Miracle-dz/UniFAR-FTUs huggingface dataset:
Potential Benefits and Costs of Concurrent Task Engagement to Maintain Vigilance

Objective: The objective of this study was to investigate the nature of concurrent task interference during a vigilance task and to determine whether a concurrent task improves performance with decreased vigilance. Background: Research has repeatedly shown that engaging in a cell phone conversation while driving increases the risk of getting into crashes. At the same time, it has also been found that task monotony could lead to an increase in crash risk. There is evidence that suggests that engaging in a concurrent task reduces the effects of monotony, leading to an improvement in vigilance task performance. Method: A monotonous drive in a driving simulator was used to investigate the effects of a concurrent verbal task. Three task conditions were used: no verbal task, continuous verbal task, and late verbal task. Results: When engaged in a secondary verbal task, drivers showed improved lane-keeping performance and steering control when vigilance was lowest. Conclusion: A strategically placed concurrent task can improve performance when vigilance is at its lowest. Application: There is potential for the design of a countermeasure system that can be strategically activated by an automated system monitoring driver performance.
Motoring to the Finish: Kinesin and Dynein Work Together to Orient the Yeast Mitotic Spindle

Two papers in this issue of The Journal of Cell Biology address the question of how the mitotic spindle is oriented within a dividing cell ([5][1], [6][2]). In this brief review, these new results will be placed in the context of other work on this topic. Spindle orientation is one of the essential
Speaker trait prediction for automatic personality perception using frequency domain linear prediction features

The aim of automatic personality perception is to predict the personality of the speaker perceived by the listener from nonverbal behavior. Extroversion, Conscientiousness, Agreeableness, Neuroticism and Openness are the speaker traits used for personality evaluation. In this work, a speaker trait prediction approach for automatic personality assessment is proposed to model the relationship between speech signal and personality traits using frequency domain linear prediction (FDLP) technique. Among several feature extraction techniques, FDLP features render increased performance. SSPNet Speaker Personality Corpus is used for experiments and evaluation. The proposed method predicts the speaker traits with 90-99% classification accuracy.
A Systematic Review and Meta-Analysis of Cyber-Victimization and Educational Outcomes for Adolescents

Adolescents’ Internet use is increasingly mobile, private, and unsupervised, which raises concerns given that the Internet increasingly serves as a medium for experiencing victimization. Although it is widely recognized that in-person victimization has a deleterious effect on adolescents’ educational outcomes, the extent to which cyber-victimization has similar effects is less well known. This systematic review and meta-analysis offers a synthesis of the relationship between cyber-victimization and educational outcomes of adolescents aged 12 to 17, including 25 effect sizes from 12 studies drawn from a variety of disciplines. A series of random-effects meta-analyses using robust variance estimation revealed associations between cyber-victimization and higher school attendance problems (r = .20) and academic achievement problems (r = .14). Results did not differ by provided definition, publication status, reporting time frame, gender, race/ethnicity, or average age. Implications for future research are discussed within context of theoretical, critical, and applied discussions.
DO SUBMILLIMETER GALAXIES REALLY TRACE THE MOST MASSIVE DARK-MATTER HALOS? DISCOVERY OF A HIGH-z CLUSTER IN A HIGHLY ACTIVE PHASE OF EVOLUTION

