Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Task Allocation in Mobile Crowd Sensing with Individual Task Quality Assurance

Task allocation is a fundamental research issue in mobile crowd sensing. While earlier research focused mainly on single tasks, recent studies have started to investigate multi-task allocation, which considers the interdependency among multiple tasks. A common drawback shared by existing multi-task allocation approaches is that, although the overall utility of multiple tasks is optimized, the sensing quality of individual tasks may become poor as the number of tasks increases. To overcome this drawback, we re-define the multi-task allocation problem by introducing task-specific minimal sensing quality thresholds, with the objective of assigning an appropriate set of tasks to each worker such that the overall system utility is maximized. Our new problem also takes into account the maximum number of tasks allowed for each worker and the sensor availability of each mobile device. To solve this newly-defined problem, this paper proposes a novel multi-task allocation framework named MTasker. Different from previous approaches which start with an empty set and iteratively select task-worker pairs, MTasker adopts a descent greedy approach, where a quasi-optimal allocation plan is evolved by removing a set of task-worker pairs from the full set. Extensive evaluations based on real-world mobility traces show that MTasker outperforms the baseline methods under various settings, and our theoretical analysis proves that MTasker has a good approximation bound.
Fog of Social IoT: When the Fog Becomes Social

SIoT and FC are two stand-alone technological paradigms under the realm of the Future Internet. SIoT relies on the self-establishment and self-management of inter-thing social relationships, in order to guarantee scalability to large IoT networks composed of both human and non-human agents. FC extends cloud capabilities to the access network, in order to allow resource-poor IoT devices to support delay-sensitive applications. Motivated by these complementary features of the SIoT and FC models, in this article we propose their integration into the novel paradigm of the SoFT. Specifically, we provide the following three main contributions. After describing the SoFT paradigm, we discuss its introduction through a number of exemplary use cases. We describe the architecture and the main resource-management functions of the resulting virtualized SoFT technological platform. It merges the physical things at the IoT layer and their virtual clones at the Fog layer into a cyber-physical overlay network of social clones. As a proof-of-concept, we present the simulated performance of a small-scale SoFT prototype, and compare its energy-vs.-delay performance with the corresponding one of a state-of-the-art virtualization-free technological platform, which relies only on device-to-device (D2D) inter-thing communication.
On the Use of URLs and Hashtags in Age Prediction of Twitter Users

Social media data represent an important resource for behavioral analysis of the ageing population. This paper addresses the problem of age prediction from Twitter dataset, where the prediction issue is viewed as a classification task. For this purpose, an innovative model based on Convolutional Neural Network is devised. To this end, we rely on language-related features and social media specific metadata. More specifically, we introduce two features that have not been previously considered in the literature: the content of URLs and hashtags appearing in tweets. We also employ distributed representations of words and phrases present in tweets, hashtags and URLs, pre-trained on appropriate corpora in order to exploit their semantic information in age prediction. We show that our CNN-based classifier, when compared with an SVM baseline model, yields an improvement of 12.3% and 6.6% in the micro-averaged F1 score on the Dutch and English datasets, respectively.
How Well do CPU, GPU and Hybrid Graph Processing Frameworks Perform?

The importance of high-performance graph processing to solve big data problems targeting high-impact applications is greater than ever before. Recent graph processing frameworks target different hardware platforms (e.g., shared memory systems, accelerators such as GPUs, and distributed systems) and differ with respect to the programming model they adopt (e.g., based on linear algebra formulations of graph algorithms or enabling direct access to the graph structure). To better understand the impact of these choices, this paper, presents a comparative study of five state-of-the-art graph processing frameworks: two CPU-only frameworks - GraphMat and Galois, two GPU-based frameworks - Nvgraph and Gunrock; and Totem, a hybrid (CPU+GPU) framework. We use three popular graph algorithms (PageRank, Single Source Shortest Path, and Breadth-First Search), and massive scale graphs with up to billions of edges. Our evaluation focuses on three performance metrics: (i) execution time, (ii) scalability and (iii) energy consumption.
Optimal Subsampling for Large Sample Logistic Regression

