess to generate synthesized datasets for language modelling. Theadvantage of these synthesized datasets is that they enable targetedtesting of deep recurrent neural architectures in terms of their abil-ity to model LDDs with different characteristics. Moreover, usinga variety of Strictlyk-Piecewise languages we generate a numberof new benchmarking datasets, and analyse the performance of anumber of SOTA recurrent architectures on these new benchmarks.
Salmon Fillet Intake Led to Higher Serum Triacylglycerol in Obese Zucker Fa/Fa Rats But Not in Normolipidemic Long-Evans Rats

The triacylglycerol lowering effect of fatty fish and fish oils is well recognized, however we recently showed that salmon intake resulted in higher serum triacylglycerol concentration in obese Zucker fa/fa rats. Since effects of salmon fillet have never before been studied in rats, the objective of this study was to compare effects of salmon intake on serum lipids in hyperlipidemic obese rats with normolipidemic lean rats. Zucker fa/fa rats and Long-Evans rats were fed diets with 25% protein from baked salmon fillet and 75% protein from casein, or casein as sole protein source (control group) for four weeks. Serum triacylglycerol concentration was higher, and cholesterol and apolipoproteinB-100 concentrations were lower in Zucker fa/fa rats fed Baked Salmon Diet compared to Zucker fa/fa rats fed Control Diet, with no differences in serum triacylglycerol, cholesterol and apolipoproteinB-100 between Long-Evans rats fed Baked Salmon Diet or Control Diet. Serum triacylglycerol fatty acid composition showed greater similarities to dietary fatty acids in Zucker fa/fa rats than in Long-Evans rats. To conclude, intake of baked salmon fillet resulted in higher serum triacylglycerol concentration and lower serum cholesterol concentration in hyperlipidemic obese Zucker fa/fa rats but did not affect serum lipids in normolipidemic lean Long-Evans rats.
Efficient Crowd Exploration of Large Networks

Accurately and efficiently crowdsourcing complex, open-ended tasks can be difficult, as crowd participants tend to favor short, repetitive "microtasks". We study the crowdsourcing of large networks where the crowd provides the network topology via microtasks. Crowds can explore many types of social and information networks, but we focus on the network of causal attributions, an important network that signifies cause-and-effect relationships. We conduct experiments on Amazon Mechanical Turk (AMT) testing how workers can propose and validate individual causal relationships and introduce a method for independent crowd workers to explore large networks. The core of the method, Iterative Pathway Refinement, is a theoretically-principled mechanism for efficient exploration via microtasks. We evaluate the method using synthetic networks and apply it on AMT to extract a large-scale causal attribution network. Worker interactions reveal important characteristics of causal perception and the generated network data can help improve our understanding of causality and causal inference.
Dropping to escape: a review of an under‐appreciated antipredator defence

Dropping is a common antipredator defence that enables rapid escape from a perceived threat. However, despite its immediate effectiveness in predator–prey encounters (and against other dangers such as a parasitoid or an aggressive conspecific), it remains an under‐appreciated defence strategy in the scientific literature. Dropping has been recorded in a wide range of taxa, from primates to lizards, but has been studied most commonly in insects. Insects have been found to utilise dropping in response to both biotic and abiotic stimuli, sometimes dependent on mechanical or chemical cues. Whatever the trigger for dropping, the decision to drop by prey will present a range of inter‐related costs and benefits to the individual and so there will be subtle complexities in the trade‐offs surrounding this defensive behaviour. In predatory encounters, dropping by prey will also impose varying costs and benefits on the predator – or predators – involved in the system. There may be important trade‐offs involved in the decision made by predators regarding whether to pursue prey or not, but the predator perspective on dropping has been less explored at present. Beyond its function as an escape tactic, dropping has also been suggested to be an important precursor to flight in insects and further study could greatly improve understanding of its evolutionary importance. Dropping in insects could also prove of significant practical importance if an improved understanding can be applied to integrated pest‐management strategies. Currently the non‐consumptive effects of predators on their prey are under‐appreciated in biological control and it may be that the dropping behaviour of many pest species could be exploited via management practices to improve crop protection. Overall, this review aims to provide a comprehensive synthesis of the current literature on dropping and to raise awareness of this fascinating and widespread behaviour. It also seeks to offer some novel hypotheses and highlight key avenues for future research.
Bayes-CPACE: PAC Optimal Exploration in Continuous Space Bayes-Adaptive Markov Decision Processes

