Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling of the Latent Embedding of Music using Deep Neural Network

While both the data volume and heterogeneity of the digital music content is huge, it has become increasingly important and convenient to build a recommendation or search system to facilitate surfacing these content to the user or consumer community. Most of the recommendation models fall into two primary species, collaborative filtering based and content based approaches. Variants of instantiations of collaborative filtering approach suffer from the common issues of so called"cold start"and"long tail"problems where there is not much user interaction data to reveal user opinions or affinities on the content and also the distortion towards the popular content. Content-based approaches are sometimes limited by the richness of the available content data resulting in a heavily biased and coarse recommendation result. In recent years, the deep neural network has enjoyed a great success in large-scale image and video recognitions. In this paper, we propose and experiment using deep convolutional neural network to imitate how human brain processes hierarchical structures in the auditory signals, such as music, speech, etc., at various timescales. This approach can be used to discover the latent factor models of the music based upon acoustic hyper-images that are extracted from the raw audio waves of music. These latent embeddings can be used either as features to feed to subsequent models, such as collaborative filtering, or to build similarity metrics between songs, or to classify music based on the labels for training such as genre, mood, sentiment, etc.
MRI Visible Drug Eluting Magnetic Microspheres for Transcatheter Intra-Arterial Delivery to Liver Tumors

Magnetic resonance imaging (MRI)-visible amonafide-eluting alginate microspheres were developed for targeted arterial-infusion chemotherapy. These alginate microspheres were synthesized using a highly efficient microfluidic gelation process. The microspheres included magnetic clusters formed by USPIO nanoparticles to permit MRI and a sustained drug-release profile. The biocompatibility, MR imaging properties and amonafide release kinetics of these microspheres were investigated during in vitro studies. A xenograft rodent model was used to demonstrate the feasibility to deliver these microspheres to liver tumors using hepatic transcatheter intra-arterial infusions and potential to visualize the intra-hepatic delivery of these microspheres to both liver tumor and normal tissues with MRI immediately after infusion. This approach offer the potential for catheter-directed drug delivery to liver tumors for reduced systemic toxicity and superior therapeutic outcomes.
Using data mining and fingerprinting extension with device orientation information for WLAN efficient indoor location estimation

Research based on indoor location systems has recently been developed due to growing interest in location-aware services to be implemented in light mobile devices. Most of this work is based on received signal strength (RSS) from access points. However, a major drawback from using RSS is its variability due to indoor multipath effect caused by reflection, diffraction and scattering of signal propagation. Therefore, different device orientations in a fixed location provide significant and different RSS values. In this paper, we propose to extend fingerprinting with device orientation information. Implementation of our location system is based on data mining techniques employing decision tree algorithms. Experimental results demonstrate that using RSS samples with the device orientation information improves the location estimation with high accuracy.
Antisense inhibition of a pectate lyase gene supports a role for pectin depolymerization in strawberry fruit softening

Cell wall disassembly in softening fruits is a complex process involving the cumulative action of many families of wall-modifying proteins on interconnected polysaccharide matrices. One strategy to elucidate the in vivo substrates of specific enzymes and their relative importance and contribution to wall modification is to suppress their expression in transgenic fruit. It has been reported previously that inhibiting the expression of pectate lyase genes by antisense technology in strawberry (Fragaria×ananassa Duch.) fruit resulted in prolonged fruit firmness. This suggested that pectin depolymerization might make a more important contribution to strawberry fruit softening than is often stated. In this present study, three independent transgenic lines were identified exhibiting a greater than 90% reduction in pectate lyase transcript abundance. Analyses of sequential cell wall extracts from the transgenic and control fruit collectively showed clear quantitative and qualitative differences in the extractability and molecular masses of populations of pectin polymers. Wall extracts from transgenic fruits showed a reduction in pectin solubility and decreased depolymerization of more tightly bound polyuronides. Additional patterns of differential extraction of other wall-associated pectin subclasses were apparent, particularly in the sodium carbonate- and chelator-soluble polymers. In addition, microscopic studies revealed that the typical ripening-associated loss of cell–cell adhesion was substantially reduced in the transgenic fruits. These results indicate that pectate lyase plays an important degradative role in the primary wall and middle lamella in ripening strawberry fruit, and should be included in synergistic models of cell wall disassembly.
Sketch Based Image Retrieval

