Miracle-dz/UniFAR-FTUs huggingface dataset:
Handcrafted Electrocorticography Electrodes for a Rodent Behavioral Model

Electrocorticography (ECoG) is a minimally invasive neural recording method that has been extensively used for neuroscience applications. It has proven to have the potential to ease the establishment of proper links for neural interfaces that can offer disabled patients an alternative solution for their lost sensory and motor functions through the use of brain-computer interface (BCI) technology. Although many neural recording methods exist, ECoG provides a combination of stability, high spatial and temporal resolution with chronic and mobile capabilities that could make BCI systems accessible for daily applications. However, many ECoG electrodes require MEMS fabricating techniques which are accompanied by various expenses that are obstacles for research projects. For this reason, this paper presents an animal study using a low cost and simple handcrafted ECoG electrode that is made of commercially accessible materials. The study is performed on a Lewis rat implanted with a handcrafted 32-channel non-penetrative ECoG electrode covering an area of 3 × 3 mm2 on the cortical surface. The ECoG electrodes were placed on the motor and somatosensory cortex to record the signal patterns while the animal was active on a treadmill. Using a Tucker-Davis Technologies acquisition system and the software Synapse to monitor and analyze the electrophysiological signals, the electrodes obtained signals within the amplitude range of 200 µV for local field potentials with reliable spatiotemporal profiles. It was also confirmed that the handcrafted ECoG electrode has the stability and chronic features found in other commercial electrodes.
Retinal Pigment Epithelium Atrophy 1 (rpea1): A New Mouse Model With Retinal Detachment Caused by a Disruption of Protein Kinase C, θ

Purpose Retinal detachments (RDs), a separation of the light-sensitive tissue of the retina from its supporting layers in the posterior eye, isolate retinal cells from their normal supply of nourishment and can lead to their deterioration and death. We identified a new, spontaneous murine model of exudative retinal detachment, nm3342 (new mutant 3342, also referred to as rpea1: retinal pigment epithelium atrophy 1), which we characterize herein. Methods The chromosomal position for the recessive nm3342 mutation was determined by DNA pooling, and the causative mutation was discovered by comparison of whole exome sequences of mutant and wild-type controls. The effects of the mutation were examined in longitudinal studies by clinical evaluation, electroretinography (ERG), light microscopy, and marker and Western blot analyses. Results New mutant 3342, nm3342, also referred to as rpea1, causes an early-onset, complete RD on the ABJ/LeJ strain background, and central exudative RD and late-onset RPE atrophy on the C57BL/6J background. The ERG responses were normal at 2 months of age but deteriorate as mice age, concomitant with progressive pan-retinal photoreceptor loss. Genetic analysis localized rpea1 to mouse chromosome 2. By high-throughput sequencing of a whole exome capture library of an rpea1/rpea1 mutant and subsequent sequence analysis, a splice donor site mutation in the Prkcq (protein kinase C, θ) gene, was identified, leading to a skipping of exon 6, frame shift and premature termination. Homozygotes with a Prkcq-targeted null allele (Prkcqtm1Litt) have similar retinal phenotypes as homozygous rpea1 mice. We determined that the PKCθ protein is abundant in the lateral surfaces of RPE cells and colocalizes with both tight and adherens junction proteins. Phalloidin-stained RPE whole mounts showed abnormal RPE cell morphology with aberrant actin ring formation. Conclusions The homozygous Prkcqrpea1 and the null Prkcqtm1Litt mutants are reliable novel mouse models of RD and can also be used to study the effects of the disruption of PRKCQ (PKCθ) signaling in RPE cells.
Detecting access anomalies in programs with critical sections

ThE paper presents an efficient on-the-fly method for detecting access anomalies in programs that contain critical section coordinw tion. For a large class of programs, a single execution instance is sufficient to determine theexistence of an acceeaanomaly for a given input when the proposed method is used. In contrast, for the same class of programs, previous on-the-fly methods for handling critical sections can fail to detect anomalies for a given input, and can require N! execution instances to find an anomaly, where N is the degree of parallelism. An algorithm for statically determining which programs are in this class is described.
Empirical Studies of a Safe Regression Test Selection Technique

Regression testing is an expensive testing procedure utilized to validate modified software. Regression test selection techniques attempt to reduce the cost of regression testing by selecting a subset of a program's existing test suite. Safe regression test selection techniques select subsets that, under certain well-defined conditions, exclude no tests (from the original test suite) that if executed would reveal faults in the modified software. Many regression test selection techniques, including several safe techniques, have been proposed, but few have been subjected to empirical validation. This paper reports empirical studies on a particular safe regression test selection technique, in which the technique is compared to the alternative regression testing strategy of running all tests. The results indicate that safe regression test selection can be cost-effective, but that its costs and benefits vary widely based on a number of factors. In particular, test suite design can significantly affect the effectiveness of test selection, and coverage-based test suites may provide test selection results superior to those provided by test suites that are not coverage-based.
Lower Volatility , Higher Inequality : Are They Related ? Ozan Eksi

