Miracle-dz/UniFAR-FTUs huggingface dataset:
Coordinated reset stimulation in a large-scale model of the STN-GPe circuit

Synchronization of populations of neurons is a hallmark of several brain diseases. Coordinated reset (CR) stimulation is a model-based stimulation technique which specifically counteracts abnormal synchrony by desynchronization. Electrical CR stimulation, e.g., for the treatment of Parkinson's disease (PD), is administered via depth electrodes. In order to get a deeper understanding of this technique, we extended the top-down approach of previous studies and constructed a large-scale computational model of the respective brain areas. Furthermore, we took into account the spatial anatomical properties of the simulated brain structures and incorporated a detailed numerical representation of 2 · 104 simulated neurons. We simulated the subthalamic nucleus (STN) and the globus pallidus externus (GPe). Connections within the STN were governed by spike-timing dependent plasticity (STDP). In this way, we modeled the physiological and pathological activity of the considered brain structures. In particular, we investigated how plasticity could be exploited and how the model could be shifted from strongly synchronized (pathological) activity to strongly desynchronized (healthy) activity of the neuronal populations via CR stimulation of the STN neurons. Furthermore, we investigated the impact of specific stimulation parameters especially the electrode position on the stimulation outcome. Our model provides a step forward toward a biophysically realistic model of the brain areas relevant to the emergence of pathological neuronal activity in PD. Furthermore, our model constitutes a test bench for the optimization of both stimulation parameters and novel electrode geometries for efficient CR stimulation.
Global chaos synchronisation of identical chaotic systems via novel sliding mode control method and its application to Zhu system

Synchronisation of chaotic systems is an important research problem in chaos theory. In this research work, a novel sliding mode control method is proposed for the global chaos synchronisation of identical chaotic systems. The general result derived using novel sliding mode control method is established using Lyapunov stability theory. As an application of the general result, the problem of global chaos synchronisation of identical Zhu chaotic systems (2010) is studied and a new sliding mode controller is derived. Numerical simulations have been shown to illustrate the phase portraits of Zhu chaotic system and the sliding mode controller design for the global chaos synchronisation of identical Zhu chaotic systems.
Evaluating Great Lakes bald eagle nesting habitat with Bayesian inference

Bayesian inference facilitated structured interpretation of a nonreplicated, experience-based survey of potential nesting habitat for bald eagles (Haliaeetus leucocephalus) along the five Great Lakes shorelines. We developed a pattern recognition (PATREC) model of our aerial search image with six habitat attributes: (a) tree cover, (b) proximity and (c) type/amount of human disturbance, (d) potential foraging habitat/shoreline irregularity, and suitable trees for (e) perching and (f) nesting. Tree cover greater than 10 percent, human disturbance more than 0.8 km away, a ratio of total to linear shoreline distance greater than 2.0, and suitable perch and nest trees were prerequisite for good eagle habitat (having sufficient physical attributes for bald eagle nesting). The estimated probability of good habitat was high (96 percent) when all attributes were optimal, and nonexistent (0 percent) when none of the model attributes were present. Of the 117 active bald eagle nests along the Great Lakes shorelines in 1992, 82 percent were in habitat classified as good. While our PATREC model provides a method for consistent interpretation of subjective surveyor experience, it also facilitates future management of bald eagle nesting habitat along Great Lakes shorelines by providing insight into the number, type, and relative importance of key habitat attributes. This practical application of Bayesian inference demonstrates the technique's advantages for effectively incorporating available expertise, detailing model development processes, enabling exploratory simulations, and facilitating long-term ecosystem monitoring.
Leukocyte-mediated Delivery of Nanotherapeutics in Inflammatory and Tumor Sites

Nanotechnology has become a powerful tool to potentially translate nanomedicine from bench to bedside. Nanotherapeutics are nanoparticles (NPs) loaded with drugs and possess the property of tissue targeting after surfaces of NPs are bio-functionalized. Designing smaller size of nanotherapeutics is presumed to increase tumor targeting based on the EPR (enhanced permeability and retention) effect. Since the immune systems possess a defence mechanism to fight diseases, there is an emerging concept that NPs selectively target immune cells to mediate the active delivery of drugs into sites of disease. In this review, we will focus on a key question of how nanotherapeutics specifically target immune cells and hijack them as a delivery vehicle to transport nanotherapeutics into disease tissues, thus possibly improving current therapies in inflammation, immune disorders and cancers. We will also discuss the challenges and opportunities for this new strategy of leukocyte-mediated delivery of nanotherapeutics.
Voice and multimodal technology for the mobile worker

