Miracle-dz/UniFAR-FTUs huggingface dataset:
Flexible photonic sensor based on locally synthesized metal oxide nanowire network

We propose a new flexible photonic sensor based on metal oxide nanowire networks fabricated by a novel eco-friendly focused energy field (FEF) method. This method is based on the localized thermal energy along microscale heater within liquid precursor solutions of nanowire synthesis. FEF method enables selective synthesis and in-situ integration of ZnO nanowires on the metal electrodes of function devices. Further, low-temperature and nontoxic nature of the process allows the compatibility with flexible polymer substrates. High band gap of ZnO (3.37eV) allows the sensitivity of ZnO nanowire networks to the UV illumination (λ=365nm) with a dark current of 60nA and on-off ratio of 50∼70. Due to the robustness and good adhesion of ZnO nanowires fabricated on the polymer substrate, the shape of nanowire and photoconductive characteristic didn't change.
Video Game Play and Effect on Spatial Knowledge Tasks Using an Augmented Sand Table

Augmented-reality (AR) can be used to supplement current analog technology (e.g. traditional Sand Table EXercises; STEX) used in training spatial knowledge and tactical decision making in the U.S. military. By utilizing an Augmented REality Sand table (ARES), an effectiveness evaluation was executed in order to scientifically evaluate performance on spatial knowledge tasks across three mediums (paper map, Google Earth©, and ARES). Post hoc analyses performed on demographic data revealed significant individual difference factors between those who reported playing video games often and those who reported playing rarely or not at all. Video game players performed better than non-video game players on distance estimation tasks and when they were utilizing computer-based tools. Additionally, participants who rated themselves as good or excellent video game players saw significant value and utility in an augmented-reality solution. These results benefit the cumulative research base both in multimodal learning literature and in video game play on cognition and visuospatial alteration.
Incremental decision procedures for modal logics with nominals and eventualities

This thesis contributes to the study of incremental decision procedures for modal logics with nominals and eventualities. Eventualities are constructs that allow to reason about the reflexive-transitive closure of relations. Eventualities are an essential feature of temporal logics and propositional dynamic logic (PDL). Nominals extend modal logics with the possibility to reason about state equality. Modal logics with nominals are often called hybrid logics. Incremental procedures are procedures that can potentially solve a problem by performing only the reasoning steps needed for the problem in the underlying calculus. We begin by introducing a class of syntactic models called demos and showing how demos can be used for obtaining nonincremental but worst-case optimal decision procedures for extensions of PDL with nominals, converse and difference modalities. We show that in the absence of nominals, such nonincremental procedures can be refined into incremental demo search procedures, obtaining a worst-case optimal decision procedure for modal logic with eventualities. We then develop the first incremental decision procedure for basic hybrid logic with eventualities, which we eventually extend to deal with hybrid PDL. The approach in the thesis suggests a new principled design of modular, incremental decision procedures for expressive modal logics. In particular, it yields the first incremental procedures for modal logics containing both nominals and eventualities.
A Dataset for Detecting Humor in Telugu Social Media Text

Increased use of online social media sites has given rise to tremendous amounts of user generated data. Social media sites have become a platform where users express and voice their opinions in a real-time environment. Social media sites such as Twitter limit the number of characters used to express a thought in a tweet, leading to increased use of creative, humorous and confusing language in order to convey the message. Due to this, automatic humor detection has become a difficult task, especially for low-resource languages such as the Dravidian languages. Humor detection has been a well studied area for resource rich languages due to the availability of rich and accurate data. In this paper, we have attempted to solve this issue by working on low-resource languages, such as, Telugu, a Dravidian language, by collecting and annotating Telugu tweets and performing automatic humor detection on the collected data. We experimented on the corpus using various transformer models such as Multilingual BERT, Multilingual DistillBERT and XLM-RoBERTa to establish a baseline classification system. We concluded that XLM-RoBERTa was the best-performing model and it achieved an F1-score of 0.82 with 81.5% accuracy.
Classification automatique de documents structurés. Application au corpus d'arbres étiquetés de type XML

