Miracle-dz/UniFAR-FTUs huggingface dataset:
Please Scroll down for Article Visual Cognition Target-target Similarity and the Attentional Blink: Task-relevance Matters!

This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.
Dynamics of rat entorhinal cortex layer II and III cells: characteristics of membrane potential resonance at rest predict oscillation properties near threshold

Neurones generate intrinsic subthreshold membrane potential oscillations (MPOs) under various physiological and behavioural conditions. These oscillations influence neural responses and coding properties on many levels. On the single‐cell level, MPOs modulate the temporal precision of action potentials; they also have a pronounced impact on large‐scale cortical activity. Recent studies have described a close association between the MPOs of a given neurone and its electrical resonance properties. Using intracellular sharp microelectrode recordings we examine both dynamical characteristics in layers II and III of the entorhinal cortex (EC). Our data from EC layer II stellate cells show strong membrane potential resonances and oscillations, both in the range of 5–15 Hz. At the resonance maximum, the membrane impedance can be more than twice as large as the input resistance. In EC layer III cells, MPOs could not be elicited, and frequency‐resolved impedances decay monotonically with increasing frequency or has only a small peak followed by a subsequent decay. To quantify and compare the resonance and oscillation properties, we use a simple mathematical model that includes stochastic components to capture channel noise. Based on this model we demonstrate that electrical resonance is closely related though not equivalent to the occurrence of sag‐potentials and MPOs. MPO frequencies can be predicted from the membrane impedance curve for stellate cells. The model also explains the broad‐band nature of the observed MPOs. This underscores the importance of intrinsic noise sources for subthreshold phenomena and rules out a deterministic description of MPOs. In addition, our results show that the two identified cell classes in the superficial EC layers, which are known to target different areas in the hippocampus, also have different preferred frequency ranges and dynamic characteristics. Intrinsic cell properties may thus play a major role for the frequency‐dependent information flow in the hippocampal formation.
Initial Experiments in Probabilistic Satissability Initial Experiments in Probabilistic Satissability

Both satissability problems and probabilistic models are popular in artiicial intelligence and computer science. This paper looks at the rich intersection between these two topics, opening the door for the use of satissability approaches in probabilistic domains. The paper examines a generic probabilistic satissability problem, which can function for probabilistic domains as Sat does for deterministic domains. Extending the analogy with Sat, the paper deenes a Davis-Putnam-Logemann-Loveland-style procedure for solving probabilistic satissability problems, and reports on a preliminary empirical exploration of the complexity of the algorithm for a collection of randomly generated probabilistic problems. The results exhibit the familiar easy-hardest-hard pattern for the diiculty of random Sat formu-lae. Special cases of the probabilistic satissabil-ity problem lie in diierent complexity classes, and one counterintuitive result is that the computational complexity and the empirical complexity of the problems examined do not track each other exactly|problems in the hardest complexity class are not the hardest to solve.
SOCIAL LEARNING IN THE CO- CURRICULUM: EXPLORING GROUP PEER TUTORING IN COLLEGE

OF DISSERTATION SOCIAL LEARNING IN THE CO-CURRICULUM: EXPLORING GROUP PEER TUTORING IN COLLEGE In a time of upheaval in American higher education, student retention continues to be a chief concern on most campuses. Peer tutoring, like other peer-based programming, is asked to serve multiple functions as a low-cost, high-impact model. This study explored the cultures of these semi-structured, co-curricular, academic-social spaces and sought to understand what happens in a group peer tutoring context that impacts students. Data was generated with students on two campuses during the spring 2014 semester using a two-phase qualitative approach. Data generating activities included observation of students and peer tutors in the tutoring spaces on each campus. The second phase of data generation included focus groups with more than 30 students on each campus. Findings suggest that the student participants on these two campuses conceive of tutoring spaces as unique, that they engage with their peers in such contexts differently than they do in other places, and that programmatic structures may influence the outcomes they achieve. Implications range from contributions to more nuanced understanding of social learning theory to the critical importance of vulnerability in student help-seeking behavior.
Personalized Learning in Mathematics

This work shows an innovative solution in order to enable the predisposition to the mathematics, by using auto-regulation of learning objectives, personalization for obtaining, in such a way, a learning path more compliant to the learner's needs. For addressing these objectives, the learning platform IWT has been integrated with the Wolfram Mathematica environment and new and interactive Learning Objects that show the potentiality of the symbolic calculus.
Wrist Actigraph Versus Self-Report in Normal Sleepers: Sleep Schedule Adherence and Self-Report Validity