ABSTRACT For massive data, the family of subsampling algorithms is popular to downsize the data volume and reduce computational burden. Existing studies focus on approximating the ordinary least-square estimate in linear regression, where statistical leverage scores are often used to define subsampling probabilities. In this article, we propose fast subsampling algorithms to efficiently approximate the maximum likelihood estimate in logistic regression. We first establish consistency and asymptotic normality of the estimator from a general subsampling algorithm, and then derive optimal subsampling probabilities that minimize the asymptotic mean squared error of the resultant estimator. An alternative minimization criterion is also proposed to further reduce the computational cost. The optimal subsampling probabilities depend on the full data estimate, so we develop a two-step algorithm to approximate the optimal subsampling procedure. This algorithm is computationally efficient and has a significant reduction in computing time compared to the full data approach. Consistency and asymptotic normality of the estimator from a two-step algorithm are also established. Synthetic and real datasets are used to evaluate the practical performance of the proposed method. Supplementary materials for this article are available online.
Optimizing Quality for Probabilistic Skyline Computation and Probabilistic Similarity Search

Probabilistic queries have been extensively explored to provide answers with confidence, in order to support the real-life applications struggling with uncertain data, such as sensor networks and data integration. However, the uncertainty of data may propagate, and thus, the results returned by probabilistic queries contain much noise, which <italic>degrades</italic> query quality significantly. In this paper, we propose an efficient optimization framework, termed as <inline-formula><tex-math notation="LaTeX">$\mathsf {QueryClean}$</tex-math><alternatives> <inline-graphic xlink:href="gao-ieq1-2805824.gif"/></alternatives></inline-formula>, for both probabilistic skyline computation and probabilistic similarity search. The goal of <inline-formula><tex-math notation="LaTeX">$\mathsf {QueryClean}$</tex-math><alternatives><inline-graphic xlink:href="gao-ieq2-2805824.gif"/></alternatives> </inline-formula> is to optimize query quality via selecting a group of uncertain objects to clean under limited resource available, where a joint-entropy based quality function is leveraged. We develop an efficient structure called <inline-formula><tex-math notation="LaTeX">$\mathsf {ASI}$</tex-math><alternatives> <inline-graphic xlink:href="gao-ieq3-2805824.gif"/></alternatives></inline-formula> to index the possible result sets of probabilistic queries, which helps to avoid many types of probabilistic query evaluations over a large number of the possible worlds for quality computation. Moreover, we present <italic>exact</italic> and <italic>approximate </italic> algorithms for the optimization problem, using two newly presented heuristics. Considerable experimental results on both real and synthetic data sets demonstrate the efficiency and scalability of our proposed framework <inline-formula><tex-math notation="LaTeX">$\mathsf {QueryClean}$</tex-math><alternatives> <inline-graphic xlink:href="gao-ieq4-2805824.gif"/></alternatives></inline-formula>.
Executive Functioning and Impairment in Emerging Adult College Students With ADHD Symptoms

Objective: To better understand current issues with adult ADHD assessment contributing to adult ADHD being under recognized and undertreated. Method: This study examined the relationship between symptoms, impairment, and executive functioning (EF) in a college sample. Results: Among individuals screening negative for ADHD, those higher in EF experienced significantly less impairment than those lower in EF. Executive functioning was shown to have a negative relationship with impairment while ADHD symptomology was shown to have a positive relationship with impairment. In addition, impairment was significantly predicted by ADHD symptom level and EF, and there was a significant interaction between EF and ADHD symptom level in predicting impairment. However, high EF did not significantly protect against impairment in individuals reporting a significant level of ADHD symptoms. Conclusion: Further research is needed to clarify the relationships between these variables in emerging adult college students.
A Microstrip Magnetic Dipole Yagi–Uda Antenna Employing Vertical I-Shaped Resonators as Parasitic Elements

