Miracle-dz/UniFAR-FTUs huggingface dataset:
Convergence results for continuous-time dynamics arising in ant colony optimization

This paper studies the asymptotic behavior of several continuous-time dynamical systems which are analogs of ant colony optimization algorithms that solve shortest path problems. Local asymptotic stability of the equilibrium corresponding to the shortest path is shown under mild assumptions. A complete study is given for a recently proposed model called EigenAnt: global asymptotic stability is shown, and the speed of convergence is calculated explicitly and shown to be proportional to the difference between the reciprocals of the second shortest and the shortest paths.
Chaos: versatile and efficient all-to-all data sharing and in-network processing at scale

An important building block for low-power wireless systems is to efficiently share and process data among all devices in a network. However, current approaches typically split such all-to-all interactions into sequential collection, processing, and dissemination phases, thus handling them inefficiently. We introduce Chaos, the first primitive that natively supports all-to-all data sharing in low-power wireless networks. Different from current approaches, Chaos embeds programmable in-network processing into a communication support based on synchronous transmissions. We show that this design enables a variety of common all-to-all interactions, including network-wide agreement and data aggregation. Results from three testbeds and simulations demonstrate that Chaos scales efficiently to networks consisting of hundreds of nodes, achieving severalfold improvements over LWB and CTP/Drip in radio duty cycle and latency with almost 100 % reliability across all scenarios we tested. For example, Chaos computes simple aggregates, such as the maximum, in a 100-node multi-hop network within less than 90 milliseconds.
Students as co-producers in a multidisciplinary software engineering project: addressing cultural distance and cross-cohort handover

ABSTRACT This article reports an undergraduate software engineering project in which, over a period of 2 years, four student teams from different cohorts developed a note-taking app for four academic clients at the students’ own university. We investigated how projects involving internal clients can give students the benefits of engaging in real software development while also giving them experience of a student-staff collaboration that has its own benefits for students, academics, and the university more broadly. As the university involved is a Sino-Foreign university located in China, where most students are Chinese and most teaching staff are not, this ‘student as co-producer’ approach interacts with another feature of the project: cultural distance. Based on analysis of notes, reports, interviews, and focus groups, we recommend that students should be provided with communicative strategies for dealing with academics as clients; universities should develop policies on ownership of student-staff collaborations; and projects should include a formalised handover process. This article can serve as guidance for educators considering a ‘students as co-producers’ approach for software development projects.
Bio-inspired models of memory capacity, recall performance and theta phase precession in the hippocampus

The hippocampus plays an important role in the encoding and retrieval of spatial and non-spatial memories. Much is known about the anatomical, physiological and molecular characteristics as well as the connectivity and synaptic properties of various cell types in the hippocampal circuits [1], but how these detailed properties of individual neurons give rise to the encoding and retrieval of memories remains unclear. Computational models play an instrumental role in providing clues on how these processes may take place. Here, we present three computational models of the region CA1 of the hippocampus at various levels of detail. Issues such as retrieval of memories as a function of cue loading, presentation frequency and learning paradigm, memory capacity, recall performance, and theta phase precession in the presence of dopamine neuromodulation and various types of inhibitory interneurons are addressed. The models lead to a number of experimentally testable predictions that may lead to a better understanding of the biophysical computations in the hippocampus.
The Effect of Selenium Supplementation on Acute Phase Reactants and Thyroid Function Tests in Hemodialysis Patients