This study compared the accuracy of reporting adherence to sleep instructions in participants who were informed that adherence would be verified with an actigraph (aware group) to participants not informed the actigraph would be used to assess adherence (unaware of group). Participants were college students (N = 68), who were screened for psychiatric or sleep disorders or extremes in circadian tendency. The UG had later actigraph estimates of bedtime than the AG, but the two groups did not differ on their self-report of adherence to the sleep rules. Only the UG had later actigraphic estimates of bedtimes that violated the sleep rules. These findings have implications for the accuracy of sleep diary self-reports as well as for the use of actigraphs in studies requiring people to follow specific sleep schedule instructions.
Chapter 7 Asymptotics and Coding Theory : One of the n ! • Dimensions of Terry

Terry joined the Berkeley Statistics faculty in the summer of 1987 after being the statistics head of CSIRO in Australia. His office was just down the hallway from mine on the third floor of Evans. I was beginning my third year at Berkeley then and I remember talking to him in the hallway after a talk that he gave on information theory and the Minimum Description Length (MDL) Principle of Rissanen. I was fascinated by the talk even though I did not understand everything. Terry pointed me to many papers, and before long Terry started to co-advise me (with Lucien Le Cam) as his first PhD student at Berkeley. It was truly a great privilege to work with Terry, especially as his first student at Berkeley since I had the luxury of having his attention almost every day – he would knock on my door to chat about research and to take me to the library to find references. Every Saturday I was invited to have lunch with him and his wife Sally at his rented house in the Normandy Village on Spruce Street – a cluster of rural European styled houses near campus (the most exotic part to me about the lunch was the avocado spread on a sandwich). Through my interactions with Terry, I was molded in n! • dimensions. In particular, I was mesmerised by the interplay shown to me by Terry of data, statistical models, and interpretations – it was art with rigor! I am able to pursue and enjoy this interplay in my current research, even though I ended up writing a theoretical PhD thesis. The four papers under “asymptotics and coding theory” in this volume represent the MDL research done during my study with Terry (and Rissanen) and a paper after my PhD on Information Theory proper: lossy compression. The Minimum Description Length (MDL) Principle was invented by Rissanen [7] to formalize Occam’s Razor. Based on a foundation of the coding theory of Shannon, its most successful application to date is model selection – now a hot topic again under the new name of sparse modeling or compressed sensing in the high-dimensional situation. An idea closely related to MDL was Minimum Message Length (MML) first articulated in the context of clustering in Wallace and Boulton
Risk Factors for Secondary Infertility among Women in Karachi, Pakistan

Background Secondary infertility in developing countries is mostly attributable to blockage of the fallopian tubes due to adhesions caused by reproductive tract infections. There is a dearth of information on the prevalence and causes of secondary infertility from Pakistan. This paper presents results on factors associated with secondary infertility among married women in Karachi, Pakistan. Methods A matched case-control study was conducted. Cases were women aged 15–35 years with history of at least one previous conception and currently seeking treatment for secondary infertility. Controls were women residing in the neighborhood of cases with at least one live birth and not taking treatment for secondary infertility. The age of controls was matched by ±5 years to that of cases. Data was collected from June to August 2003. Conditional logistic regression was used to determine crude and adjusted odds ratios (OR) with corresponding 95% confidence intervals (CI) for factors associated with secondary infertility. Results The final multivariate logistic regression model revealed that after adjusting for age, cases were more likely to be the housewives (AOR = 2.6, 95% CI:1.5–4.4), had used inappropriate material to absorb blood during menstruation (AOR = 9.0, 95% CI: 5.0–16.4), and at their last delivery, had a birth attendant who did not wash hands with soap and water (AOR = 3.0, 95% CI: 1.4–5.7). Moreover, women with secondary infertility were more likely to report current or past history of having STI symptoms (AOR = 3.6, 95% CI: 2.4–5.6) and use of intra-vaginal indigenous medicines during their last post-partum period (AOR = 3.1, 95% CI: 1.6–5.7). Conclusion We recommend health education and awareness messages for safe practices during menstruation, delivery, and the postpartum period for women in general. Additionally, sanitary napkins should be made available at an affordable cost, and safe delivery kits should contain educational/pictorial brochures for appropriate hand washing skills.
The Effect of Political Uprisings on the Location of Price Discovery: Evidence from Egyptian Cross-Listed Equities

Abstract: In this study, we aim to determine the location of price discovery for Egyptian cross-listed stocks around the political uprising that started on January 25, 2011, and resulted in a two-month stock market closure. We measure price discovery using Gonzalo and Granger's component share and Hasbrouck's information shares and find that, for some stocks, the contribution of the foreign market in pricing cross-listed equities increases in the period directly following the reopening of the Egyptian stock market. We discuss the factors that could explain the more pronounced shifts in the location of price discovery for some companies in the post-uprising period.
A randomized dual consensus ADMM method for multi-agent distributed optimization