We present detailed observations of a z ∼ 1.99 cluster of submillimeter galaxies (SMGs), discovered as the strongest redshift spike in our entire survey of ∼100 SMGs across 800 arcmin2. It is the largest blank-field SMG concentration currently known and has <0.01% chance of being drawn from the underlying selection function for SMGs. We have compared UV observations of galaxies at this redshift, where we find a much less dramatic overdensity, having an 11% chance of being drawn from its selection function. We use this z ∼ 1.99 overdensity to compare the biasing of UV- and submillimeter-selected galaxies, and test whether SMGs could reside in less overdense environments, with their apparent clustering signal being dominated by highly active merger periods in modest mass structures. We discuss the probable mechanisms for the apparently different bias we see at the two wavelengths. This impressively active formation phase in a low-mass cluster is not something seen in simulations, although we propose a toy model using merger bias, which could account for the bias seen in the SMGs. While enhanced buildup of stellar mass appears characteristic of other high-z galaxy clusters, neither the UV galaxies nor SMGs in this structure exhibit larger stellar masses than their field galaxy counterparts (although the excess of SMGs in the structure represents a larger volume-averaged stellar mass than the field). Our findings have strong implications for future surveys of high-z galaxies at long wavelengths such as SCUBA2 and Herschel. We suggest that since these surveys will select galaxies during their episodes of peak starbursts, they could probe a much wider range of environments than just the progenitors of rich clusters, revealing more completely the key events and stages in galaxy formation and assembly.
A Genetic Approach to the Recruitment of PRC2 at the HoxD Locus

Polycomb group (PcG) proteins are essential for the repression of key factors during early development. In Drosophila, the polycomb repressive complexes (PRC) associate with defined polycomb response DNA elements (PREs). In mammals, however, the mechanisms underlying polycomb recruitment at targeted loci are poorly understood. We have used an in vivo approach to identify DNA sequences of importance for the proper recruitment of polycomb proteins at the HoxD locus. We report that various genomic re-arrangements of the gene cluster do not strongly affect PRC2 recruitment and that relatively small polycomb interacting sequences appear necessary and sufficient to confer polycomb recognition and targeting to ectopic loci. In addition, a high GC content, while not sufficient to recruit PRC2, may help its local spreading. We discuss the importance of PRC2 recruitment over Hox gene clusters in embryonic stem cells, for their subsequent coordinated transcriptional activation during development.
Test generation for combinational hardware Trojans

Hardware Trojans become a security threat to the integrated circuit supply chain. Detecting hardware Trojans is difficult as such circuits are stealthy in nature and triggered only under rare conditions. Traditional ATPG patterns are not useful for Trojan activation, and in general random patterns have to be applied for Trojan detection. In this paper we will first analyze how combinational rare conditions can be constructed in a systemic way, so that a Trojan circuit with a desired triggering probability can be synthesized accordingly. A watch list of Trojan candidates can be constructed according to the analysis. A set of test cubes can be generated from the candidates, and experimental results that the number of test cubes is restricted in most cases. The number of test vectors can be further reduced when physical layout information is taken into account. In addition, we can augment the test cubes with random assignment of X-bits to deal with addition trigger signals other than the target events. The results of this study should be helpful to the development of Trojan detection methods.
A fast overlap control in image morphing using mass-spring system

A fast and stable deformation model is essential for realistic simulation of image morphing. In order to stabilize deformation, we used two internal thin plate mass-spring systems. The deformation results are globally smoother and more stable. One-to-one deformation is one of the important issues in image morphing. We focus on fast removing overlaps in the process of deformation. To rapidly remove overlaps, the external forces are set automatically on its eight neighboring nodes. The speed of removing overlaps is faster when external forces are set on eight neighbouring nodes than when on two neighbouring nodes.
Simple and Scalable Constrained Clustering: a Generalized Spectral Method

We present a simple spectral approach to the well-studied constrained clustering problem. It captures constrained clustering as a generalized eigenvalue problem with graph Laplacians. The algorithm works in nearly-linear time and provides concrete guarantees for the quality of the clusters, at least for the case of 2-way partitioning. In practice this translates to a very fast implementation that consistently outperforms existing spectral approaches both in speed and quality.
Statistical Extraction Of Protein Surface Atoms Based On A Voxelisation Method