Background: Selenium deficiency is a common problem in patients with chronic kidney disease (CKD). This micronutrient has anti-inflammatory and anti-oxidant effects. Selenium is also found in high concentrations in the thyroid gland. Objectives: To determine the effect of selenium supplementation on thyroid function tests and acute phase reactants in hemodialysis patients. Patients and Methods: In this double-blinded randomized clinical in 3 months, 64 hemodialysis patients with selenium deficiency were divided into experimental (received selenium supplementation; 32 cases) or control group (received placebo; 32 cases). Erythrocyte sedimentation rate (ESR), ferritin, quantitative C-reactive protein (CRP) and thyroid function tests (TFTs) including thyroid stimulating hormone (TSH), T3 resin uptake (T3RU), and free T4 were measured before and after the intervention and compared between experimental and control groups. Results: At baseline, no significant difference was found between experimental and control groups regarding CRP, ESR and ferritin serum levels. Likewise, after intervention, no significant difference was found between experimental and control groups for CRP (14.77 ± 17.93 vs. 18.29 ± 21.56 mg/L), ESR (32.90 ± 32.62 vs. 33.91 ± 31.15 mm/h) and ferritin (528.6 ± 423.07 vs. 519.52 ± 345.59 ng/mL). At baseline, no significant difference was found between experimental and control groups regarding TFTs. Likewise, after intervention, no significant difference was found between experimental and control groups for TSH (3.7 ± 2.22 vs. 2.84 ± 1.88 µU/mL), free T4 (7.19 ± 1.98 vs. 7.02 ± 1.87 µg/dL) and T3RU (30.04 ± 2.28% vs. 29.2 ± 1.98%). Conclusions: Oral selenium supplementation for three months did not have any significant effect on thyroid function tests or acute phase reactants.
Impact of Human Advice on Agent Teams : A Preliminary Report

Given a large-scale team composed of intelligent members that are following derived heuristics, the performance of the team is often suboptimal. Providing more accurate heuristics may be infeasible or even impossible. We propose an approach that incoporates a human advisor interacting with a multiagent team in which the advice that the human gives is a component of the heuristic that each agent already uses. This approach allows us to have a clear way of adjusting the level of the team’s autonomy, addresses the issue of who on the team will be affected by the advice, and also factors in advice immediately (while the team is still performing). We looked back at data from previous experiments with an advisor (fire chief) in a disaster rescue simulation. Then we applied our approach to a domain where robots maintain a room full of sensors. We studied different advisors and varied how much the team members listened. Our initial results show that the problem of how to have a team of multiagents interpret advice from a human turned out to be much harder than we first thought. Our hypothesis is that human advice will be of assistance when the human can provide strategic advice that was previously unknown to the agents. Yet the the strategic advice must still be given in a manner in which the agents can understand through their limited methods of communication.
A new variable step size LMS algorithm with application to active noise control

A new adaptive step size adjustment least mean square (LMS) algorithm is presented. The proposed algorithm modifies the existing LMS using the estimated output error as an important component for the modification of the step size. Experiment results demonstrate that application of the new algorithm leads to a significant gain in SNR (signal-to-noise ratio), thus visibly reduces the output error and greatly improves the convergence speed in the context of adaptive active noise cancellation.
Food Frequency Questionnaires and Overnight Urines Are Valid Indicators of Daidzein and Genistein Intake in U.S. Women Relative to Multiple 24-h Urine Samples

Data regarding convenient, valid methods for measuring U.S. isoflavone intake are limited. We evaluated a soy food questionnaire (SFQ), the Willett food frequency questionnaire (FFQ), and overnight urine samples relative to excretion in 24-h urine samples. We also described intake among women in a high-risk program for breast or ovarian cancer. Between April 2002 and June 2003, 451 women aged 30 to 50 yr with a family history of breast or ovarian cancer completed the SFQ and FFQ. Of them, 27 provided four 24-h and overnight urine specimens. In these women, 24-h sample measures were correlated with SFQ estimates of daidzein (Spearman r = .48) and genistein (r = .54) intake, moderately correlated with the Willett FFQ (daidzein r = .38, genistein r = .33), and strongly correlated with overnight urine excretion (daidzein r = .84, genistein r = 0.93). Among all 451 SFQ respondents, mean (median) daidzein and genistein intakes were 2.8 (0.24) and 3.9 (0.30) mg/day. Primary sources of both were soymilk, soy nuts, and tofu. We conclude that targeted soy food questionnaires, comprehensive FFQs, and multiple overnight urines are all reasonable options for assessing isoflavone intake in epidemiologic studies.
Rao-Blackwellized Particle Smoothers for Conditionally Linear Gaussian Models