A low-profile planar Yagi–Uda antenna with exact endfire radiation and vertical polarization is presented in this paper. The concept is the combination of an aperture element and a novel vertical I-shaped resonator (VISR). The aperture acts as the driven antenna, which can be equivalent of a virtual magnetic dipole. The VISR array constructed with VISR unit cell is employed as a director or a reflector element depending on the resonant frequency of the unit cell, which consists of two identical rectangular metal conductors printed on both sides of a substrate and their electrical connection using via holes. Two prototype antennas are then designed, fabricated, and measured to test the validity of the operating principle. Antenna I with one director and one reflector achieves an endfire realized gain of 9.9–10.3 dBi and a front-to-back ratio (FBR) of more than 17.4 dB across 6.36–6.70 GHz. With four directors and one reflector, an endfire realized gain of 12.9–13.6 dBi and an FBR of more than 17.9 dB are accomplished by antenna II across 6.38–6.71 GHz. Moreover, a sensitivity analysis is implemented for the proposed antennas. Simulated results show that the standard PCB process is a reliable method for antenna fabrication.
Auto-tuning Streamed Applications on Intel Xeon Phi

Many-core accelerators, as represented by the XeonPhi coprocessors and GPGPUs, allow software to exploit spatial and temporal sharing of computing resources to improve the overall system performance. To unlock this performance potential requires software to effectively partition the hardware resource to maximize the overlap between host-device communication and accelerator computation, and to match the granularity of task parallelism to the resource partition. However, determining the right resource partition and task parallelism on a per program, per dataset basis is challenging. This is because the number of possible solutions is huge, and the benefit of choosing the right solution may be large, but mistakes can seriously hurt the performance. In this paper, we present an automatic approach to determine the hardware resource partition and the task granularity for any given streamed application, targeting the Intel XeonPhi architecture. Instead of hand-crafting the heuristic for which the process will have to repeat for each hardware generation, we employ machine learning techniques to automatically learn it. We achieve this by first learning a predictive model offline using training programs; we then use the learned model to predict the resource partition and task granularity for any unseen programs at runtime. We apply our approach to 23 representative parallel applications and evaluate it on a CPU-XeonPhi mixed heterogenous many-core platform. Our approach achieves, on average, a 1.6x (upto 5.6x) speedup, which translates to 94.5% of the performance delivered by a theoretically perfect predictor.
A Collaborative Method for Route Discovery Using Taxi Drivers’ Experience and Preferences

This paper presents a collaborative route discovery method that leverages the experience and preferences of taxi drivers in urban areas. The proposed method is mainly comprised of two phases: collaborative preference discovery (CPD) and intelligent driver network generation (IDNG). In the first phase, given an origin-destination (O-D) pair and provided that the cluster is a road segment set within a time-reachable range, we propose CPD which involves cluster-to-cluster retrieval to capture the top-k routes that are not only frequently traversed by taxis but also neighboring to the O-D pair. In the second phase, to support route computation, an IDNG algorithm is devised to generate an experiential graph for each specific O-D pair. In empirical studies, using the period-based experiential route database, sensitivity analysis is employed to select optimal parameters of intelligent driver networks. The results demonstrate that the routes recommended by our collaborative method are much more reliable than those of the shortest-path method with respect to the variance of travel time. Moreover, the recommended routes are traversed more frequently than those of the fastest-path and the shortest-path methods, while the travel time and route lengths of our routes are approximately equal to those of the conventional methods.
The brain of the tree pangolin (Manis tricuspis). II. The olfactory system