The availability of real time, complete information exchange with the project information repository is critical for decision-making in construction, as information frequently has to be transmitted to and received from the project repository right on site. Information and Communication Technology, specifically wireless communications through mobile device, is seen as a key enabler of leading edge, innovative and powerful field solutions. However, the widespread usage of mobile devices is limited by antiquated and cumbersome interfaces. Speech recognition, along with VoiceXML technology on handheld smart device, should play a major role in overcoming user interface limitations for mobile devices and improve their usability for industrial field applications. This paper discusses the advantages of using VoiceXML technology for voice-enabled, construction field applications. It presents a pilot application of voice technology for inventory management, and outlines the direction of future research in the area of voice and multimodal communications that enable information mobility in AEC industry.
Fault-tolerance in a distributed management system: a case study

Our case study provides the most important conceptual lessons learned from the implementation of a Distributed Telecommunication Management System (DTMS), which controls a networked voice communication system. Major requirements for the DTMS are fault-tolerance against site or network failures, transactional safety, and reliable persistence. In order to provide distribution and persistence both transparently and fault-tolerant we introduce a two-layer architecture facilitating an asynchronous replication algorithm. Among the lessons learned are: component based software engineering poses a significant initial overhead but is worth it in the long term; a fault-tolerant naming service is a key requirement for fail-safe distribution; the reasonable granularity for persistence and concurrency control is one whole object; asynchronous replication on the database layer is superior to synchronous replication on the instance level in terms of robustness and consistency; semi-structured persistence with XML has drawbacks regarding consistency, performance and convenience; in contrast to an arbitrarily meshed object model, a accentuated hierarchical structure is more robust and feasible; a query engine has to provide a means for navigation through the object model; finally the propagation of deletion operation becomes more complex in an object-oriented model. By incorporating these lessons learned we are well underway to provide a highly available, distributed platform for persistent object systems.
Improved diagnosis by combining structural and process knowledge

This paper presents an approach for using process knowledge in addition to structural knowledge for plant-wide diagnosis. System diagnosis based on fault-detection and fault-diagnosis often uses structural knowledge (e.g. hierarchies of sub-systems and components) to identify the root cause of faults and malfunctions. The structural knowledge describes the internal relationships of a plant and is particularly suitable for diagnosis if an assembly or component breaks down, which affects other parts of the system. However, the root cause of faults cannot always be identified based on structural knowledge alone, but information about the processes (e.g. production processes) is required in addition. Based on an integration of the VDI/VDE-guideline 3682 “Formalized process description” the authors present a method to consider process knowledge for an improved system diagnosis.
The variability of compound stress in English: structural, semantic, and analogical factors

It is generally assumed that noun–noun (NN) compounds in English are stressed on the left-hand member (e.g. cóurtroom, wátchmaker). However, there is a considerable amount of variation in stress assignment (e.g. silk tíe, Madison Ávenue, singer-sóngwriter), whose significance and sources are largely unaccounted for in the literature. This article presents an experimental study in which three competing hypotheses concerning NN stress assignment are tested. The stress patterns of novel and existing compounds, as obtained in a reading experiment with native speakers of American English, were acoustically measured and analyzed. The results show that there is indeed a considerable amount of variation in stress assignment, and that all three hypothesized factors, i.e. structure, semantics, and analogy, are relevant, though to different degrees. On a theoretical level, the findings strongly suggest that a categorical approach cannot be upheld and that probability and analogy need to be incorporated into an adequate account of stress assignment in noun–noun constructions. The article also makes a methodological contribution to the debate in showing that experimental studies using pitch measurements can shed new light on the issue of variable compound stress.
Comparing metrics across TREC and NTCIR: the robustness to system bias

Test collections are growing larger, and relevance data constructed through pooling are suspected of becoming more and more incomplete and biased. Several studies have used evaluation metrics specifically designed to handle this problem, but most of them have only examined the metrics under incomplete but unbiased conditions, using random samples of the original relevance data. This paper examines nine metrics in a more realistic setting, by reducing the number of pooled systems. Even though previous work has shown that metrics based on a condensed list, obtained by removing all unjudged documents from the original ranked list, are effective for handling very incomplete but unbiased relevance data, we show that these results do not hold in the presence of system bias. In our experiments using TREC and NTCIR data, we first show that condensed-list metrics overestimate new systems while traditional metrics underestimate them, and that the overestimation tends to be larger than the underestimation. We then show that, when relevance data is heavily biased towards a single team or a few teams, the condensed-list versions of Average Precision (AP), Q-measure (Q) and normalised Discounted Cumulative Gain (nDCG), which we call AP', Q' and nDCG', are not necessarily superior to the original metrics in terms of discriminative power, i.e., the overall ability to detect pairwise statistical significance. Nevertheless, even under system bias, AP' and Q' are generally more discriminative than bpref and the condensed-list version of Rank-Biased Precision (RBP), which we call RBP'.
Direct robust Non-Negative Matrix Factorization and its application on image processing