Starting from the 1980s, the US experienced both a decrease in GDP volatility and an increase in income inequality. I bring into attention that volatility and inequality are two moments of the same underlying individual income data, and document that similar concurrent changes in volatility and inequality occurred in a panel of industrialized countries in addition to US. I formalize the relation of variables and show that they are connected through the variance of individuals' income shocks and also through the degree to which these shocks are correlated across individuals. Next, I decompose inequality and volatility into the variance and correlation of the shocks. My ndings for variance and correlation quantitatively match empirical ndings from the US micro income data. They indicate that the decline in the correlation term is responsible for the opposite movements of these two variables within the last decades in the US, rather than the change in the variance that has been used in each literature. Explaining the changes in the US data is further important as the recurring pattern in the data across industrialized countries is the same that of the US, increase in inequality and decline in volatility, pointing out decline in correlation of shocks across countries. Since in the closed economy they are the local aggregate shocks that affect individuals in a parallel manner and create correlation, I also discuss that trade globalization, through foreign aggregate shocks, may explain the decline in correlation. Finally, by decomposing aggregate inequality I show that it took several decades for the US income inequality to nish responding to the changes in the economy at the time of Great Moderation, happening due to the cohort structure of the population. This nding questions time series regression estimates explaining changes in income inequality. JEL Codes: D31, E32, F40
Speech enhancement using Affine Projection Algorithm in subband

In this paper, a novel structure is proposed in order to improve the functionality of Adaptive Noise Cancellers (ANC); for this purpose Affine Projection Algorithm (APA) has been used. We exploit the Stationary Wavelet Transform (SWT) to decompose the input signals in subbnad to achieve a higher convergence rate. The reason of using SWT (an oversampled filter bank) is to reduce the effect of aliasing which produces some disturbance on the adaptive process. Since In the higher frequency subbands, there is no noticeable difference between APA and simpler approaches such as spectral subtraction or Wiener filter, these algorithms are substituted instead of APA for the sake of complexity. Simulation results show that the proposed ANC performance is better than the conventional ANC which uses adaptive algorithm in the time domain. The results of subjective and objective tests confirm this fact.
Extending Transfer Entropy Improves Identification of Effective Connectivity in a Spiking Cortical Network Model

Transfer entropy (TE) is an information-theoretic measure which has received recent attention in neuroscience for its potential to identify effective connectivity between neurons. Calculating TE for large ensembles of spiking neurons is computationally intensive, and has caused most investigators to probe neural interactions at only a single time delay and at a message length of only a single time bin. This is problematic, as synaptic delays between cortical neurons, for example, range from one to tens of milliseconds. In addition, neurons produce bursts of spikes spanning multiple time bins. To address these issues, here we introduce a free software package that allows TE to be measured at multiple delays and message lengths. To assess performance, we applied these extensions of TE to a spiking cortical network model (Izhikevich, 2006) with known connectivity and a range of synaptic delays. For comparison, we also investigated single-delay TE, at a message length of one bin (D1TE), and cross-correlation (CC) methods. We found that D1TE could identify 36% of true connections when evaluated at a false positive rate of 1%. For extended versions of TE, this dramatically improved to 73% of true connections. In addition, the connections correctly identified by extended versions of TE accounted for 85% of the total synaptic weight in the network. Cross correlation methods generally performed more poorly than extended TE, but were useful when data length was short. A computational performance analysis demonstrated that the algorithm for extended TE, when used on currently available desktop computers, could extract effective connectivity from 1 hr recordings containing 200 neurons in ∼5 min. We conclude that extending TE to multiple delays and message lengths improves its ability to assess effective connectivity between spiking neurons. These extensions to TE soon could become practical tools for experimentalists who record hundreds of spiking neurons.
EEG analysis for cognitive interference effects in a Stroop task

The present EEG study investigates neuronal mechanisms underlying stimulus-response interference effects using a Stroop task. Although recent functional MRI studies showed that the anterior cingulate cortex (ACC) is involved in conflict monitoring during the interference, precise temporal characteristics of neuronal responses and their relationships with the actual motor response time(i.e., the time of motor action) are still unclear. For this, we modified the class Stroop task using action colored words with two relevant motor responses; button pressing and pedal stepping. Complex EEG waveforms from 13 subjects and their response times for three experimental conditions (congruent, incongruent, and neutral) with two types of motor responses were analyzed for finding a special event related potential (ERP) for conflict monitoring. In results, we found that a component of the event related potential, which was a negative wave around 200ms after the stimulus onset (N2b), was significantly modulated with the amount of interference and correlated with response time. The N2b was high and response time was slow when the action word stimulus was incongruent with the required response action. Our results implied that the relatively early ERP component, N2b could be detected when a current stimulus meaning is conflict with a required response action and this signal might be useful for other mechanical purposes such as BCI applications.
Two Catalan-type Riordan Arrays and their Connections to the Chebyshev Polynomials of the First Kind