This study employed a range of neuroanatomical stains to determine the organization of the main and accessory olfactory systems within the brain of the tree pangolin. The tree pangolin has a typically mammalian olfactory system, but minor variations were observed. The main olfactory system is comprised of the layered main olfactory bulb (MOB), the anterior olfactory nucleus (AON), the rostral olfactory cortex (including the taenia tecta, anterior hippocampal continuation and induseum griseum), the olfactory tubercle (Tu), the lateral olfactory tract (lot) and the olfactory limb of the anterior commissure, the nucleus of the lateral olfactory tract (NLOT), the piriform cortex (PIR) and a typically mammalian rostral migratory stream (RMS). The accessory olfactory system included the layered accessory olfactory bulb (AOB) and the nucleus of the accessory olfactory tract (NAOT). Volumetric analysis of the relative size of the MOB and PIR indicate that the tree pangolin has an olfactory system that occupies a proportion of the brain typical for the majority of mammals. Within the MOB, the glomeruli of the tree pangolin, at 200 μm diameter, are larger than observed in most other mammalian species, and the MOB lacks a distinct internal plexiform layer. In addition, the laminate appearance of the NLOT was not observed in the tree pangolin. The accessory olfactory system appears to lack the posterior compartment of the accessory olfactory bulb. These observations are contextualized in relation to olfactory‐mediated behaviors in pangolins.
Employing proteomics in the study of antigen presentation: an update

ABSTRACT Introduction: Our immune system discriminates self from non-self by examining the peptide cargo of human leukocyte antigen (HLA) molecules displayed on the cell surface. Successful recognition of HLA-bound non-self peptides can induce T cell responses leading to, for example, the destruction of infected cells. Today, largely due to advances in technology, we have an unprecedented capability to identify the nature of these presented peptides and unravel the true complexity of antigen presentation. Areas covered: In addition to conventional linear peptides, HLA molecules also present post-translationally modified sequences comprising a wealth of chemical and structural modifications, including a novel class of noncontiguous spliced peptides. This review focuses on these emerging themes in antigen presentation and how mass spectrometry in particular has contributed to a new view of the antigenic landscape that is presented to the immune system. Expert Commentary: Advances in the sensitivity of mass spectrometers and use of hybrid fragmentation technologies will provide more information-rich spectra of HLA bound peptides leading to more definitive identification of T cell epitopes. Coupled with improvements in sample preparation and new informatics workflows, studies will access novel classes of peptide antigen and allow interrogation of rare and clinically relevant samples.
Miniature Gas Sensor and Sensor Array With Single- and Dual-Mode RF Dielectric Resonators

RF resonant polymer-coated sensors are a proven effective approach to enhance the sensitivity of polymeric sensing materials. However, the level of sensitivity improvement offered by an RF resonant sensor is directly proportional to the quality factor (Q) of the resonator—the larger the Q-factor, the higher the sensitivity enhancement is. This paper first presents a miniature polymer-based dielectric resonant (DR) sensor design operated at its fundamental mode (TEH) at 24 GHz demonstrating a measured Q-factor of 3820. The sensor is coated with a (per)nigraniline-based conductive sensing material for detections of acetone and isopropanol volatile organic compounds and has a small footprint of <inline-formula> <tex-math notation="LaTeX">$4.5\,\,\text {mm} \times 4.5$ </tex-math></inline-formula> mm. The sensor has demonstrated a low-ppm detection capability for acetone and isopropanol gases with a response onset time less than 15 s. This paper further demonstrates a novel airborne carcinogenic agent sensor array implemented with a single dielectric resonator operating in dual mode (HEH) at 27 GHz. It is functionalized by placing two carcinogen sensing materials—polyhydroxyethyl-methacrylate (PHM) and fluoroalcohol polysiloxanes (FAPS)—at the optimal geometrical locations on the DR, such that the polymers would independently affect the resonant frequencies of the two orthogonal HEH degenerate modes. By monitoring <inline-formula> <tex-math notation="LaTeX">$\Delta f_{0}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\Delta S_{11}$ </tex-math></inline-formula> near <inline-formula> <tex-math notation="LaTeX">$f_{0,\mathrm {HEH1}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$f_{0,\mathrm {HEH2}}$ </tex-math></inline-formula>, both the concentrations and the response signatures of the airborne carcinogenic agents can be detected. The measurement results indicate that the HEH-mode two-sensor array carrying PHM and FAPS is successful in identifying and differentiating two carcinogenic agents—dioxane and benzene—and their respective concentrations.
Interfilament interaction between IMPDH and CTPS cytoophidia

