Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Support and Resilience among Syrian Refugees: The Mediating Role of Self-Efficacy

ABSTRACT Refugees are exposed to potentially traumatic events before, during, and after the forced displacement. Related to these events, they are at risk of developing mental health problems. From a public-health perspective, it is important to investigate factors fostering resilience among refugee population. This study aimed to explore the mediation role of self-efficacy between social support and resilience among Syrian refugees living in Istanbul, Turkey. A cross-sectional survey of 339 Syrian refugees aged 18 and older was conducted between September 2018 and March 2019 in Istanbul. The Connor-Davidson Resilience Scale (CD-RISC) was used to measure resilience, the Multidimensional Scale of Perceived Social Support (MSPSS) assessed social support, and self-efficacy was measured by the General Self-Efficacy Scale (GSE). The simple mediation model analysis showed the mediating effect of self-efficacy on the social support and resilience relationship. Among the demographic factors, males and individuals with higher education levels reported increased resilience. Findings illuminated the importance of self-efficacy in promoting resilience among refugee population. Implications were discussed in a culturally appropriate way to enhance self-efficacy among Syrian refugees.
Synthesis and Characterization of Pyrazole-Enriched Cationic Nanoparticles as New Promising Antibacterial Agent by Mutual Cooperation

A pyrazole derivative (CB1) was previously evaluated in vivo for various pharmacological activities (with the exception of antimicrobial effects), using DMSO as the administrative medium, mainly due to its water insolubility. Considering the global necessity for new antimicrobial agents, CB1 attracted our attention as a candidate to meet this need, mainly because the secondary amine group in its structure would make it possible to obtain its hydrochloride salt (CB1H), thus effortlessly solving its water-solubility drawbacks. In preliminary microbiologic investigations on Gram-negative and Gram-positive bacteria, CB1H displayed weak antibacterial effects on MDR isolates of Gram-positive species, nonetheless better than those displayed by the commonly-used available antibiotics. Therefore, aiming at improving such activity and extending the antibacterial spectrum of CB1H to Gram-negative pathogens, in this first work CB1 was strategically formulated in nanoparticles using a cationic copolymer (P7) previously developed by us, possessing potent broad-spectrum bactericidal activity. Using the nanoprecipitation method, CB1H-loaded polymer nanoparticles (CB1H-P7 NPs) were obtained, which were analyzed by attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy to confirm the successful loading. Additionally, CB1H-P7 NPs were fully characterized in terms of morphology, size, polydispersity indices, surface charge, DL%, and EE%, as well as release and potentiometric profiles.
Comparative Proteomic Analysis of Rhizoctonia solani Isolates Identifies the Differentially Expressed Proteins with Roles in Virulence

Sheath blight of rice is a destructive disease that could be calamitous to rice cultivation. The significant objective of this study is to contemplate the proteomic analysis of the high virulent and less virulent isolate of Rhizoctonia solani using a quantitative LC-MS/MS-based proteomic approach to identify the differentially expressed proteins promoting higher virulence. Across several rice-growing regions in Odisha, Eastern India, 58 Rhizoctonia isolates were obtained. All the isolates varied in their pathogenicity. The isolate RS15 was found to be the most virulent and RS22 was identified as the least virulent. The PCR amplification confirmed that the RS15 and RS22 belonged to the Rhizoctonia subgroup of AG1-IA with a specific primer. The proteomic information generated has been deposited in the PRIDE database with PXD023430. The virulent isolate consisted of 48 differentially abundant proteins, out of which 27 proteins had higher abundance, while 21 proteins had lower abundance. The analyzed proteins acquired functionality in fungal development, sporulation, morphology, pathogenicity, detoxification, antifungal activity, essential metabolism and transcriptional activities, protein biosynthesis, glycolysis, phosphorylation and catalytic activities in fungi. A Quantitative Real-Time PCR (qRT-PCR) was used to validate changes in differentially expressed proteins at the mRNA level for selected genes. The abundances of proteins and transcripts were positively correlated. This study provides the role of the proteome in the pathogenicity of R. solani AG1-IA in rice and underpins the mechanism behind the pathogen’s virulence in causing sheath blight disease.
Do Females Intend to Use Gamified Virtual Currency in E-Tailing?: An Empirical Study

The current study serves two purposes. First and foremost, this research aims to determine the impact of a game mechanism known as virtual currency on the intention of young female consumers to use it in a e-tailing platform. Second, it looks at the function of e-trust and task awareness as an extension to the TAM framework, two antecedents that are thought to influence female virtual currency adoption intentions in e-tailing. The proposed framework was evaluated using data from an online survey of 386 female participants across India. The conceptual framework is empirically validated using the PLS-SEM technique. The current study broadens the scope of game mechanics by emphasizing the importance of e-trust as an independent variable and task awareness as a mediator. Findings imply that the e-tailer's may include virtual currency into their platforms, allowing female consumers to make substantial use of it in their purchasing decisions.
QANANET: Improve Question Answering By Learning Not To Answer