Proteins play a vital role in maintaining the balance of bodily functions in all living beings. However their functional properties are difficult to predict since they depend not only on the sequence of constituent amino acids but also on the 3D folding of the protein. This paper presents a new statistical method for extraction of surface atoms of a protein. The method is based on space-voxelisation and generalizes our previous deterministic method by repeating the surface extraction process for various orientations of a protein; so as to achieve a statistical consensus about surface atoms. Based on the experimental study we have established an optimal range of values of voxel occupancy for the selection of surface atoms; with optimality defined as a maximum coincidence of extracted surface atoms from the protein presented to the algorithm in 13 different orientations. The results show that the voxel occupancy threshold of between >40% and >50% allows our algorithm to extract surface atoms with high degree of confidence.
The Relevance Of Attachment Theory Principles To Psychotherapy And How It Can Contribute To Fostering Therapeutic Alliance and The Change Process For Clients: A Literature Review

This study is focused on how attachment theory can help explain the importance of the therapeutic alliance as a key factor for effective psychotherapy and how a felt sense of safety and care can help to establish and foster a therapeutic alliance through the application of clinical strategies to promote change and growth of the client through the therapeutic alliance. This study is relevant to psychotherapy is because of the key role attachment styles play in therapy (Taylor, Rietzschel, Danquah & Berry, 2015). Attachment theory informs how therapists can create a safe and caring relational environment and how this in turn can support the exploration and reconstruction of the client’s internal working models (IWMs) of self and others and insecure attachment patterns for effective affect regulation.
Network Distance Based Cache Replacement Policy for Location-Dependent Data in Mobile Environment

Data caching in mobile clients is an important technique to enhance data availability and to improve data access time. Due to cache size limitations, cache replacement policies are used to find a suitable subset of items for eviction from the cache. The existing policies focus on Euclidean space and consider Euclidean distance as a spatial parameter for eviction. However, in practice the position and movement of objects are constrained to spatial networks where the important distance measure is the network distance. In this paper we propose a cache replacement policy, which considers the network density, network distance and probability of access as important factors for eviction. A series of simulation experiments have been conducted to evaluate the performance of the policy. Results indicate that the proposed cache replacement scheme performs significantly better than the existing policies FAR and PAID.
Modeling Cache Contention and Throughput of Multiprogrammed Manycore Processors

This paper proposes an analytical model for accurately predicting the impact of contention on cache miss rates. The focus is multiprogrammed workloads running on multithreaded manycore architectures. This work addresses a key challenge facing earlier cache contention models as the number of concurrent threads exceeds the associativity of shared caches. The memory access characteristics of individual applications are obtained in isolation by profiling their circular sequences and two new measures of access locality are proposed. An evaluation of this model in the context of a Niagara processor shows that it achieves an average 8.7 percent error in miss rate predictions which improves upon the best prior model by 48.1x. This paper also presents a novel Markov chain throughput model. When combining the contention model with the Markov chain model, throughput is estimated with an average error of 8.3 percent compared to detailed simulation. Moreover, the combined model tracks throughput sufficiently well to find the same optimized design point for application-specific workloads 65 times faster than detailed simulation. This paper also shows that the models accurately predict cache contention and throughput trends across various workloads on real hardware.
Testing C Programs for Vulnerability Using Trace-Based Symbolic Execution and Satis ﬁ ability Analysis