Sketch based image retrieval is a task that has been explored a lot recently as an alternative method for image retrieval. We develop this task on The Sketchy Database, where we use Siamese and Triplet network to perform sketch based image retrieval. We employ deep residual learning network as the constituent network in the Siamese and Triplet architecture and use new data augmentation techniques for the task. Recent success of deep residual network (ResNet) suggests that it performs better than the previous architectures.
Evaluating and Improving Modern Variable and Revision Ordering Strategies in CSPs

A key factor that can dramatically reduce the search space during constraint solving is the criterion under which the variable to be instantiated next is selected. For this purpose numerous heuristics have been proposed. Some of the best of such heuristics exploit information about failures gathered throughout search and recorded in the form of constraint weights, while others measure the importance of variable assignments in reducing the search space. In this work we experimentally evaluate the most recent and powerful variable ordering heuristics, and new variants of them, over a wide range of benchmarks. Results demonstrate that heuristics based on failures are in general more efficient. Based on this, we then derive new revision ordering heuristics that exploit recorded failures to efficiently order the propagation list when arc consistency is maintained during search. Interestingly, in addition to reducing the number of constraint checks and list operations, these heuristics are also able to cut down the size of the explored search tree.
Survey Non-Response and Unemployment Duration

Social surveys are often used to estimate unemployment duration distributions. Survey non-response may then cause a bias. We study this using a unique dataset that combines survey information of individual workers with administrative records of the same workers. The latter provide information on unemployment durations and personal characteristics of all survey respondents and non-respondents. We develop a method to empirically distinguish between two explanations for a bias in results based on only survey data: (1) selectivity due to related unobserved determinants of unemployment durations and non-response, and (2) a causal effect of a job exit on non-response. The latter may occur even in fully homogeneous populations. The methodology exploits variation in the timing of the duration outcome relative to the survey moment. The results show evidence for both explanations. We discuss implications for standard methods to deal with non-response bias.
Fuzzy logic based particle filter for tracking a maneuverable target

In this paper we propose a new fuzzy logic-based particle filter (FLPF) algorithm for tracking a maneuvering target. The nonlinear system which is comprised of two-input and single-output are presented by fuzzy relational equations. Each of the fuzzy relational equations is expressed in a canonical-rule based form. The dynamics of the maneuvering target are modeled by multiple switching (jump Markov) systems. We assume that the target follows one-out-of-three dynamic behavior model at any time in the observation period: constant velocity (CV) motion model, clockwise coordinated turn (CCT) model, and anticlockwise coordinated turn (ACT) model. The time-varying deviation between actual and predicted positions is inferred by a two-input single-output fuzzy relation. An example is included for visualizing the effectiveness of the proposed algorithm. For comparison purposes, we simulated a conventional sequential importance sampling (SIS) algorithm. Simulation results showed that the FLPF has better tracking performance compared to the SIS.
Do we need specific quality models for multi-agent systems? Toward using the ISO/IEC 25010 quality model for MAS

The quality assurance of software is one of the most important purposes of software engineering. However, the quality concept is undergone to subjective interpretations. Consequently, several models are proposed to understand and evaluate the software quality. The ISO/IEC 25010 is the new international standard quality model. Being standard increases the applicability of such model for all software products. Knowing that the multi-agent systems are one of the most applied software paradigms; we target in this paper the applicability of the ISO/IEC 25010 to agent-based software.
Dynamic causal modeling of load‐dependent modulation of effective connectivity within the verbal working memory network

Neuroimaging studies have consistently shown that working memory (WM) tasks engage a distributed neural network that primarily includes the dorsolateral prefrontal cortex, the parietal cortex, and the anterior cingulate cortex. The current challenge is to provide a mechanistic account of the changes observed in regional activity. To achieve this, we characterized neuroplastic responses in effective connectivity between these regions at increasing WM loads using dynamic causal modeling of functional magnetic resonance imaging data obtained from healthy individuals during a verbal n‐back task. Our data demonstrate that increasing memory load was associated with (a) right‐hemisphere dominance, (b) increasing forward (i.e., posterior to anterior) effective connectivity within the WM network, and (c) reduction in individual variability in WM network architecture resulting in the right‐hemisphere forward model reaching an exceedance probability of 99% in the most demanding condition. Our results provide direct empirical support that task difficulty, in our case WM load, is a significant moderator of short‐term plasticity, complementing existing theories of task‐related reduction in variability in neural networks. Hum Brain Mapp 35:3025–3035, 2014. © 2013 Wiley Periodicals, Inc.
Online bandit convex optimization over a network