We present the first PAC optimal algorithm for Bayes-Adaptive Markov Decision Processes (BAMDPs) in continuous state and action spaces, to the best of our knowledge. The BAMDP framework elegantly addresses model uncertainty by incorporating Bayesian belief updates into long-term expected return. However, computing an exact optimal Bayesian policy is intractable. Our key insight is to compute a near-optimal value function by covering the continuous state-belief-action space with a finite set of representative samples and exploiting the Lipschitz continuity of the value function. We prove the near-optimality of our algorithm and analyze a number of schemes that boost the algorithm's efficiency. Finally, we empirically validate our approach on a number of discrete and continuous BAMDPs and show that the learned policy has consistently competitive performance against baseline approaches.
A Split-Gate Positive Feedback Device With an Integrate-and-Fire Capability for a High-Density Low-Power Neuron Circuit

Hardware-based spiking neural networks (SNNs) to mimic biological neurons have been reported. However, conventional neuron circuits in SNNs have a large area and high power consumption. In this work, a split-gate floating-body positive feedback (PF) device with a charge trapping capability is proposed as a new neuron device that imitates the integrate-and-fire function. Because of the PF characteristic, the subthreshold swing (SS) of the device is less than 0.04 mV/dec. The super-steep SS of the device leads to a low energy consumption of ∼0.25 pJ/spike for a neuron circuit (PF neuron) with the PF device, which is ∼100 times smaller than that of a conventional neuron circuit. The charge storage properties of the device mimic the integrate function of biological neurons without a large membrane capacitor, reducing the PF neuron area by about 17 times compared to that of a conventional neuron. We demonstrate the successful operation of a dense multiple PF neuron system with reset and lateral inhibition using a common self-controller in a neuron layer through simulation. With the multiple PF neuron system and the synapse array, on-line unsupervised pattern learning and recognition are successfully performed to demonstrate the feasibility of our PF device in a neural network.
Competitive Online Virtual Cluster Embedding Algorithms

In the conventional cloud service model, computing resources are allocated for tenants on a pay-per-use basis. However, the performance of applications that communicate inside this network is unpredictable because network resources are not guaranteed. To mitigate this issue, the virtual cluster (VC) model has been developed in which network and compute units are guaranteed. Thereon, many algorithms have been developed that are based on novel extensions of the VC model in order to solve the online virtual cluster embedding problem (VCE) with additional parameters. In the online VCE, the resource footprint is greedily minimized per request which is connected with maximizing the profit for the provider per request. However, this does not imply that a global maximization of the profit over the whole sequence of requests is guaranteed. In fact, these algorithms do not even provide a worst case guarantee on a fraction of the maximum achievable profit of a certain sequence of requests. Thus, these online algorithms do not provide a competitive ratio on the profit. In this thesis, two competitive online VCE algorithms and two heuristic algorithms are presented. The competitive online VCE algorithms have different competitive ratios on the objective function and the capacity constraints whereas the heuristic algorithms do not violate the capacity constraints. The worst case competitive ratios are analyzed. After that, the evaluation shows the advantages and disadvantages of these algorithms in several scenarios with different request patterns and profit metrics on the fat-tree and MDCube datacenter topologies. The results show that for different scenarios, different algorithms have the best performance with respect to certain metrics.
Stroke rehabilitation and research: consideration of the role of the cortico-reticulospinal system

Abstract A cerebrovascular accident, otherwise known as stroke, has the potential to damage multiple areas within the brain affecting descending motor control via a multitude of pathways resulting in a wide variety of movement problems. The cortico-reticulospinal system, one of the largest motor systems, is frequently affected, compromising its output, resulting in postural control deficits. The identification of clinically relevant instruments and scales to document and evaluate recovery in post-stroke patients is vital. However, the availability of such measures and scales which take into consideration the role of postural control as an integral component of functional movement performance are scarce. This paper will critically discuss the importance of integrating current neuroscience and motor control knowledge in order to better understand and describe the clinical presentation of persons post-stroke such that the effectiveness of stroke rehabilitation can be appropriately measured.
Vanguard: Detecting Missing Checks for Prognosing Potential Vulnerabilities