Accurate automated question answering help people learn new knowledge at large scale. In this project, we attempt to solve the question answering task proposed by the SQuAD dataset by 1) increasing accuracy of Bi-directional Attention Flow (BiDAF) [1] using convolution and self-attention based on QANet [2] and 2) adding a new classification AvNA head and loss function for QANet to explicitly handle non-answer case which are new to SQuAD 2.0 [3]. This report shows that our QANet implemented from scratch significantly improves the baseline BiDAF model F 1 dev score from 60 . 71 to 69 . 34 , and ExactMatch ( EM ) from 57 . 10 to 65 . 52 . Our final model with AvNA head further improves the dev score to 70 . 37 for F 1 and 66 . 85 for EM. On the IID default track test leaderboard, our model achieves relatively high F 1 of 66 . 581 and EM of 62 . 975 .
CREIME—A Convolutional Recurrent Model for Earthquake Identification and Magnitude Estimation

The detection and rapid characterization of earthquake parameters such as magnitude are important in real‐time seismological applications such as Earthquake Monitoring and Earthquake Early Warning (EEW). Traditional methods, aside from requiring extensive human involvement can be sensitive to signal‐to‐noise ratio leading to false/missed alarms depending on the threshold. We here propose a multitasking deep learning model—the Convolutional Recurrent model for Earthquake Identification and Magnitude Estimation (CREIME) that: (a) detects the earthquake signal from background seismic noise, (b) determines the first P wave arrival time, and (c) estimates the magnitude using the raw three‐component waveforms from a single station as model input. Considering, that speed is essential in EEW, we use up to 2 s of P wave information which, to the best of our knowledge, is a significantly smaller data window compared to the previous studies. To examine the robustness of CREIME, we test it on two independent data sets and find that it achieves an average accuracy of 98% for event versus noise discrimination and can estimate first P‐arrival time and local magnitude with average root mean squared errors of 0.13 s and 0.65 units, respectively. We compare CREIME with traditional methods such as short‐term‐average/long‐term‐average (STA/LTA) and show that CREIME has superior performance, for example, the accuracy for signal and noise discrimination is higher by 4.5% and 11.5%, respectively, for the two data sets. We also compare the architecture of CREIME with the architectures of other baseline models, trained on the same data, and show that CREIME outperforms the baseline models.
Maintaining Expander Decompositions via Sparse Cuts

In this article, we show that the algorithm of maintaining expander decompositions in graphs undergoing edge deletions directly by removing sparse cuts repeatedly can be made efficient. Formally, for an $m$-edge undirected graph $G$, we say a cut $(S, \overline{S})$ is $\phi$-sparse if $|E_G(S, \overline{S})|<\phi \cdot \min\{vol_G(S), vol_G(\overline{S})\}$. A $\phi$-expander decomposition of $G$ is a partition of $V$ into sets $X_1, X_2, \ldots, X_k$ such that each cluster $G[X_i]$ contains no $\phi$-sparse cut (meaning it is a $\phi$-expander) with $\tilde{O}(\phi m)$ edges crossing between clusters. A natural way to compute a $\phi$-expander decomposition is to decompose clusters by $\phi$-sparse cuts until no such cut is contained in any cluster. We show that even in graphs undergoing edge deletions, a slight relaxation of this meta-algorithm can be implemented efficiently with amortized update time $m^{o(1)}/\phi^2$. Our approach naturally extends to maintaining directed $\phi$-expander decompositions and $\phi$-expander hierarchies and thus gives a unifying framework while having simpler proofs than previous state-of-the-art work. In all settings, our algorithm matches the run-times of previous algorithms up to subpolynomial factors. Moreover, our algorithm provides stronger guarantees for $\phi$-expander decompositions. For example, for graphs undergoing edge deletions, our approach is the first to maintain a dynamic expander decomposition where each updated decomposition is a refinement of the previous decomposition, and our approach is the first to guarantee a sublinear $\phi m^{1+o(1)}$ bound on the total number of edges that cross between clusters across the entire sequence of dynamic updates.
Microjoule‐Level Mid‐Infrared Femtosecond Pulse Generation in Hollow‐Core Fibers