In this paper we study the online bandit optimization over an undirected and connected network. In this setting, a network consists of multiple interacting agents plays a repeated game of T rounds with the dynamic environment. The objective function is a sum of all the agents' local cost functions that change over time, due to the uncertainties in the environment; moreover, each agent can only observe the value of each cost function at a single point at each round. We propose an efficient algorithm for distributed online bandit optimization with strongly convex cost functions and obtain a bound of Õ(T2/3) on the expected regret.
CD103 Is a Marker for Alloantigen-Induced Regulatory CD8+ T Cells

The αEβ7 integrin CD103 may direct lymphocytes to its ligand E-cadherin. CD103 is expressed on T cells in lung and gut and on allograft-infiltrating T cells. Moreover, recent studies have documented expression of CD103 on CD4+ regulatory T cells. Approximately 4% of circulating CD8+ T cells bear the CD103 molecule. In this study, we show that the absence or presence of CD103 was a stable trait when purified CD103− and CD103+CD8+ T cell subsets were stimulated with a combination of CD3 and CD28 mAbs. In contrast, allostimulation induced CD103 expression on ∼25% of purified CD103−CD8+ T cells. Expression of CD103 on alloreactive cells was found to be augmented by IL-4, IL-10, or TGF-β and decreased by addition of IL-12 to MLCs. The alloantigen-induced CD103+CD8+ T cell population appeared to be polyclonal and retained CD103 expression after restimulation. Markedly, in vitro-expanded CD103+CD8+ T cells had low proliferative and cytotoxic capacity, yet produced considerable amounts of IL-10. Strikingly, they potently suppressed T cell proliferation in MLC via a cell-cell contact-dependent mechanism. Thus, human alloantigen-induced CD103+CD8+ T cells possess functional features of regulatory T cells.
Infopipes: the ISL/ISG implementation evaluation

We provide a performance comparison of generated Infopipes that have been translated and the Spi/XlP variant of Infopipe specification into executable code. Infopipes are abstractions to support information flow applications. These tools are evaluated through a realistic application: a continuous image streaming program. We implement the application in C and compare its performance to both a hand-written application and one that uses SunRPC.
Elephant shark (Callorhinchus milii) provides insights into the evolution of Hox gene clusters in gnathostomes

We have sequenced and analyzed Hox gene clusters from elephant shark, a holocephalian cartilaginous fish. Elephant shark possesses 4 Hox clusters with 45 Hox genes that include orthologs for a higher number of ancient gnathostome Hox genes than the 4 clusters in tetrapods and the supernumerary clusters in teleost fishes. Phylogenetic analysis of elephant shark Hox genes from 7 paralogous groups that contain all of the 4 members indicated an ((AB)(CD)) topology for the order of Hox cluster duplication, providing support for the 2R hypothesis (i.e., 2 rounds of whole-genome duplication during the early evolution of vertebrates). Comparisons of noncoding sequences of the elephant shark and human Hox clusters have identified a large number of conserved noncoding elements (CNEs), which represent putative cis-regulatory elements that may be involved in the regulation of Hox genes. Interestingly, in fugu more than 50% of these ancient CNEs have diverged beyond recognition in the duplicated (HoxA, HoxB, and HoxD) as well as the singleton (HoxC) Hox clusters. Furthermore, the b-paralogs of the duplicated fugu Hox clusters are virtually devoid of unique ancient CNEs. In contrast to fugu Hox clusters, elephant shark and human Hox clusters have lost fewer ancient CNEs. If these ancient CNEs are indeed enhancers directing tissue-specific expression of Hox genes, divergence of their sequences in vertebrate lineages might have led to altered expression patterns and presumably the functions of their associated Hox genes.
The teacher in the loop: customizing MMLA in blended CSCL scenarios

In across-spaces learning scenarios, evidence needs to be gathered from different spaces to obtain a more complete view of the teaching and learning processes. Multimodal learning analytics (MMLA) enables us to gather data from physical spaces, enriching the evidence coming from digital ones. However, blended learning scenarios are heterogeneous, and the varying data sources available in each particular context can condition the accuracy, relevance, and actionability of the analyses. To avoid this problem, in this paper we propose to involve teachers in customizing the LA solution they will use, adapting it to their particular blended learning context (e.g., identifying relevant data sources and metrics). Preliminary results from two studies in blended CSCL settings show an improvement in the accuracy of the resulting MMLA solution. Although this kind of approach requires additional time from teachers, participants reported increased levels of relevance, novelty, understanding and actionability of the results.
Constraint Based Dimension Correlation and Distance Divergence for Clustering High-Dimensional Data