Riordan matrix methods and properties of generating functions are used to prove that the entries of two Catalan-type Riordan arrays are linked to the Chebyshev polynomials of the first kind. The connections are that the rows of the arrays are used to expand the monomials (1/2)(2x) n and (1/2)(4x) n in terms of certain Chebyshev polynomials of degree n. In addition, we find new integral representations of the central binomial coefficients and Catalan numbers.
Tools of the Trade: A Survey of Various Agent Based Modeling Platforms

Agent Based Modeling (ABM) toolkits are as diverse as the community of people who use them. With so many toolkits available, the choice of which one is best suited for a project is left to word of mouth, past experiences in using particular toolkits and toolkit publicity. This is especially troublesome for projects that require specialization. Rather than using toolkits that are the most publicized but are designed for general projects, using this paper, one will be able to choose a toolkit that already exists and that may be built especially for one's particular domain and specialized needs. In this paper, we examine the entire continuum of agent based toolkits. We characterize each based on 5 important characteristics users consider when choosing a toolkit, and then we categorize the characteristics into user-friendly taxonomies that aid in rapid indexing and easy reference.
PrIA: A Private Intelligent Assistant

Personalized services such as news recommendations are becoming an integral part of our digital lives. The problem is that they extract a steep cost in terms of privacy. The service providers collect and analyze user's personal data to provide the service, but can infer sensitive information about the user in the process. In this work we ask the question "How can we provide personalized news recommendation without sharing sensitive data with the provider? " We propose a local private intelligence assistance framework (PrIA), which collects user data and builds a profile about the user and provides recommendations, all on the user's personal device. It decouples aggregation and personalization: it uses the existing aggregation services on the cloud to obtain candidate articles but makes the personalized recommendations locally. Our proof-of-concept implementation and small scale user study shows the feasibility of a local news recommendation system. In building a private profile, PrIA avoids sharing sensitive information with the cloud-based recommendation service. However, the trade-off is that unlike cloud-based services, PrIA cannot leverage collective knowledge from large number of users. We quantify this trade-off by comparing PrIA with Google's cloud-based recommendation service. We find that the average precision of PrIA's recommendation is only 14% lower than that of Google's service. Rather than choose between privacy or personalization, this result motivates further study of systems that can provide both with acceptable trade-offs.
Whole nerve recordings with the spiral nerve cuff electrode

The feasibility of whole nerve recordings from the hypoglossal (HG) nerve is demonstrated in acute cats using the spiral nerve cuff electrode. A good contact between the nerve and the electrodes, provided by the spiral nerve cuff due to its self-coiling property, should improve the signal-to-noise ratio. An instrumentation amplifier with very low input noise characteristics is also utilized. The performance of the spiral cuff is studied in terms of signal-to-noise ratios and frequency characteristics. We conclude that the spiral nerve cuff electrode can reliably be used for acute recordings in a laboratory environment. <<ETX>>
Cognitive Modeling Suggests That Attentional Failures Drive Longer Stop-Signal Reaction Time Estimates in Attention Deficit/Hyperactivity Disorder

Mean stop-signal reaction time (SSRT) is frequently employed as a measure of response inhibition in cognitive neuroscience research on attention deficit/hyperactivity disorder (ADHD). However, this measurement model is limited by two factors that may bias SSRT estimation in this population: (a) excessive skew in “go” RT distributions and (b) trigger failures, or instances in which individuals fail to trigger an inhibition process in response to the stop signal. We used a Bayesian parametric approach that allows unbiased estimation of the shape of entire SSRT distributions and the probability of trigger failures to clarify mechanisms of stop-signal task deficits in ADHD. Children with ADHD displayed greater positive skew than their peers in both go RT and SSRT distributions. However, they also displayed more frequent trigger failures, which appeared to drive ADHD-related stopping difficulties. Results suggest that performance on the stop-signal task among children with ADHD reflects impairments in early attentional processes, rather than inefficiency in the stop process.
Two-Processor Scheduling of General Acyclic SWITCH-less Program Nets via Hybrid Priority Lists

