Miracle-dz/UniFAR-FTUs huggingface dataset:
Rumor Source Obfuscation on Irregular Trees

Anonymous messaging applications have recently gained popularity as a means for sharing opinions without fear of judgment or repercussion. Messages in these applications propagate anonymously (without authorship metadata) over a network that is typically defined by social connections or physical proximity. However, recent advances in rumor source detection show that the source of such an anonymous message can be inferred by statistical inference attacks. Adaptive diffusion was recently proposed as a solution that achieves optimal source obfuscation over regular trees. However, in real social networks, node degrees differ from node to node, and adaptive diffusion can be significantly sub-optimal. This gap increases as the degrees become more irregular. In order to quantify this gap, we model the underlying network as coming from standard branching processes with i.i. d. degree distributions. Building upon the analysis techniques from branching processes, we give an analytical characterization of the dependence between the probability of detection achieved by adaptive diffusion and the degree distribution. Further, this analysis provides a key insight: passing a rumor to a friend who has many friends makes the source more ambiguous. This leads to a new family of protocols that we call Preferential Attachment Adaptive Diffusion (PAAD). When messages are propagated according to PAAD, we give both the MAP estimator for finding the source and also an analysis of the probability of detection achieved by this adversary. The analytical results are not directly comparable, since the adversary's observed information has a different distribution under adaptive diffusion than under PAAD. Instead, we present results from numerical experiments that suggest that PAAD achieves a lower probability of detection, at the cost of increased communication for coordination.
Learning Wake-Sleep Recurrent Attention Models

Despite their success, convolutional neural networks are computationally expensive because they must examine all image locations. Stochastic attention-based models have been shown to improve computational efficiency at test time, but they remain difficult to train because of intractable posterior inference and high variance in the stochastic gradient estimates. Borrowing techniques from the literature on training deep generative models, we present the Wake-Sleep Recurrent Attention Model, a method for training stochastic attention networks which improves posterior inference and which reduces the variability in the stochastic gradients. We show that our method can greatly speed up the training time for stochastic attention networks in the domains of image classification and caption generation.
Rechargeable Metal–Air Proton‐Exchange Membrane Batteries for Renewable Energy Storage

Abstract Rechargeable proton‐exchange membrane batteries that employ organic chemical hydrides as hydrogen‐storage media have the potential to serve as next‐generation power sources; however, significant challenges remain regarding the improvement of the reversible hydrogen‐storage capacity. Here, we address this challenge through the use of metal‐ion redox couples as energy carriers for battery operation. Carbon, with a suitable degree of crystallinity and surface oxygenation, was used as an effective anode material for the metal redox reactions. A Sn0.9In0. 1P2O7‐based electrolyte membrane allowed no crossover of vanadium ions through the membrane. The V4+/V3+, V3+/V2+, and Sn4+/Sn2+ redox reactions took place at a more positive potential than that for hydrogen reduction, so that undesired hydrogen production could be avoided. The resulting electrical capacity reached 306 and 258 mAh g−1 for VOSO4 and SnSO4, respectively, and remained at 76 and 91 % of their respective initial values after 50 cycles.
Selecting Very Soft X-Ray Sources in External Galaxies: Luminous Supersoft X-ray Sources and Quasisoft Sources

We introduce a procedure to identify very soft X-ray sources (VSSs) in external galaxies. Our immediate goal was to formulate a systematic procedure to identify luminous supersoft X-ray sources (SSSs), so as to allow comparisons among galaxies and to study environmental effects. The focus of this paper is on the design of the selection algorithm and on its application to simulated data. In the companion paper we test it by applying it to sources discovered through Chandra observations of 4 galaxies. We find that, in its application to both simulated and real data, our procedure also selects somewhat harder sources, which we call quasisoft. Whereas values of kT for SSSs are typically tens of eV, some quasisoft sources (QSSs) may have kT as high as ~250-300 eV. The dominant spectral component of other QSSs may be as soft as SSS spectra, but the spectra may also include a low-luminosity harder component. We sketch physical models for both supersoft and quasisoft sources. Some SSSs are likely to be accreting white dwarfs; some of these may be progenitors of Type Ia supernovae. Most QSSs may be too hot to be white dwarfs. They, together with a subset of SSSs, may be neutron stars or, perhaps most likely, accreting intermediate-mass black holes.
A SIFT descriptor with local kernel color histograms