In real applications of image processing, we frequently face outliers, which cannot be simply treated as Gaussian noise. Nonnegative Matrix Factorization (NMF) is a popular method in image processing for its good performance and elegant theoretical interpretation, however, traditional NMF is not robust enough to outliers. To robustify NMF algorithm, here we present Direct Robust Nonnegative Matrix Factorization (DRNMF) for image denoising based on the assumptions that the ground truth data is of low rank and the outliers are sparse. This method explictly models the outliers in the data, and the sparsity of the outliers is controlled by L0 norm. The experiments show that DRNMF can accurately localize the outliers, and outperforms traditional NMF in image denoising.
Showing Israel the red card. Activists engaged in pro-Palestinian sport-related campaigns

ABSTRACT This article explores the motivations of activists involved in pro-Palestinian sports-based campaigns. The activists’ intention is to bring pressure to bear upon Israel until it complies with international law and supports the rights of Palestinian people under the universal principles of human rights. In response to expressions of pro-Palestinian solidarity, the Israeli state and its supporters are interpreting such activity as a ‘new’ manifestation of ‘old’ antisemitism. In seeking to assess whether such activity is informed by antisemitism, 10 semi-structured interviews were conducted with activists to examine their motives. Their political biographies were explored as were their views on the use of sport as platform to express support for the Palestinian people and/or their displeasure at Israeli participation in international sport. One central theme was the activists’ responses to the suggestion that they were motivated by antisemitism. A qualitative content analysis of the interview transcripts revealed a shared observation that the accusation of antisemitism was a ‘shameless tactic’ employed by those seeking to cover up the ongoing injustices experienced by the Palestinian people. Sport was seen as a legitimate platform for political activity, to raise public awareness and to put pressure on the Israeli state. The findings contribute to a better understanding of activist motivations, the use of sport as a political platform and the challenges facing sport and its governing bodies.
Illumination normalization for robust face recognition using edge-preserving filtering

A new illumination normalization method based on the Retinex illumination model for face recognition is proposed. Total variation under L2 constraint (TV-L2) model and wiener filtering are used to estimate illumination, respectively. Their capability of edge-preserving can impair the notorious halo effect. Illumination normalized face image can be obtained by subtracting the luminance image from the original image in logarithm domain. For validating the effectiveness of the proposed method, experiments have been conducted on the representative Yale B and CMU-PIE face databases. The Experimental results show that the presented method can weaken the effect of uneven illumination effectively and improve face recognition rate.
Evaluation of Student's Physiological Response Towards E-Learning Courses Material by Using GSR Sensor

This study aims to evaluate student physiological response towards the e-learning materials. The experiments were conducted by introducing two contracting e-learning materials, i.e., the one is characterized as interactive material and the other is non-interactive one. During the experiment physiological sensor, i.e., galvanic skin response (GSR) sensor was attached to the participant. Furthermore, GSR data were extracted by feature generator, LDA. The purpose of feature extraction is to find preferably small number of features that are particularly distinguishing or informative for the classification process and that are invariant to irrelevant transformations of the data. Finally, several classifiers were performed discriminating student attitude towards e-learning course materials response using GSR sensor data. The results showed that discriminant analysis (DA) and support vector machine (SVM) give high accuracy rate, while the k-nearest neighbor (KNN) give moderate accuracy rate.
CSE 255 Assignment 1 Improved image-based recommendations for what not to wear dataset

We extend the cited model in two fundamentally different ways. First, we include different distance functions between image features to capture relationship between images of products that are visually different but related to each other (e.x. white shirt and blue jeans). Secondly, we exploit the category information present as meta-data to find relationships between products (e.x.product-pair belonging to categories boys − > badminton shoes − > Nike and boys − > badminton shoes − > Adidas tend to be related). Category information in this dataset is hierarchical but our model only considers number of matching product categories between a product pair.
Perceptual contributions of blocky, blurry, noisy, and ringing synthetic artifacts to overall annoyance