Recently, the alternating direction method of multipliers (ADMM) has been used for distributed consensus optimization and is shown to converge faster than conventional approaches based on consensus subgradient. In this paper, we consider a convex optimization problem with a linearly coupled equality constraint and employ a dual consensus ADMM (DC-ADMM) method for solving the problem in a fully distributed fashion. In particular, by considering a non-ideal network where the agents can be ON and OFF randomly and the communications among agents can fail probabilistically, we propose a randomized DC-ADMM method that is robust against these non-ideal effects. Moreover, we show that the proposed randomized method is provably convergent to an optimal solution and has a worst-case O(1/k) convergence rate, where k is the iteration number. Simulation results are presented to examine the practical convergence behavior of the proposed method in the presence of randomly ON/OFF agents and non-ideal communication links.
CasOT: a genome-wide Cas9/gRNA off-target searching tool

The CRISPR/Cas or Cas9/guide RNA system is a newly developed, easily engineered and highly effective tool for gene targeting; it has considerable off-target effects in cultured human cells and in several organisms. However, the Cas9/guide RNA target site is too short for existing alignment tools to exhaustively and effectively identify potential off-target sites. CasOT is a local tool designed to find potential off-target sites in any given genome or user-provided sequence, with user-specified types of protospacer adjacent motif, and number of mismatches allowed in the seed and non-seed regions. AVAILABILITY http://eendb.zfgenetics. org/casot/ CONTACT: zfgenetics@gmail.com or bzhang@pku.edu. cn Supplementary Information: Supplementary data are available at Bioinformatics online.
Is There a Role for Acupuncture in the Symptom Management of Patients Receiving Palliative Care for Cancer? A Pilot Study of 20 Patients Comparing Acupuncture with Nurse-Led Supportive Care

Purpose A pilot study to document changes in symptoms after acupuncture or nurse-led supportive care in patients with incurable cancer. Methods Patients receiving palliative care with estimated survival of at least 3 months were screened with the Edmonton Symptom Assessment System (ESAS). Patients (n=20) with significant symptoms were randomised to receive weekly acupuncture or nurse-led supportive care for 4 weeks. ESAS scores were obtained before and after each treatment, and weekly for 6 weeks after treatment by telephone. Results 42 of 170 patients screened were eligible. 20 gave consent for recruitment. The compliance rate was 90% for acupuncture and 80% for nurse-led supportive care. Total symptom scores were reduced by an average of 22% after each acupuncture visit and by 14% after each supportive care visit. Compared with baseline, ESAS scores at the end of the follow-up period were reduced by 19% for the acupuncture arm and 26% for nurse-led supportive care. Conclusion Patients appear to benefit from incorporating acupuncture in the treatment of advanced incurable cancer. Acupuncture was well tolerated with no significant or unexpected side effects. Acupuncture had an immediate effect on all symptoms, whereas nurse-led supportive care had a larger impact 6 weeks after the final session. Both interventions appear helpful to this population and warrant further study.
Escherichia coli MazF Leads to the Simultaneous Selective Synthesis of Both “Death Proteins” and “Survival Proteins”

The Escherichia coli mazEF module is one of the most thoroughly studied toxin–antitoxin systems. mazF encodes a stable toxin, MazF, and mazE encodes a labile antitoxin, MazE, which prevents the lethal effect of MazF. MazF is an endoribonuclease that leads to the inhibition of protein synthesis by cleaving mRNAs at ACA sequences. Here, using 2D-gels, we show that in E. coli, although MazF induction leads to the inhibition of the synthesis of most proteins, the synthesis of an exclusive group of proteins, mostly smaller than about 20 kDa, is still permitted. We identified some of those small proteins by mass spectrometry. By deleting the genes encoding those proteins from the E. coli chromosome, we showed that they were required for the death of most of the cellular population. Under the same experimental conditions, which induce mazEF-mediated cell death, other such proteins were found to be required for the survival of a small sub-population of cells. Thus, MazF appears to be a regulator that induces downstream pathways leading to death of most of the population and the continued survival of a small sub-population, which will likely become the nucleus of a new population when growth conditions become less stressful.
Convex Multitask Learning with Flexible Task Clusters

Traditionally, multitask learning (MTL) assumes that all the tasks are related. This can lead to negative transfer when tasks are indeed incoherent. Recently, a number of approaches have been proposed that alleviate this problem by discovering the underlying task clusters or relationships. However, they are limited to modeling these relationships at the task level, which may be restrictive in some applications. In this paper, we propose a novel MTL formulation that captures task relationships at the feature-level. Depending on the interactions among tasks and features, the proposed method construct different task clusters for different features, without even the need of pre-specifying the number of clusters. Computationally, the proposed formulation is strongly convex, and can be efficiently solved by accelerated proximal methods. Experiments are performed on a number of synthetic and real-world data sets. Under various degrees of task relationships, the accuracy of the proposed method is consistently among the best. Moreover, the feature-specific task clusters obtained agree with the known/plausible task structures of the data.
In sickness and in health: the role of methyl-CpG binding protein 2 in the central nervous system