SIFT (Scale Invariant Feature Transform) has proved to be the most robust local invariant feature descriptor in object recognition and matching. Being designed mainly for the gray images, SIFT shows its vulnerability when deal with color images. To overcome this problem and increase the descriptor's distinctiveness, we introduce a new descriptor, a combination of the SIFT approach and the improved local kernel color histograms, which shows a better performance than the original SIFT through experiments. Moreover, the experiments results show that the radio of correct matches increase and the mismatch radio remain constant simultaneously.
Induction of Apoptosis by the Nonstructural Protein 4 and 10 of Porcine Reproductive and Respiratory Syndrome Virus

Infection by most viruses triggers apoptosis in host cells, and viruses manipulate this cell response to promote viral replication, virus spread, and cell killing. Porcine reproductive and respiratory syndrome virus (PRRSV) has been shown to induce apoptosis both in vitro and in vivo, while the regulatory roles of PRRSV-encoded products in apoptosis are not fully understood. In the present study, we first showed a biphasic apoptosis regulation by a highly pathogenic PRRSV strain JXwn06. It was indicated that PRRSV infection delays apoptosis at early infection but activates apoptosis at late infection in MARC-145 cells. In PRRSV-infected MARC-145 cells, procaspase-8, -9 and -12 were activated at late infection, demonstrating the involvements of death receptor pathway, mitochondrial pathway and endoplasmic reticulum (ER) stress pathway in inducing apoptosis. PRRSV was also shown to induce a similar apoptosis process in pulmonary alveolar macrophages (PAMs) with an early initiation. Next, the PRRSV-encoded apoptosis inducers were screened, indicating that the nonstructural protein (Nsp) 4 and Nsp10 of PRRSV are pro-apoptotic. In the presence of Nsp4, it was confirmed that procaspase-8, -9 and -12 were cleaved, and Nsp4 facilitates the cleavage of procaspase-9 by activating B-cell lymphoma 2 interacting mediator of cell death (Bim), a pro-apoptotic protein. In addition, Nsp4 was shown to induce the degradation of an anti-apoptotic protein, B-cell lymphoma-extra large (Bcl-xL). Nsp10 was shown to activate procaspase-8 and -9 but procaspase-12 and to upregulate the expression of BH3-only pro-apoptotic protein BH3 interacting-domain death agonist (Bid) and its active form, truncated Bid (tBid). Clearly, the participation of both activated caspase-8 and Bid is required for Nsp10-induced apoptosis, indicating a crosstalk between extrinsic- and mitochondria-dependent pathways. Together, our findings suggest that PRRSV infection regulates apoptosis in a two-phase manner and activates all three apoptotic pathways; the Nsp4 and Nsp10 of PRRSV function as apoptosis inducers with different molecular basis.
Oh Oh Oh Whoah! Towards Automatic Topic Detection In Song Lyrics

We present an algorithm that allows for indexing music by topic. The application scenario is an information retrieval system into which any song with known lyrics can be inserted and indexed so as to make a music collection browseable by topic. We use text mining techniques for creating a vector space model of our lyrics collection and non-negative matrix factorization (NMF) to identify topic clusters which are then labeled manually. We include a discussion of the decisions regarding the parametrization of the applied methods. The suitability of our approach is assessed by measuring the agreement of test subjects who provide the labels for the topic clusters.
Atomic Interaction Networks in the Core of Protein Domains and Their Native Folds