It is challenging to have a general solution to precisely detect arbitrary vulnerabilities. Thus security research has focused on detecting specific types of vulnerabilities. Missing checks for untrusted inputs used in security-sensitive operations are one of the major causes of various serious vulnerabilities. Efficiently detecting missing checks is essential for identifying insufficient attack protections and prognosing potential vulnerabilities. This paper proposes a systematic static approach to detect missing checks for manipulable data used in security-sensitive operations in C/C++ programs. We first locate customized security-sensitive operations with lightweight static analysis; then judge assailability of sensitive data used in security-sensitive operations via static taint analysis; finally, assess the existence and risk degree of missing checks using static analysis. We have implemented the approach into an automated and cross-platform tool, named Vanguard, on top of Clang/LLVM 3.6. 0. Experimental results on open-source projects have shown its effectiveness and efficiency. Furthermore, Vanguard has led us to uncover five known vulnerabilities and two unknown bugs.
Biomarkers of exposure and effect in a working population exposed to lead, manganese and arsenic

ABSTRACT Lead (Pb), manganese (Mn) and arsenic (As) are among the major toxicants in mining environments. Miners are commonly and repeatedly exposed to this toxic mixture. Some adverse effects may appear at concentrations below environmental quality guidelines for individual mixture components. Further, Pb, Mn, and As induce common adverse outcomes, such as interferences in the cholinergic system and heme synthesis. It is thus vital to monitor miners through biomarkers (BM), such that subclinical effects may be identified at an early stage. The main objectives of this study were to evaluate the exposure of a mining population to these three metals and determine alterations in cholinergic and heme synthesis parameters. Blood and urine samples of workers (n = 60) were obtained from a Portuguese mining industry and compared with a control population (n = 80). The levels of the metals were determined in biological samples, as well as urinary heme precursor levels, delta aminolevulinic acid (ALA) and porphyrins, and blood acetylcholinesterase (AChE) activity. The miners exhibited significantly higher values of Pb and As in blood and urine compared to control. In the case of Mn near or slightly higher than limit values were found. Our data show that heme precursors may be used simultaneously with metal levels as BMs for multiple metal exposures on an individual basis, resulting in 94.3% and 95.7% accuracy, respectively, in blood and urine, for subjects correctly identified with respect to occupation. This study also revealed that biological monitoring of this working population regarding metal body burden and heme precursor accumulation is advisable.
Lupus nephritis: An update on treatments and pathogenesis

Immunosuppressive therapies for lupus nephritis (LN) have improved significantly over the past few decades, resulting in growing number of choices for treatment individualization and improved renal and patient outcomes. Corticosteroids combined with mycophenolate or cyclophosphamide induces a satisfactory response in a high proportion of Asian and Caucasian patients, but the rate of improvement varies considerably between patients. Relatively low disease flare rate was observed in Chinese patients receiving low‐dose prednisolone and mycophenolate maintenance. Short‐term results with calcineurin inhibitors (CNI) are encouraging, attributed both to their immunosuppressive efficacy and the action of these agents on podocyte biology leading to more rapid proteinuria suppression. Additional data, especially on the avoidance of nephrotoxicity and metabolic side effects, is required to facilitate selection of patients appropriate for this treatment. Modifications of standard regimens such as reducing corticosteroid exposure or using enteric‐coated mycophenolate might help reduce treatment‐related toxicities without compromising efficacy. While clinical outcomes of patients have improved with recent therapeutic advances, individual and ethnic variations in disease manifestations and treatment response, as well as the prevention of infections and long‐term complications still present challenges to frontline clinicians. Recent data from histological examination and translational studies also suggest that complement activation via the alternative pathway, immune deposition on renal tubular basement membrane, and local inflammatory responses involving resident kidney cells are of pathogenic relevance in LN. The progress of clinical and translational studies has improved not only the understanding of disease mechanisms but also clinical decision making in the management of LN.
From Behavioral and Communication Intervention to Interaction Design: User Perspectives from Clinicians

To improve functional communication and behavioral management, many children with disabilities receive behavioral and communication-related intervention from professionals such as behavioral analysts and speech and language therapists. This paper presents user perspectives from three clinicians who have used and/or designed assistive technology with children with disabilities, and calls for researchers to recognize and leverage clinicians' knowledge to design accessible technology for children with complex sensory and communication needs.
CDF Transform-Shift: An effective way to deal with inhomogeneous density datasets