The array of specialized neuronal and glial cell types that characterize the adult central nervous system originates from neuroepithelial proliferating precursor cells. The transition from proliferating neuroepithelial precursor cells to neuronal lineages is accompanied by rapid global changes in gene expression in coordination with epigenetic modifications at the level of the chromatin structure. A number of genetic studies have begun to reveal how epigenetic deregulation results in neurodevelopmental disorders such as mental retardation, autism, Rubinstein–Taybi syndrome and Rett syndrome. In this review we focus on the role of the methyl‐CpG binding protein 2 (MeCP2) during development of the central nervous system and its involvement in Rett syndrome. First, we present recent findings that indicate a previously unconsidered role of glial cells in the development of Rett syndrome. Next, we discuss evidence of how MeCP2 deficiency or loss of function results in aberrant gene expression leading to Rett syndrome. We also discuss MeCP2’s function as a repressor and activator of gene expression and the role of its different target genes, including microRNAs, during neuronal development. Finally, we address different signaling pathways that regulate MeCP2 expression at both the post‐transcriptional and post‐translational level, and discuss how mutations in MeCP2 may result in lack of responsiveness to environmental signals.
Reconfigurable two-stage Nyquist filters utilizing the farrow structure

This paper introduces reconfigurable two-stage finite-length impulse response (FIR) Nyquist filters. In both stages, the Farrow structure realizes reconfigurable lowpass linear-phase FIR Nyquist filters. By adjusting the variable multipliers of the Farrow structure, various FIR Nyquist filters and integer interpolation/decimation structures are obtained, online. However, the filter design problem is solved only once, offline. Design examples illustrate the method.
Understand Group Travel Behaviors in an Urban Area Using Mobility Pattern Mining

With the development of cities, especially in developing countries, public transport is a major choice for millions city dwellers, which can ease the traffic pressure, such as crowdedness. However, for cities, in particular cities of developing countries, the continuous development of urban construction leads to the function of regions are changed, and then the collective that travel to specific locations in the city are redistributed, such as a new shopping mall in operation or a new-built community comes into service to meet travel demands of citizens. Currently, Automated Fare Collection Systems (AFCS) are widely used in cities around the world and large amounts of data from AFCS have been acquired. In this paper, we present a new framework to use the data through AFCS to discovering regions with high passenger gathering intensity and classify points in these regions with similar passenger gathering feature varying with time in dynamic way, which is called spark region. Furthermore, the novel definition group mobility pattern (GMP) is proposed to mine the regular group behavior among these spark regions. A series of analysis is employed by using large-scale and real-world data, which consists of nearly 17million people's daily public transit records, bus trajectories generated by over 14,854 buses organizations in Beijing at 20seconds interval. The actual application indicates group mobility pattern is helpful for diagnosis and understanding residence of each region with their demand for public transportation in a significant way.
Diff . Private Interface Original Data Users Estimation ( E ) Cell Partitioning ( I ) Smoothing ( S ) Sub-cube Partitioning ( P ) Query Strategy Released Data ISPE

Although the mechanism of differential privacy provides a strong guarantee for privacy protection, it remains a key open problem to find efficient algorithms for non-interactive differentially private data release while maintaining good utility. In this paper, we propose an adaptive framework, called ISPE, to release differentially private histogram data through an interactive differentially private interface and estimate arbitrary count queries with high accuracy. The data release component adaptively releases a set of histograms that explicitly exploit the underlying data indirectly observed by the differentially private interface. The estimation component uses a novel notion of neighborhood reference and proposes an algorithm based on the minimum mean squared error (MMSE) estimator that seeks an estimate answer for a given query such that the error between the estimated answer and the true answer is minimized. We present a set of experimental results demonstrating the feasibility and high accuracy of the approach using both synthetic data and real world dataset.
Aeaeêêìáaeae Èììêë Çê Çaeaeíêêêaeì Çåèçaeaeaeì Èêçìçççä Ëëaeaeàêçaeáëëìáçae Àààòþ Ïº Ë Blockinñññø

In general few components are reused as they are. Often, avai lable components are incompatible with what is required. This necessitates c omponent adaptations or the use of adapters between components. In this paper we de velop algorithms for the synthesis of adapters, coercing incompatible compo nents into meeting requirements. We concentrate on adapters for concurrent syst ems, where adapters are able to resolve synchronisation problems of concurrent components. A new interface model for components, which includes protocol in formation, allows us to generate these adapters semi-automatically.