Vastly divergent sequences populate a majority of protein folds. In the quest to identify features that are conserved within protein domains belonging to the same fold, we set out to examine the entire protein universe on a fold-by-fold basis. We report that the atomic interaction network in the solvent-unexposed core of protein domains are fold-conserved, extraordinary sequence divergence notwithstanding. Further, we find that this feature, termed protein core atomic interaction network (or PCAIN) is significantly distinguishable across different folds, thus appearing to be “signature” of a domain's native fold. As part of this study, we computed the PCAINs for 8698 representative protein domains from families across the 1018 known protein folds to construct our seed database and an automated framework was developed for PCAIN-based characterization of the protein fold universe. A test set of randomly selected domains that are not in the seed database was classified with over 97% accuracy, independent of sequence divergence. As an application of this novel fold signature, a PCAIN-based scoring scheme was developed for comparative (homology-based) structure prediction, with 1–2 angstroms (mean 1.61A) Cα RMSD generally observed between computed structures and reference crystal structures. Our results are consistent across the full spectrum of test domains including those from recent CASP experiments and most notably in the ‘twilight’ and ‘midnight’ zones wherein <30% and <10% target-template sequence identity prevails (mean twilight RMSD of 1.69A). We further demonstrate the utility of the PCAIN protocol to derive biological insight into protein structure-function relationships, by modeling the structure of the YopM effector novel E3 ligase (NEL) domain from plague-causative bacterium Yersinia Pestis and discussing its implications for host adaptive and innate immune modulation by the pathogen. Considering the several high-throughput, sequence-identity-independent applications demonstrated in this work, we suggest that the PCAIN is a fundamental fold feature that could be a valuable addition to the arsenal of protein modeling and analysis tools.
Exploring a Framework for Identity and Attribute Linking across Heterogeneous Data Systems

Online-activity-generated digital traces provide opportunities for novel services and unique insights as demonstrated in, for example, research on mining software repositories. The inability to link these traces within and among systems, such as Twitter, GitHub, or Reddit, inhibit the advances in this area. Furthermore, no single approach to integrate data from these disparate sources is likely to work. We aim to design Foreseer, an extensible framework, to design and evaluate identity matching techniques for public, large, and low-accuracy operational data. Foreseer consists of three functionally independent components designed to address the issues of discovery and preparation, storage and representation, and analysis and linking of traces from disparate online sources. The framework includes a domain specific language for manipulating traces, generating insights, and building novel services. We have applied it in a pilot study of roughly 10TB of data from Twitter, Reddit, and StackExchange including roughly 6M distinct entities and, using basic matching techniques, found roughly 83,000 matches among these sources. We plan to add additional entity extraction and identification algorithms, data from other sources, and design tools for facilitating dynamic ingestion and tagging of incoming data on a more robust infrastructure using Apache Spark or another distributed processing framework. We will then evaluate the utility and effectiveness of the framework in applications ranging from identifying malicious contributors in software repositories to the evaluation of the utility of privacy preservation schemes.
Using Symmetry to Schedule Classical Matrix Multiplication

Presented with a new machine with a specific interconnect topology, algorithm designers use intuition about the symmetry of the algorithm to design time and communication-efficient schedules that map the algorithm to the machine. Is there a systematic procedure for designing schedules? We present a new technique to design schedules for algorithms with no non-trivial dependencies, focusing on the classical matrix multiplication algorithm. We model the symmetry of algorithm with the set of instructions $X$ as the action of the group formed by the compositions of bijections from the set $X$ to itself. We model the machine as the action of the group $N\times \Delta$, where $N$ and $\Delta$ represent the interconnect topology and time increments respectively, on the set $P\times T$ of processors iterated over time steps. We model schedules as symmetry-preserving equivariant maps between the set $X$ and a subgroup of its symmetry and the set $P\times T$ with the symmetry $N\times\Delta$. Such equivariant maps are the solutions of a set of algebraic equations involving group homomorphisms. We associate time and communication costs with the solutions to these equations. We solve these equations for the classical matrix multiplication algorithm and show that equivariant maps correspond to time- and communication-efficient schedules for many topologies. We recover well known variants including the Cannon's algorithm and the communication-avoiding "2.5D" algorithm for toroidal interconnects, systolic computation for planar hexagonal VLSI arrays, recursive algorithms for fat-trees, the cache-oblivious algorithm for the ideal cache model, and the space-bounded schedule for the parallel memory hierarchy model. This suggests that the design of a schedule for a new class of machines can be motivated by solutions to algebraic equations.
Learning material design for teaching genetics while implementing flipped classroom