Abstract. To develop a no-reference video quality model, it is important to know how the perceived strengths of artifacts are related to their physical strengths and to the perceived annoyance. When more than one artifact is present, it is important to know whether and how its corresponding perceived strength depends on the presence of other artifacts and how perceived strengths combine to determine the overall annoyance. We study the characteristics of different types of artifacts commonly found in compressed videos. We create artifact signals predominantly perceived as blocky, blurry, ringing, and noisy and combine them in various proportions. Then, we perform two psychophysical experiments to independently measure the strength and overall annoyance of these artifact signals when presented alone or in combination. We analyze the data from these experiments and propose models for the overall annoyance based on combinations of the perceptual strengths of the individual artifact signals. We also test the interactions among different types of artifact signals. The results provide interesting information that may help the development of video quality models based on artifact measurements.
Recent Trends in the Application of Meta-Heuristics to VLSI Layout Design

@yahoo.com,rahaman h@yahoo.co.in,partha@iimcal.ac. inAbstract. This paper browses through some well-known meta-heuristicsearch strategies, and brie°y discusses some of their recent applicationsto the VLSI layout design process. It starts with very brief descriptionof the diﬁerent phases of VLSI layout design, and a brief overview ofMeta-Heuristic search strategies. Since most of the VLSI layout designproblems are hard optimization problems, the concept of NP-hardness forsuch problems is next explained along with the various algorithmic frame-works to solve them. Four selected well-known meta-heuristic strategies,namely, Simulated Annealing, Genetic Algorithm, Tabu Search and AntColony Optimization are next explained, followed a comparison of thesemethods. Next, selected applications of these meta-heuristics to VLSIlayout design are discussed. Some of their advanced variants and diﬁerenthybridization techniques, adopted for superior result, are also discussedto highlight the recent research trends in meta-heuristics.
Face Emotion Perception and Executive Functioning Deficits in Depression

Frontal, limbic and temporal regions of the brain important in emotion perception and executive functioning also have been implicated in the etiology and maintenance of depression; yet, the relationships among these topics remain poorly understood. The present study evaluated emotion perception and executive functioning among 21 depressed women and 20 nondepressed women controls. Depressed women performed significantly worse than controls in emotion perception accuracy and in inhibitory control, an aspect of executive functioning, whereas the groups did not differ in other cognitive tests assessing memory, visual-spatial, motor, and attention skills. The findings suggest that emotion perception and executive functioning are disproportionately negatively affected relative to other cognitive functions, even in a high-functioning group of mildly depressed women. Measures of emotion perception and executive functioning may be of assistance in objectively measuring functional capability of the ventral and dorsal neural systems, respectively, as well as in the diagnosis of depression. We would like to thank Rachel Burns, Luis Casenas, Najat Hamid, Jessica Layne, Justin Miller, Rebecca Reiten and Megan Shaheen for their assistance in data collection and coding. We gratefully acknowledge the comments and suggestions for this manuscript by Dr. Angela Freymuth Caveney, Ph.D. This project was supported in large part by a Rachel Upjohn Clinical Scholars Award and through the assistance of the Neuropsychology Division
Combining Lexicon Expansion, Information Retrieval, and Cluster-based Ranking for Biomedical Question Answering

The Oregon Health & Science University submission to the TREC 2006 Genomics Track approached the question answer extraction task in three phases. In the first phase the biological questions were parsed into relevant entities and query expressions were generated. The second phase retrieved relevant passages from the corpus using Lucene as an information retrieval engine. The third phase performed ranking of the retrieved passages and generated the final submitted output. Through these experiments and comparison with the approaches of others we hope to learn the contribution and value of several techniques applicable to question answer extraction including: lexicon-based query term expansion, query back-off techniques for questions with few applicable passages, and passage clustering for identifying distinct aspects of question answers. Our experiments showed no improvement after cluster-based ranking. Maximal span based passage indexing proved to be too coarse, resulting in an overall average performing passage MAP of 4%.
Degeneracy of non-Abelian quantum Hall states on the torus: domain walls and conformal field theory

We analyze the non-Abelian Read–Rezayi quantum Hall states on the torus, where it is natural to employ a mapping of the many-body problem onto a one-dimensional lattice model. On the thin torus—the Tao–Thouless (TT) limit—the interacting many-body problem is exactly solvable. The Read–Rezayi states at filling ν = k/(kM+2) are known to be exact ground states of a local repulsive k+1-body interaction, and in the TT limit this is manifested in that all states in the ground state manifold have exactly k particles on any kM+2 consecutive sites. For the two-body correlations of these states also imply that there is no more than one particle on M adjacent sites. The fractionally charged quasiparticles and quasiholes appear as domain walls between the ground states, and we show that the number of distinct domain wall patterns gives rise to the nontrivial degeneracies, required by the non-Abelian statistics of these states. In the second part of the paper we consider the quasihole degeneracies from a conformal field theory (CFT) perspective, and show that the counting of the domain wall patterns maps one to one on the CFT counting via the fusion rules. Moreover we extend the CFT analysis to topologies of higher genus.