Sequential Monte Carlo (SMC) methods, such as the particle filter, are by now one of the standard computational techniques for addressing the filtering problem in general state-space models. However, many applications require post-processing of data offline. In such scenarios the smoothing problem-in which all the available data is used to compute state estimates-is of central interest. We consider the smoothing problem for a class of conditionally linear Gaussian models. We present a forward-backward-type Rao-Blackwellized particle smoother (RBPS) that is able to exploit the tractable substructure present in these models. Akin to the well known Rao-Blackwellized particle filter, the proposed RBPS marginalizes out a conditionally tractable subset of state variables, effectively making use of SMC only for the “intractable part” of the model. Compared to existing RBPS, two key features of the proposed method are: 1) it does not require structural approximations of the model, and 2) the aforementioned marginalization is done both in the forward direction and in the backward direction.
Efficient Reconstruction of All-in-Focus Images Through Shifted Pinholes From Multi-Focus Images for Dense Light Field Synthesis and Rendering

Scene refocusing beyond extended depth of field for users to observe objects effectively is aimed by researchers in computational photography, microscopic imaging, and so on. Ordinary all-in-focus image reconstruction from a sequence of multi-focus images achieves extended depth of field, where reconstructed images would be captured through a pinhole in the center on the lens. In this paper, we propose a novel method for reconstructing all-in-focus images through shifted pinholes on the lens based on 3D frequency analysis of multi-focus images. Such shifted pinhole images are obtained by a linear combination of multi-focus images with scene-independent 2D filters in the frequency domain. The proposed method enables us to efficiently synthesize dense 4D light field on the lens plane for image-based rendering, especially, robust scene refocusing with arbitrary bokeh. Our novel method using simple linear filters achieves not only reconstruction of all-in-focus images even for shifted pinholes more robustly than the conventional methods depending on scene/focus estimation, but also scene refocusing without suffering from limitation of resolution in comparison with recent approaches using special devices such as lens arrays in computational photography.
STUDYING THE EFFECTS OF STRESS ON NEGOTIATION BEHAVIOR

Negotiation is a collaborative activity that requires the participation of different parties whose behaviors influence the outcome of the whole process. The work presented here focuses on the identification of such behaviors and their impact on the negotiation process. The premise for this study is that identifying and cataloging the behavior of parties during a negotiation may help to clarify the role that stress plays in the process. To do so, an experiment based on a negotiation game was implemented. During this experiment, behavioral and contextual information about participants was acquired. The data from this negotiation game were analyzed in order to identify the conflict styles used by each party and to extract behavioral patterns from the interactions, useful for the development of plans and suggestions for the associated participants. The work highlights the importance of the knowledge about social interactions as a basis for informed decision support in situations of conflict.
2.45-GHz CMOS Reflection-Type Phase-Shifter MMICs With Minimal Loss Variation Over Quadrants of Phase-Shift Range

CMOS reflection-type phase shifters with minimal insertion-loss variation over quadrants of phase-shift range are presented. Two performance enhancement techniques are proposed. First, the 3-dB quadrature hybrid is designed with a phase-compensated inductively coupled hybrid. Second, an impedance-transformed pi-resonated varactor network is presented to provide a full 360deg phase range, using a MOSFET varactor with limited reactance variation range. The design considerations and simulation are described. Two experimental 2.45-GHz phase shifters were implemented in 0.18-mum CMOS technology. One has a measured phase-shift range of 120deg with the insertion loss of 5.6 plusmn 1.2 dB in 2.33-2.60 GHz and the other has a phase range larger than 340deg with the insertion loss of 10.6 plusmn 2 dB in 2.44-2.55 GHz. Both chips are extremely compact with sizes of 0.72 and 0.66 mm2, respectively, and consume zero dc power.
2 Stroke January 2016 Definition of Outcome Variables All patients with stroke