This paper shows the process of research and design of learning materials for flipped classrooms, an active teaching method that intends to switch from the traditional acquisition of content in class and to the assignments’ resolution at home. In order to choose the content to be addressed, we based our efforts on a research that indicated "Genetics" as a subject of difficult comprehension. With the use of digital platforms, some flashcards, quizzes, mind maps, video and a website were produced to create the materials and promote the flipped classroom values. It is intended to show that the flipped classroom is an applicable approach, placing students at the center of the learning process.
Metabisulphite-induced occupational asthma in a radiographer

There is epidemiological evidence for an increased prevalence of occupational asthma among radiographers. However, the causes of darkroom disease are not yet defined. A 37-year-old female radiographer reported work-related asthma ∼2 yrs after starting work in a local hospital. She was atopic and showed bronchial hyperresponsiveness to methacholine. Occupational-type exposure with a fixing agent, but not with a developer, produced an immediate-type asthmatic reaction. As the fixing agent contained sodium metabisulphite (SMBS), a substance known to cause asthma, bronchial challenges with SMBS were performed in the patient and nine asthmatic controls. The patient showed a positive bronchial immediate reaction on 2 separate days after inhalation of 48 and 96 µg SMBS, and one control also showed a significant fall in forced expiratory volume in one second after inhalation of 12 µg SMBS. The positive reaction in the control subject argues for a greater susceptibility of both persons to SMBS or its reaction product sulphur dioxide, rather than for a new occupational allergen. It is concluded that sodium metabisulphite exposure should be recognised as a cause of darkroom asthma.
Heuristic Methods for Inference of XML Schemas: Lessons Learned and Open Issues

In this paper we focus on a specific class of XML schema inference approaches  so-called heuristic approaches. Contrary to grammar-inferring approaches, their result does not belong to any specific class of grammars and, hence, we cannot say anything about their features from the point of view of theory of languages. However, the heuristic approaches still form a wider and more popular set of approaches due to natural and user-friendly strategies. We describe a general framework of the inference algorithms and we show how its particular phases can be further enhanced and optimized to get more reasonable and realistic output. The aim of the paper is (1) to provide a general overview of the heuristic inference process and existing approaches, (2) to sum up the improvements and optimizations we have proposed so far in our research group, and (3) to discuss possible extensions and open problems which need to be solved. Hence, it enables the reader to get acquainted with the field fast.
The influence of values and other social and psychological factors on the dress and appearance of African American college students

nature of values (Kahle, 1984) suggesting that they are defined and redefined from internal and external dynamics (Kahle, 1983). Values are the most abstract form of social cognition that summarize adaptive behaviors (Kahle, 1983) and that are used to guide general
A Method Based on Iterative Combinatorial Auction Mechanism for Resource Allocation in Grid Multi-agent Systems

Resources allocation is complex undertaking problem in distributed resources share problems. In this work, we consider the problem of engineering agents that act as proxies for the procurement of computational and data resources. By using economic models, mechanisms such as auctions may appropriate for the management of these problems. In this paper, we provide the design and theoretical analysis of auction mechanism, design the details of prices announcement, agent creation and resource situation submission, bidding rules, and temporary allocation rules. A simulation environment is established and the simulation experiments indicate that the algorithm meet the requirements of user’s QoS better than the conventional algorithms.
Preference aggregation in combinatorial domains using GAI-nets