Inosine monophosphate dehydrogenase (IMPDH) and cytidine triphosphate synthase (CTPS) are two metabolic enzymes that perform rate‐limiting steps in the de novo synthesis of purine and pyrimidine nucleotides, respectively. It has been shown that IMPDH and CTPS can comprise a filamentous macrostructure termed the cytoophidium, which may play a role in regulation of their catalytic activity. Although these two proteins may colocalise in the same cytoophidium, how they associate with one another is still elusive. As reported herein, we established a model HeLa cell line coexpressing OFP‐tagged IMPDH2 and GFP‐tagged CTPS1 and recorded the assembly, disassembly and movement of the cytoophidium in live cells. Moreover, by using super‐resolution confocal imaging, we demonstrate how IMPDH‐ and CTPS‐based filaments are aligned or intertwined in the mixed cytoophidium. Collectively, our findings provide a panorama of cytoophidium dynamics and suggest that IMPDH and CTPS cytoophidia may coordinate by interfilament interaction.
Decoding the transcriptional programs activated by psychotropic drugs in the brain

Analysis of drug‐induced gene expression in the brain has long held the promise of revealing the molecular mechanisms of drug actions as well as predicting their long‐term clinical efficacy. However, despite some successes, this promise has yet to be fulfilled. Here, we present an overview of the current state of understanding of drug‐induced gene expression in the brain and consider the obstacles to achieving a robust prediction of the properties of psychoactive compounds based on gene expression profiles. We begin with a comprehensive overview of the mechanisms controlling drug‐inducible transcription and the complexity resulting from expression of noncoding RNAs and alternative gene isoforms. Particular interest is placed on studies that examine the associations within drug classes with regard to the effects on gene transcription, alterations in cell signaling and neuropharmacological drug properties. While the ability of gene expression signatures to distinguish specific clinical classes of psychotropic and addictive drugs remains unclear, some reports show that under specific constraints, drug properties can be predicted based on gene expression. Such signatures offer a simple and effective way to classify psychotropic drugs and screen novel psychoactive compounds. Finally, we note that the amount of data regarding molecular programs activated in the brain by drug treatment has grown exponentially in recent years and that future advances may therefore come in large part from integrating the currently available high‐throughput data sets.
JustOrthologs: a fast, accurate and user-friendly ortholog identification algorithm

Motivation: Orthologous gene identification is fundamental to all aspects of biology. For example, ortholog identification between species can provide functional insights for genes of unknown function and is a necessary step in phylogenetic inference. Currently, most ortholog identification algorithms require all‐versus‐all BLAST comparisons, which are time‐consuming and memory intensive. Results: In contrast to existing approaches, JustOrthologs exploits the conservation of gene structure by using the lengths of coding sequence regions and dinucleotide percentages to identify orthologs. In comparison to OrthoMCL, OMA and OrthoFinder, JustOrthologs decreases ortholog identification runtime by more than 96% and achieves comparable precision and recall scores. The computational speedup allowed us to conduct pairwise comparisons of 1197 complete genomes (780 eukaryotes and 417 archaea). We confirmed gene annotations for 384 120 genes, grouped 1 675 415 genes in previously unreported ortholog groups, and identified 51 429 potentially mislabeled genes across 622 843 ortholog groups. Availability and implementation: JustOrthologs is an open source collaborative software package available in the GitHub repository: https://github.com/ridgelab/JustOrthologs/. All test FASTA files used for comparisons are freely available at https://github.com/ridgelab/JustOrthologs/comparisonFastaFiles/. Reference genomes used in this work are available for download from the NCBI repository: ftp://ftp.ncbi. nih.gov/genomes/. Supplementary information: Supplementary data are available at Bioinformatics online.