Given the social burden of cognitive impairment caused by cerebrovascular disease, several studies have identified factors that influence cognitive outcome after stroke. A potential protective factor not yet examined in this context is bilingualism. Recent research suggests that bilingualism is associated with better cognitive function in aging and a later onset of dementia, including vascular dementia (VaD). These findings are interpreted in the context of an advantage in executive control and enhanced cognitive reserve in bilinguals. However, this effect is confounded by immigration and education and continues to be debated. To explore this further, we studied the association between bilingualism and cognitive outcome of stroke. We hypothesized that if bilinguals differ from monolinguals in vascular risk factor profile, they would present with a later occurrence of stroke. In contrast, if bilinguals have indeed a better cognitive reserve, we would expect in them the same age of stroke but a more favorable cognitive outcome. Nizam’s Institute of Medical Sciences, Hyderabad, is a clinical research center well suited to explore this relationship. Patients with stroke and dementia are assessed by the same team. Bilingualism is common, without the confounding effect of immigration, and has been systematically studied. Methods
State of the Art Interactions between Mechanical and Biological Processes in Acute Lung Injury

Human studiesand animalmodels suggest that mechanical as well as biological processes contribute to acute lung injury. While mechanical stresses and bacterial products can directly alter the endothelial and epithelial barriers in the lungs, a growing body of evidence suggests that synergistic interactions between low levels of mechanical stress and bacterial products in the lungs can cause or exacerbate acute lung injury. New approaches to disrupting these synergistic interactions between mechanical stress and innate immunity have the potential to reduce the incidence or improve the outcome of acute lung injury in humans.
Deoxynivalenol Impairs Hepatic and Intestinal Gene Expression of Selected Oxidative Stress, Tight Junction and Inflammation Proteins in Broiler Chickens, but Addition of an Adsorbing Agent Shifts the Effects to the Distal Parts of the Small Intestine

Broiler chickens are rather resistant to deoxynivalenol and thus, clinical signs are rarely seen. However, effects of subclinical concentrations of deoxynivalenol on both the intestine and the liver are less frequently studied at the molecular level. During our study, we investigated the effects of three weeks of feeding deoxynivalenol on the gut wall morphology, intestinal barrier function and inflammation in broiler chickens. In addition, oxidative stress was evaluated in both the liver and intestine. Besides, the effect of a clay-based mycotoxin adsorbing agent on these different aspects was also studied. Our results show that feeding deoxynivalenol affects the gut wall morphology both in duodenum and jejenum of broiler chickens. A qRT-PCR analysis revealed that deoxynivalenol acts in a very specific way on the intestinal barrier, since only an up-regulation in mRNA expression of claudin 5 in jejunum was observed, while no effects were seen on claudin 1, zona occludens 1 and 2. Addition of an adsorbing agent resulted in an up-regulation of all the investigated genes coding for the intestinal barrier in the ileum. Up-regulation of Toll-like receptor 4 and two markers of oxidative stress (heme-oxigenase or HMOX and xanthine oxidoreductase or XOR) were mainly seen in the jejunum and to a lesser extent in the ileum in response to deoxynivalenol, while in combination with an adsorbing agent main effect was seen in the ileum. These results suggest that an adsorbing agent may lead to higher concentrations of deoxynivalenol in the more distal parts of the small intestine. In the liver, XOR was up-regulated due to DON exposure. HMOX and HIF-1α (hypoxia-inducible factor 1α) were down-regulated due to feeding DON but also due to feeding the adsorbing agent alone or in combination with DON.
Spam email filtering with bayesian belief network: using relevant words