Clusters are hidden in subspaces of high dimensional data, i.e., only a subset of features is relevant for each cluster. Subspace clustering is challenging since the search for the relevant features of each cluster and the detection of the final clusters are circular dependent and should be solved simultaneously. In this paper, we point out that feature correlation and distance divergence are important to subspace clustering, but both have not been considered in previous works. Feature correlation groups correlated features independently thus helps to reduce the search space for the relevant features search problem. Distance divergence distinguishes distances on different dimensions and helps to find the final clusters accurately. We tackle the two problems with the aid of a small amount domain knowledge in the form of must-links and cannot-links. We then devise a semi-supervised subspace clustering algorithm CDCDD. CDCDD integrates our solutions of the feature correlation and distance divergence problems, and uses an adaptive dimension voting scheme, which is derived from a previous unsupervised subspace clustering algorithm FINDIT. Experimental results on both synthetic data sets and real data sets show that the proposed CDCDD algorithm outperforms FINDIT in terms of accuracy, and outperforms the other constraint based algorithm SCMINER in terms of both accuracy and efficiency.
Design guidelines for composite patches bonded to cracked aluminum substrates

Design guidelines are proposed for boron fiber-reinforced epoxy composite patches bonded to cracked aluminum substrates. Stress analysis was performed on the composite patch bonded to an uncracked aluminum substrate. The load transfer via the adhesive and the stress levels in the aluminum substrate and the patch were determined. The local stress and stress distribution in the repaired V-shape side cracked substrates were obtained. The effects of crack length and patch geometries including the length and number of plies on the failure behavior of the repair were identified. The results were validated by static tensile tests. It was found that the theoretical calculations were in agreement with the experimental results. This indicates that the proposed analysis can be used as design guidelines for composite repairs of cracked structures. These guidelines coupled with a simple surface preparation technique developed in this work might aid composite patch installation in service.
Understanding the Impact of Inter-Thread Cache Interference on ILP in Modern SMT Processors

Simultaneous Multithreading (SMT) has emerged as an effective method of increasing utilization of resources in modern super-scalar processors. SMT processors increase instruction-level parallelism (ILP) and resource utilization by simultaneously executing instructions from multiple independent threads. Although simultaneously sharing resources beneﬁts system throughput, co-scheduled threads often aggressively compete for limited resources, namely the cache memory system. While compiler and hardware technologies have been traditionally examined for their effect on ILP, in the context of SMT machines, the operating system also has a substantial inﬂuence on system performance. By making informed scheduling decisions, the operating system can limit the amount of contention in the memory hierarchy between threads and reduce the impact of multiple threads simultaneously accessing the cache system. This paper explores the design of a novel ﬁne-grained hardware cache monitoring system in an SMT-based processor that enables improved operating system scheduling and recaptures parallelism by mitigating interference.
Learning to Drive a Real Car in 20 Minutes

The paper describes our first experiments on reinforcement learning to steer a real robot car. The applied method, neural fitted Q iteration (NFQ) is purely data-driven based on data directly collected from real-life experiments, i.e. no transition model and no simulation is used. The RL approach is based on learning a neural Q value function, which means that no prior selection of the structure of the control law is required. We demonstrate, that the controller is able to learn a steering task in less than 20 minutes directly on the real car. We consider this as an important step towards the competitive application of neural Q function based RL methods in real-life environments.
Barriers to healthcare seeking among medical students: a cross sectional study from South India

Objective To determine the barriers to seeking help from healthcare services reported by medical students at an Indian medical school, and to compare the barriers for using physical health services with those for using mental health services. Methods In 2014, we invited all medical students across the nine semesters of training at a government medical college in Puducherry, India, to complete a 28 item questionnaire about perceived barriers to seeking healthcare services. The questionnaire enquired about barriers to using physical and mental healthcare services separately. Results Completed responses were available from 461 of 509 students (response rate 90.6%). Stigma, confidentiality issues, lack of awareness about where to seek help and fear of unwanted intervention were more commonly reported for mental healthcare seeking (OR 4.21, 4.01, 3.19 and 2.43, respectively), while issues relating to cost, lack of time and fear of side effects were observed less frequently (OR 0.45, 0.46 and 0.57, respectively) compared with physical healthcare seeking. In comparison with physical health, students were more indifferent to their mental health issues and preferred self-diagnosis and informal consultations over formal documented care. Conclusions Barriers to seeking healthcare services differ for mental and physical health issues. Many system based barriers such as stigma, confidentiality issues and poor awareness of service location were reported by students. Institutional programmes should use this information for improving the usage, satisfaction and effectiveness of healthcare delivery systems for medical students.