This paper deals with two-processor scheduling for general acyclic SWITCH-less program nets with random node firing times. First, we introduce a hybrid priority list L∗ that has been shown to generate optimal schedules for the acyclic SWITCH-less program nets with unity node firing times, of which AND-nodes possess at most single input edge. Then considering the factors of existence of the AND-nodes with two input edges as well as random node firing times, we extend L∗ to design a new dynamic priority list Ld and four static priority lists {Lsi |i=1, 2, 3, 4}; and then combining Ld and Lsi (i=1, 2, 3, 4) we propose four hybrid priority lists {Li |i=1, 2, 3, 4}. Finally, we apply genetic algorithm to evaluate the schedules generated by the four lists through simulations on 400 program nets. Our simulation results show two of the four lists can generate reasonably good schedules. key words: dataow program, program net, two-processor
An analysis of the impact of a leadership intervention on an undergraduate work-based learning project for human resource management students

Purpose The purpose of this paper is to analyse the impact of an action research intervention during a work-based learning (WBL) project among human resource management (HRM) students at a business college in London. The intervention was the researcher’s meeting with the nominated group leaders to facilitate reflection on their leadership and instil confidence. Design/methodology/approach This paper is based on an action research leadership intervention on a broader undergraduate WBL module taught across nine disciplines and numerous projects. The action learning involved the phases of action, reflection, learning and planning. The sample was five group leaders on one of the projects for HRM students. A content analysis of their assessment submissions was included in the reflection, learning and planning phases. Findings Based on a content analysis, most of the group leaders acknowledged the leadership intervention in their submissions in varying degrees of quantity and quality. The findings reflect the impact of the intervention upon leadership confidence and the application of theory on practice. Specific leadership input would enhance the impact. The intervention did address confidence which impacted self-directed learning. Research limitations/implications The research is limited to a specific context and small sample. It is limited by the fact that reflective assessment work could not be used in comparison with the project assessment submissions. Practical implications The research demonstrates directly from the assessed submissions of students the benefit of WBL with a specific focus on confidence, leadership, reflection and self-directed learning. It demonstrates as an example the application of action research on a small WBL sample. Social implications The research is the evidence of the importance of leadership and confidence among mature adults in WBL contexts. Originality/value This paper demonstrates the impact of WBL on the learning of mature adults and, furthermore, the impact of a leadership intervention on the motivation of students for self-directed learning.
On the Induction of Intelligible Ensembles

Ensembles of classiiers, e.g. decision trees, often exhibit greater predictive accuracy than single classiiers alone. Bagging and boosting are two standard ways of generating and combining multiple classiiers. Unfortunately , the increase in predictive performance is usually linked to a dramatic decrease in intelligibility: ensembles are more or less black boxes comparable to neural networks. So far attempts at pruning of ensembles have not been very successful, approximately reducing ensembles into half. This paper describes a diierent approach which both tries to keep ensemble-sizes small during induction already and also limits the complexity of single classiiers rigorously. Single classiiers are decision-stumps of a prespeciied maximal depth. They are combined by majority voting. Ensembles are induced and pruned by a simple hill-climbing procedure. These ensembles can reasonably be transformed into equivalent decision trees. We conduct some empirical evaluation to investigate both predictive accuracies and classiier complexities.
Unsupervised fuzzy tournament selection 2865

Tournament selection has been widely used and studied in evolutionary algorithms. The size of tournament is a crucial parameter for this method. It influences on the algorithm convergence, the population diversity and the solution quality. This paper presents a new technique to adjust this parameter dynamically using fuzzy unsupervised learning. The efficiency of the proposed technique is shown by using several benchmark multimodal test functions.
SOME FURTHER RESULTS ON THE RECOVERY ALGORITHMS

When seeking a representation of a signal on a redundant basis one generally replaces the quest for the sparsest model by an`1 minimization and solves thus a linear program. In the presence of noise one has in addition to replace the exact reconstruction constraint by an approximate one. We consider simultaneously several ways to allow for reconstruction errors and detail the optimality conditions of each of the criterion. We then analyze if these conditions are helpful in the implementation of optimization algorithms.
Improving the Coherence of Multi-document Summaries: A Corpus Study for Modeling the Syntactic Realization of Entities

References included in multi-document summaries are often problematic. In this paper, we present a corpus study performed to derive statistical models for the syntactic realization of referential expressions. Our work shows how the syntactic realization of entities can inﬂuence the coherence of the text and provides a model for rewriting references in multi-document summaries to smooth disﬂuen-cies.
TV-L1 Optical Flow Estimation

This article describes an implementation of the optical flow estimation method introduced by Zach, Pock and Bischof in 2007. This method is based on the minimization of a functional containing a data term using the L 1 norm and a regularization term using the total variation of the flow. The main feature of this formulation is that it allows discontinuities in the flow field, while being more robust to noise than the classical approach by Horn and Schunck. The algorithm is an efficient numerical scheme, which solves a relaxed version of the problem by alternate minimization. Source Code A C implementation of this algorithm is provided. The source code and an online demo are accessible at the web page of this article 1 .