This paper deals with preference representation and aggregation in combinatorial domains. We assume that the set of alternatives is defined as the cartesian product of finite domains and that agents' preferences are represented by generalized additive decomposable (GAI) utility functions. GAI functions allow an efficient representation of interactions between attributes while preserving some decomposability of the model. We address the preference aggregation problem and consider several criteria to define the notion of compromise solution (maxmin, minmaxregret, weighted Tchebycheff distance). For each of them, we propose a fast procedure for the exact determination of the optimal compromise solution in the product set. This procedure relies on a ranking algorithm enumerating solutions according to the sum of the agents individual utilities until a boundary condition is reached. We provide results of numerical experiments to highlight the practical efficiency of our procedure
Epigenetic Regulation of Glucose Transporters in Non-Small Cell Lung Cancer

Due to their inherently hypoxic environment, cancer cells often resort to glycolysis, or the anaerobic breakdown of glucose to form ATP to provide for their energy needs, known as the Warburg effect. At the same time, overexpression of the insulin receptor in non-small cell lung cancer (NSCLC) is associated with an increased risk of metastasis and decreased survival. The uptake of glucose into cells is carried out via glucose transporters or GLUTs. Of these, GLUT-4 is essential for insulin-stimulated glucose uptake. Following treatment with the epigenetic targeting agents histone deacetylase inhibitors (HDACi), GLUT-3 and GLUT-4 expression were found to be induced in NSCLC cell lines, with minimal responses in transformed normal human bronchial epithelial cells (HBECs). Similar results for GLUT-4 were observed in cells derived from liver, muscle, kidney and pre-adipocytes. Bioinformatic analysis of the promoter for GLUT-4 indicates that it may also be regulated by several chromatin binding factors or complexes including CTCF, SP1 and SMYD3. Chromatin immunoprecipitation studies demonstrate that the promoter for GLUT-4 is dynamically remodeled in response to HDACi. Overall, these results may have value within the clinical setting as (a) it may be possible to use this to enhance fluorodeoxyglucose (18F) positron emission tomography (FDG-PET) imaging sensitivity; (b) it may be possible to target NSCLC through the use of HDACi and insulin mediated uptake of the metabolic targeting drugs such as 2-deoxyglucose (2-DG); or (c) enhance or sensitize NSCLC to chemotherapy.
Canny Text Detector: Fast and Robust Scene Text Localization Algorithm

This paper presents a novel scene text detection algorithm, Canny Text Detector, which takes advantage of the similarity between image edge and text for effective text localization with improved recall rate. As closely related edge pixels construct the structural information of an object, we observe that cohesive characters compose a meaningful word/sentence sharing similar properties such as spatial location, size, color, and stroke width regardless of language. However, prevalent scene text detection approaches have not fully utilized such similarity, but mostly rely on the characters classified with high confidence, leading to low recall rate. By exploiting the similarity, our approach can quickly and robustly localize a variety of texts. Inspired by the original Canny edge detector, our algorithm makes use of double threshold and hysteresis tracking to detect texts of low confidence. Experimental results on public datasets demonstrate that our algorithm outperforms the state-of the-art scene text detection methods in terms of detection rate.
Visual SLAM using multiple RGB-D cameras

In this paper, we present a solution to visual simultaneous localization and mapping (SLAM) using multiple RGB-D cameras. In the SLAM system, we integrate visual and depth measurements from those RGB-D cameras to achieve more robust pose tracking and more detailed environmental mapping in unknown environments. We present the mathematical analysis of the iterative optimizations for pose tracking and map refinement of a RGB-D SLAM system in multi-camera cases. The resulted SLAM system allows configurations of multiple RGB-D cameras with non-overlapping fields of view (FOVs). Furthermore, we provide a SLAM-based semiautomatic method for extrinsic calibration among such cameras. Finally, the experiments in complex indoor scenarios demonstrate the efficiency of the proposed visual SLAM algorithm.