Security testing has gained signi ﬁ cant attention recently due to the huge number of attacks against software systems. This paper presents a novel security testing method using trace-based symbolic execution and satis ﬁ ability analysis . It reuses test cases generated from traditional functional testing to produce execution traces . An execution trace is a sequence of program statements exercised by a test case. Each execution trace is symbolically executed to produce program constraints and security constraints . A program constraint is a constraint imposed by program logic on program variables. A security constraint is a condition on program variables that must be satis ﬁ ed to ensure system security. A security vulnerability exists if there is an assignment of values to program variables that satis ﬁ es the program constraint but violates the security constraint. This assignment of values is used to generate test cases to uncover the security vulnerability. One novelty of this method is a test model that uni ﬁ es program constraints and security constraints such that formal reasoning can be applied to detect vulnerabilities. Another nov-elty is attribute-based analysis that abstracts program variables and functions for effective and ef ﬁ cient symbolic execution. A tool named SecTAC has been implemented and applied to 14 benchmark programs and 3 moderate size open-source programs. The experiment shows that SecTAC quickly detects all reported vulnerabilities and 15 new ones that have not been detected previously. The merits of the proposed method are threefold. First, trace-based symbolic execution reduces the search space greatly as compared to conventional symbolic execution. Second, attribute-based analysis tracks more useful information about program variables and functions than previous methods, resulting in more effective detection of vulnerabilities. Third, it is ef ﬁ cient and effective as the experiment result indicates.
Finding Preference Profiles of Condorcet Dimension $k$ via SAT

Condorcet winning sets are a set-valued generalization of the well-known concept of a Condorcet winner. As supersets of Condorcet winning sets are always Condorcet winning sets themselves, an interesting property of preference profiles is the size of the smallest Condorcet winning set they admit. This smallest size is called the Condorcet dimension of a preference profile. Since little is known about profiles that have a certain Condorcet dimension, we show in this paper how the problem of finding a preference profile that has a given Condorcet dimension can be encoded as a satisfiability problem and solved by a SAT solver. Initial results include a minimal example of a preference profile of Condorcet dimension 3, improving previously known examples both in terms of the number of agents as well as alternatives. Due to the high complexity of such problems it remains open whether a preference profile of Condorcet dimension 4 exists.
Bid and ask prices as non-linear continuous time G-expectations based on distortions

Probability distortions for constructing nonlinear G-expectations for the bid and ask or lower and upper prices in continuous time are here extended to the direct use of measure distortions. Fairly generally measure distortions can be constructed as probability distortions applied to an exponential distribution function on the half line. The valuation methodologies are extended beyond contract valuation to the valuation of economic activities with infinite lives. Explicit computations illustrate the procedures for stock indices and insurance loss processes.
Sparcle: an evolutionary processor design for large-scale multiprocessors

The design of the Sparcle chip, which incorporates mechanisms required for massively parallel systems in a Sparc RISC core, is described. Coupled with a communications and memory management chip (CMMU) Sparcle allows a fast, 14-cycle context switch, an 8-cycle user-level message send, and fine-grain full/empty-bit synchronization. Sparcle's fine-grain computation, memory latency tolerance, and efficient message interface are discussed. The implementation of Sparcle as a CPU for the Alewife machine is described. <<ETX>>
Highly birefringent photonic crystal fiber with negative dispersion and its propagation

In this paper we show a comparison of two hexagonal geometries of Photonic Crystal Fiber (PCF). These geometries are different with respect to the area of circular air holes. The area of circular air holes is kept fixed for a structure and is kept increasing and decreasing alternatively with increase in number of rings for another structure. Both structure considered consists of seven rings. The structure with variation in area of air-holes shows negative dispersion for a wide range of wavelength. The birefringence of PCF structure with variation in area of circular air hole is high compare to that of other proposed PCF structure. The confinement Loss reported is of the order of 10 for both the PCF structure. But one of the structures with variation in area of circular air holes shows comparatively low confinement loss. The confinement factor was reported to be uniform for all the structures in this paper. The mode field pattern for both the structures appears to be a flower like shape with entire field being concentrated in the core and without leakage to cladding. The far field pattern (FFP) is also observed for both the structures.
Deep Sensing for Space-Time Doubly Selective Channels: When a Primary User Is Mobile and the Channel Is Flat Rayleigh Fading