In this paper, we report our work on a Bayesian Belief Network approach to spam email filtering (classifying email as spam or nonspam/legitimate). Our evaluation suggests that a Bayesian Belief Network based classifier will outperform the popular Naive Bayes approach and two other famous learners: decision tree and k-NN. These four algorithms are tested on two different data sets with three different feature selection methods (Information Gain, Gain Ratio and Chi Squared) for finding relevant words. 10-fold cross-validation results show that Bayesian Belief Network performs best on both datasets. We suggest that this is because the 'dependant learner' characteristics of Bayesian Belief Network classification are more suited to spam filtering. The performance of the Bayesian Belief Network classifier could be further improved by careful feature subset selection.
FALDOI: Large Displacement Optical Flow by Astute Initialization

We propose a large displacement optical flow method that introduces a new strategy to compute a good local minimum of any optical flow energy functional. The method requires a given set of discrete matches, which can be extremely sparse, and an energy functional. The matches are used to guide a structured coordinate-descent of the energy functional around these keypoints. It results in a two-step minimization method at the finest scale which is very robust to the inevitable outliers of the sparse matcher, and it is better than the multi- scale methods, especially when there are small objects with very large displacements, that the multi-scale methods are incapable to find. Indeed, the proposed method recovers the correct motion field of any object which has at least one correct match, regardless of the magnitude of the displacement. We validate our proposal using several optical flow variational models. The results consistently outperform the coarse-to-fine approaches and achieve good qualitative and quantitative performance on the standard optical flow benchmarks.
Equality-based flow analysis versus recursive types

Equality-based control-flow analysis has been studied by Henglein, Bondorf and Jørgensen, DeFouw, Grove, and Chambers, and others. It is faster than the subset-based-0-CFA, but also more approximate. Heintze asserted in 1995 that a program can be safety checked with an equality-based control-flow analysis if and only if it can be typed with recursive types. In this article we falsify Heintze's assertion, and we present a type system equivalent to equality-based control-flow analysis. The new type system contains both recursive types and an unusual notion of subtyping. We have <italic>s</italic> ≤ <italic>t</italic> if <italic>s</italic> and <italic>t</italic> unfold to the same regular tree, and we have <inline-equation> <f>⊥≤t≤⊤ </f> </inline-equation> where <italic>t</italic> is a function type. In particular, there is no nontrivial subtyping between function types.
Optimizing Flash-based Key-value Cache Systems

Flash-based key-value cache systems, such as Facebook's McDipper [1] and Twitter's Fatcache [2], provide a cost-efficient solution for high-speed key-value caching. These cache solutions typically take commercial SSDs and adopt a Memcached-like scheme to store and manage key-value pairs in flash. Such a practice, though simple, is inefficient. We advocate to reconsider the hardware/software architecture design by directly opening device-level details to key-value cache systems. This co-design approach can effectively bridge the semantic gap and closely connect the two layers together. Leveraging the domain knowledge of key-value caches and the unique device-level properties, we can maximize the efficiency of a key-value cache system on flash devices while minimizing its weakness. We are implementing a prototype based on the Open-channel SSD hardware platform. Our preliminary experiments show very promising results.
Mining associated sensor patterns for data stream of wireless sensor networks

WSNs generate a large amount of data in the form of data stream; and mining these streams to extract useful knowledge is a highly challenging task. Existing works proposed in literature use sensor association rules measured in terms of occurrence frequency of patterns. However, these rules often generate a huge number of rules, most of which are non-informative or fail to reflect the true correlation among data objects. Additionally mining associated sensor patterns from sensor stream data, which is vital for real-time applications, has not been addressed yet in literature. In this paper, we address these problems and propose a new type of sensor behavioral pattern called associated sensor patterns which capture simultaneously association-like co-occurrence as well as substantial temporal correlations implied by such co-occurrences in sensor data. We propose a novel tree structure, called associated sensor pattern stream tree (ASPS-tree) and a new technique, called associated sensor pattern mining of data stream (ASPMS), using sliding window-based associated sensor pattern mining for WSNs. By capturing the useful knowledge of the data stream into an ASPS-tree, our ASPMS algorithm can mine associated sensor patterns in the current window with frequent pattern (FP)-growth like pattern-growth method. Extensive experimental analyses show that our technique is very efficient in discovering associated sensor patterns over sensor data stream.