A fiber‐based approach that generates mid‐infrared femtosecond pulses in the 3–4 μ$\umu$ m spectral region with microjoule‐level single pulse energy is demonstrated. This is realized in a piece of gas‐filled antiresonant hollow‐core fiber that is pumped by a two‐micron light source. A rapid variation of the dispersion near a structural resonance of the fiber creates a phase‐matching point in mid‐infrared, which mediates the frequency‐down conversion. Femtosecond pulses centered at 3.16 μ$\umu$ m wavelength with the pulse energy of more than 1 μ$\umu$ J are generated, achieving a conversion efficiency as high as 8.2%. The emission wavelength is determined solely by the dielectric wall thickness of cladding elements, while the yield is subject to other experimental parameters. This, combined with high power‐handling capability of hollow‐core fibers, makes it possible to power scale the mid‐infrared output by either increasing the pulse energy or repetition rate of the pump. The technique presents a new pathway to build an all‐fiber‐based mid‐infrared supercontinuum source, which promises to be a powerful new tool for ultrahigh sensitivity molecular spectroscopy.
P4RROT: Generating P4 Code for the Application Layer

Throughput and latency critical applications could often benefit of performing computations close to the client. To enable this, distributed computing paradigms such as edge computing have recently emerged. However, with the advent of programmable data planes, computations cannot only be performed by servers but they can be offloaded to network switches. Languages like P4 enable to flexibly reprogram the entire packet processing pipeline. Though these devices promise high throughput and ultra-low response times, implementing application-layer tasks in the data plane programming language P4 is still challenging for an application developer who is not familiar with networking domain. In this paper, we first identify and examine obstacles and pain points one can experience when offloading server-based computations to the network. Then we present P4rrot, a code generator (in form of a library) which allows to overcome these limitations by providing a user-friendly API to describe computations to be offloaded. After discussing the design choices behind P4rrot, we introduce our proof-of-concept implementation for two P4 targets: Netronome SmartNIC and BMv2. To demonstrate the applicability of P4rrot, we investigate case studies in the context of publish-subscribe sensor data processing and real-time data streaming, supporting, in particular, MQTT-SN and MoldUDP packets.
Applying machine learning techniques to the analysis of policy data of the military health enterprise

It is common practice in organizational research to apply policy analysis to better understand how an enterprise is approaching and addressing a particular topic of interest. The approach to policy analysis commonly used is known as the Coding Approach. The Coding Approach is a highly qualitative process that involves the manual identification of relevant policy documents, the manual review of these documents to identify the key features of the topic, and the manual identification of the significance of these features as it relates to the policy documents. This process can generates rich insights into policies and how the topic of interest is being approached and viewed by the Stakeholder of the enterprise. This process however is a manually intensive process and is subject to the bias of the expert/s reviewing and analyzing the documents. My study proposes a new approach towards policy analysis that uses the Coding Approach as its template, but applies Machine Learning Techniques, such as Natural Language Processing and Data Mining Algorithms, together with a highly structured form of Case and Cross Case Analysis to identify documents that are related to the topic of interest, to categorize these documents, to surface the key features of the topic, to calculate the significance of these features as reflected by the documents and to draw inferences about the key features and its significance as it relates to the policy documents. This new approach provides a mixed methods approach that marries the best of both quantitative and qualitative techniques towards document analysis. This approach also reduces the amount bias that can be injected from the experts analyzing the documents, and thus guarantees an almost consistent result from document analysis regardless of the experts performing the analysis. For my study I applied my mixed methods approach to analyzing the policy documents of the Military Health Enterprise MHS, to understand how well the MHS 's policies were addressing the delivery of psychological services to service members and their families. This study is important to the MHS for two reasons. With the reductions of the US Military presence in Afghanistan and Iraq there are large numbers of veterans returning who may have various forms of PTSD, who will requires varying types and levels of care. The other reason why this study is important to the MHS is due to the recent scandals (Walter Reed, 2007 & VA, 2014) it has faced specifically around to the health care services it was supposed to provide to service members. Now its crucial for the MHS to understand the disconnect between its policies and what's actually being implemented. This study will provide the MHS with a non-bias review of what are the features of significance from a policy point of view in regards to the delivery of psychological services to service members and their families. THIS PAGE INTENTIONALLY LEFT BLANK ;, , -assw nm n 'tisdisuesa2WkdiiaA"a a inss iW auaa dE~ M EN MM mi-
'The Pedestrian next to the Lamppost” Adaptive Object Graphs for Better Instantaneous Mapping

Estimating a semantically segmented bird's-eye-view (BEV) map from a single image has become a popular technique for autonomous control and navigation. However, they show an increase in localization error with distance from the camera. While such an increase in error is entirely expected -localization is harder at distance - much of the drop in performance can be attributed to the cues used by current texture-based models, in particular, they make heavy use of object-ground intersections (such as shadows) [10], which become increasingly sparse and uncertain for distant objects. In this work, we address these shortcomings in BEV-mapping by learning the spatial relationship between objects in a scene. We propose a graph neural network which predicts BEV objects from a monocular image by spatially reasoning about an object within the context of other objects. Our approach sets a new state-of-the-art in BEV estimation from monocular images across three large-scale datasets, including a 50% relative improvement for objects on nuScenes.
Unconstrained Proximal Operator: the Optimal Parameter for the Douglas-Rachford Type Primal-Dual Methods