The unrestrained mobility and dynamic spectrum sharing are considered as two key features of next-generation communications. In this paper, spectrum sensing in mobile scenarios is investigated, which faces still great challenges as both the mobile location of primary-user and fading channel will become time-variant. Such two uncertainties would arouse remarkable fluctuations in the strength of received signals, making most existing sensing schemes invalid. To cope with this exceptional difficulty, a novel paradigm, i.e., deep sensing, is designed, which estimates the time-dependent flat-fading gains and primary-user's mobile positions jointly, at the same time of detecting its emission status. All three hidden states involved by the space-time doubly selective scenario are taken into accounts. A unified dynamic state-space model is established to characterize the dynamic behaviors of unknown states, in which the time-dependent flat fading is modeled as a stochastic discrete-state Markov chain. A Bayesian approach, premised on a formulation of random finite set, is suggested to recursively estimate primary user's unknown states accompanying two others link uncertainties. In order to avoid the mis-tracking of the mobile positions, which is caused either by the incessant disappearance of primary-user or time-variant channels, an adaptive horizon expanding mechanism is also integrated. Numerical simulations validate the proposed scheme.
Functional MR Imaging in Patients with Carotid Artery Stenosis before and after Revascularization

BACKGROUND AND PURPOSE: Significant extracranial stenosis of the ICA is a known risk factor for future stroke and it has been shown that revascularization reduces the risk of future stroke. We applied BOLD fMRI in patients with carotid artery stenosis before and after CEA. Our purpose was to determine whether fMRI is able to demonstrate impaired CVR and to identify patient parameters that are associated with postoperative changes of cerebral hemodynamics. MATERIALS AND METHODS: Nineteen consecutive patients with symptomatic (n = 13) and asymptomatic (n = 6) stenosis of the ICA were prospectively recruited (male/female ratio = 16:3; age, 69 ± 8,1 years). fMRI using a simple bilateral motor task was performed immediately before and after CEA. RESULTS: Mean BOLD MSC was significantly increased postoperatively (MSC, 0.13 ± 0.66; P = 0.0002). Patients with a stenosis of <80% demonstrated an increase in MSC (MSC, 0.32 ± 0.59; P ≤ .0001). Patients with previous ischemic stroke showed a larger MSC than patients with TIAs (stroke: MSC, 0.55 ± 0.65; P ≤ .0001; TIA: MSC, 0.05 ± 0.26; P = 0.054). Patients older than 70 years had a significantly larger MSC following surgery (≤70 years: MSC, −0.01 ± 0.39; P = .429; >70 years: MSC, 0.29 ± 0.48; P ≤ .0001). CONCLUSIONS: BOLD fMRI can demonstrate changes in cerebral hemodynamics before and after CEA, indicative of an ameliorated CVR. This response is dependent on the age of the patient, the degree of preoperative stenosis, and the patient's symptoms.
The Generalized FITC Approximation

We present an efficient generalization of the sparse pseudo-input Gaussian process (SPGP) model developed by Snelson and Ghahramani [1], applying it to binary classification problems. By taking advantage of the SPGP prior covariance structure, we derive a numerically stable algorithm with O(NM2) training complexity—asymptotically the same as related sparse methods such as the informative vector machine [2], but which more faithfully represents the posterior. We present experimental results for several benchmark problems showing that in many cases this allows an exceptional degree of sparsity without compromising accuracy. Following [1], we locate pseudo-inputs by gradient ascent on the marginal likelihood, but exhibit occasions when this is likely to fail, for which we suggest alternative solutions.
A Survey on Lightweight Entity Authentication with Strong PUFs

Physically unclonable functions (PUFs) exploit the unavoidable manufacturing variations of an Integrated Circuit (IC). Their input-output behavior serves as a unique IC “fingerprint. ” Therefore, they have been envisioned as an IC authentication mechanism, in particular the subclass of so-called strong PUFs. The protocol proposals are typically accompanied with two PUF promises: lightweight and an increased resistance against physical attacks. In this work, we review 19 proposals in chronological order: from the original strong PUF proposal (2001) to the more complicated noise bifurcation and system of PUF proposals (2014). The assessment is aided by a unified notation and a transparent framework of PUF protocol requirements.