Many distance-based algorithms exhibit bias towards dense clusters in inhomogeneous datasets (i.e., those which contain clusters in both dense and sparse regions of the space). For example, density-based clustering algorithms tend to join neighbouring dense clusters together into a single group in the presence of a sparse cluster; while distance-based anomaly detectors exhibit difficulty in detecting local anomalies which are close to a dense cluster in datasets also containing sparse clusters. In this paper, we propose the CDF Transform-Shift (CDF-TS) algorithm which is based on a multi-dimensional Cumulative Distribution Function (CDF) transformation. It effectively converts a dataset with clusters of inhomogeneous density to one with clusters of homogeneous density, i.e., the data distribution is converted to one in which all locally low/high-density locations become globally low/high-density locations. Thus, after performing the proposed Transform-Shift, a single global density threshold can be used to separate the data into clusters and their surrounding noise points. Our empirical evaluations show that CDF-TS overcomes the shortcomings of existing density-based clustering and distance-based anomaly detection algorithms and significantly improves their performance.
A quantum network stack and protocols for reliable entanglement-based networks

We present a stack model for breaking down the complexity of entanglement-based quantum networks. More specifically, we focus on the structures and architectures of quantum networks and not on concrete physical implementations of network elements. We construct the quantum network stack in a hierarchical manner comprising several layers, similar to the classical network stack, and identify quantum networking devices operating on each of these layers. The layers responsibilities range from establishing point-to-point connectivity, over intra-network graph state generation, to inter-network routing of entanglement. In addition we propose several protocols operating on these layers. In particular, we extend the existing intra-network protocols for generating arbitrary graph states to ensure reliability inside a quantum network, where here reliability refers to the capability to compensate for devices failures. Furthermore, we propose a routing protocol for quantum routers which enables the generation of arbitrary graph states across network boundaries. This protocol, in correspondence with classical routing protocols, can compensate dynamically for failures of routers, or even complete networks, by simply re-routing the given entanglement over alternative paths. We also consider how to connect quantum routers in a hierarchical manner to reduce complexity, as well as reliability issues arising in connecting these quantum networking devices.
An Inquiry into the Anatomy of the Par Vagum and Spinal Accessory of the Eighth Pair of Nerves

upper part of its course, numerous experiments prove that it sends off motor, as well as sensitive branches. Arnold, Scarpa, and Bischoff attempted to explain this by conjecturing that the motor filaments of the vagus were derived from the spinal accessory, which they believed stood in the same relation to the par vagum that the anterior roots of the spinal nerves do to the posterior. Bischoff was farther strengthened in this opinion by an experiment; for, having cut the spinal accessory within the cranium of a goat, he found, that, as the roots of that nerve were divided, the voice became gradually weaker, and was lost entirely when the nerve
Nonvariceal upper gastrointestinal hemorrhage: European Society of Gastrointestinal Endoscopy (ESGE) Cascade Guideline

In order to address the status of gastrointestinal (GI) endoscopy in Africa, a European Society of Gastrointestinal Endoscopy (ESGE) International Affairs Working Group (IAWG) was established with participation from the World Endoscopy Organization (WEO) 1 . A previous survey conducted by the IAWG on the main indications of GI endoscopy in African countries showed nonvariceal upper GI hemorrhage (NVUGIH) to be among the top three indications 1 . This indication may reflect the very high prevalence of Helicobacter pylori infection, which is well known to be associated with gastroduodenal peptic ulcers and their complications, such as bleeding. In addition, the increased use of aspirin as prevention and treatment of cardiovascular disease, and the increase in life expectancy, may also, at least in part, account for the NVUGIH indication 2 3 4 . The management of NVUGIH requires a multi-step approach, involving endoscopic factors such as emergency endoscopy with different hemostatic devices, and clinical factors such as hospitalization, blood transfusion, and drugs. Such an approach is clearly sensitive to organizational, medical, pharmaceutical, and technological resources. However, the lack of structural endoscopic and clinical health resources in some underserved African areas is likely to affect the clinical outcome of patients with NVUGIH. In this regard, the previous IAWG survey revealed several critical needs in training, education, and technology that may represent a barrier to adequate NVUGIH management 1 . Most of the respondents from the African countries participating in the IAWG survey reported a lack of national guidelines for GI endoscopy; however, there was interest in the international guidelines, pending adaptation to reflect the endoscopic resources available in their regions. For this reason, the IAWG decided to apply the cascade methodology to adapt ESGE guidelines to resource-sensitive settings in partnership with African experts 1 . Cascade guidelines are resource oriented and provide hierarchical recommendations based on the resources available to the medical care provider 5 6 . In detail, four levels of resource availability – basic, limited, enhanced, and maximal levels – are identified, with most of the interest being in the basic and limited levels. We aimed to standardize the management of NVUGIH in low-resources setting. Here, we report the cascade adaptation of the ESGE guideline on the diagnosis and management of NVUGIH 7 .