In this work, we propose an alternative parametrized form of the proximal operator, of which the parameter no longer needs to be positive. That is, the parameter can be a non-zero scalar, a full-rank square matrix, or, more generally, a bijective bounded linear operator. We demonstrate that the positivity requirement is essentially due to a quadratic form. We prove several key characterizations for the new form in a generic way (with an operator parameter). We establish the optimal choice of parameter for the Douglas-Rachford type methods by solving a simple unconstrained optimization problem. The optimality is in the sense that a non-ergodic worst-case convergence rate bound is minimized. We provide closed-form optimal choices for scalar and orthogonal matrix parameters under zero initialization. Additionally, a simple self-contained proof of a sharp linear convergence rate for a (1 /L )-cocoercive ﬁxed-point sequence with L ∈ (0 , 1) is provided (as a preliminary result). Toour knowledge, an operator parameter is new. To show its practical use, we design a dedicated parameter for the 2-by-2 block-structured semideﬁnite program (SDP). Such a structured SDP is strongly related to the quadratically constrained quadratic program (QCQP), and we therefore expect the proposed parameter to be of great potential use. At last, two well-known applications are investigated. Numerical results show that the theoretical optimal parameters are close to the practical optimums, except they are not a priori knowledge. We then demonstrate that, by exploiting problem model structures, the theoretical optimums can be well approximated. Such approximations turn out to work very well, and in some cases almost reach the underlying limits.
Universal Representations: A Unified Look at Multiple Task and Domain Learning

We propose a unified look at jointly learning multiple vision tasks and visual domains through universal representations, a single deep neural network. Learning multiple problems simultaneously involves minimizing a weighted sum of multiple loss functions with different magnitudes and characteristics and thus results in unbalanced state of one loss dominating the optimization and poor results compared to learning a separate model for each problem. To this end, we propose distilling knowledge of multiple task/domain-specific networks into a single deep neural network after aligning its representations with the task/domain-specific ones through small capacity adapters. We rigorously show that universal representations achieve state-of-the-art performances in learning of multiple dense prediction problems in NYU-v2 and Cityscapes, multiple image classification problems from diverse domains in Visual Decathlon Dataset and cross-domain few-shot learning in MetaDataset. Finally we also conduct multiple analysis through ablation and qualitative studies.
Improving Voice Trigger Detection with Metric Learning

Voice trigger detection is an important task, which enables activating a voice assistant when a target user speaks a keyword phrase. A detector is typically trained on speech data independent of speaker information and used for the voice trigger detection task. However, such a speaker independent voice trigger detector typically suffers from performance degradation on speech from underrepresented groups, such as accented speakers. In this work, we propose a novel voice trigger detector that can use a small number of utterances from a target speaker to improve detection accuracy. Our proposed model employs an encoder-decoder architecture. While the encoder performs speaker independent voice trigger detection, similar to the conventional detector, the decoder predicts a personalized embedding for each utterance. A personalized voice trigger score is then obtained as a similarity score between the embeddings of enrollment utterances and a test utterance. The personalized embedding allows adapting to target speaker's speech when computing the voice trigger score, hence improving voice trigger detection accuracy. Experimental results show that the proposed approach achieves a 38% relative reduction in a false rejection rate (FRR) compared to a baseline speaker independent voice trigger model.
Distributed Transition Systems with Tags for Privacy Analysis

We present a logical framework that formally models how a given private information P stored on a given database D, can get captured progressively, by an agent/adversary querying the database repeatedly. Named DLTTS (Distributed Labeled Tagged Transition System), the framework borrows ideas from several domains: Probabilistic Automata of Segala, Probabilistic Concurrent Systems, and Probabilistic labelled transition systems. To every node on a DLTTS is attached a tag that represents the 'current' knowledge of the adversary, acquired from the responses of the answering mechanism of the DBMS to his/her queries, at the nodes traversed earlier, along any given run; this knowledge is completed at the same node, with further relational deductions, possibly in combination with 'public' information from other databases given in advance. A 'blackbox' mechanism is also part of a DLTTS, and it is meant as an oracle; its role is to tell if the private information has been deduced by the adversary at the current node, and if so terminate the run. An additional special feature is that the blackbox also gives information on how 'close', or how 'far', the knowledge of the adversary is, from the private information P , at the current node. A metric is defined for that purpose, on the set of all 'type compatible' tuples from the given database, the data themselves being typed with the headers of the base. Despite the transition systems flavor of our framework, this metric is not 'behavioral' in the sense presented in some other works. It is exclusively database oriented, and allows to define new notions of adjacency and of indistinguishabilty between databases, more generally than those usually based on the Hamming metric (and a restricted notion of adjacency). Examples are given all along to illustrate how our framework works. Keywords:Database, Privacy, Transition System, Probability, Distribution.