Le domaine de la Recherche d’Information Structuree (RIS) est un domaine qui emerge avec l’arrivee de donnees semi structurees comme les documents XML. Ce domaine, a travers l’initiative INEX, concerne principalement le developpement de moteurs de recherche documentaire. Aujourd’hui, il est necessaire de developper des modeles pour le traitement de differentes problematiques dans les documents structures comme la discrimination ou la restructuration. Dans cet article, nous nous interessons a la classification automatique de documents XML en fonction de leur regularites structurelles. Nous proposons de modeliser la structure des documents XML par un reseau bayesien qui permet de prendre en compte differentes dependances entres les unites structurelles du document. Nous presentons les resultats de nos differents modeles sur le corpus INEX et voyons ensuite comment un de nos modeles permet de determiner un representant de chacune des classes obtenues sous forme d’une DTD probabiliste.
In vitro Regeneration in Three Varieties of White jute (Corchorus capsularis L.)

Healthy multiple shoot regeneration was observed from petiole-attached cotyledon (PC) explants of CVL-1 on MS containing 0.2 mg/l BAP and 1.0 mg/l IAA. On the other hand, the best response for multiple shoot regeneration in CVE-3 was obtained when the same explants were cultured on MS with 2.5 mg/l BAP and 0.5 mg/l NAA. However, the same explant of D-154 was found to show less responsive compared to other two varieties to produce multiple shoots. Cotyledonary nodal explants of all the three varieties were found to produce maximum number of multiple shoots on MS supplemented with 0.2 mg/l BAP and 1.0 mg/l IAA. Best root induction was observed at the base of the in vitro regenerated shoots on half the strength of MS supplemented with 0.3 mg/l IBA. The in vitro grown plantlets were successfully transplanted into soil. They grew up to maturity, flowered and fruited like the control plants. Key words: Regeneration, White jute, Corchorus capsularis DOI = 10.3329/ptcb.v17i1. 1116 Plant Tissue Cult. & Biotech. 17(1): 11-18, 2007 (June)
Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbation

The robustness of Text-to-SQL parsers against adversarial perturbations plays a crucial role in delivering highly reliable applications. Previous studies along this line primarily focused on perturbations in the natural language question side, neglecting the variability of tables. Motivated by this, we propose the Adversarial Table Perturbation (ATP) as a new attacking paradigm to measure robustness of Text-to-SQL models. Following this proposition, we curate ADVETA, the first robustness evaluation benchmark featuring natural and realistic ATPs. All tested state-of-the-art models experience dramatic performance drops on ADVETA, revealing significant room of improvement. To defense against ATP, we build a systematic adversarial training example generation framework tailored for better contextualization of tabular data. Experiments show that our approach brings models best robustness improvement against ATP, while also substantially boost model robustness against NL-side perturbations. We will release ADVETA and code to facilitate future research.
Comparative Snippet Generation

We model products’ reviews to generate comparative responses consisting of positive and negative experiences regarding the product. Specifically, we generate a single-sentence, comparative response from a given positive and a negative opinion. We contribute the first dataset for this task of Comparative Snippet Generation from contrasting opinions regarding a product, and an analysis of performance of a pre-trained BERT model to generate such snippets.
An Ensemble Approach to Detect Emotions at an Essay Level

This paper describes our system (IREL, reffered as himanshu.1007 on Codalab) for Shared Task on Empathy Detection, Emotion Classification, and Personality Detection at 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis at ACL 2022. We participated in track 2 for predicting emotion at the essay level. We propose an ensemble approach that leverages the linguistic knowledge of the RoBERTa, BART-large, and RoBERTa model finetuned on the GoEmotions dataset. Each brings in its unique advantage, as we discuss in the paper. Our proposed system achieved a Macro F1 score of 0.585 and ranked one out of thirteen teams
d-Xylose Metabolism in Hypocrea jecorina: Loss of the Xylitol Dehydrogenase Step Can Be Partially Compensated for by lad1-Encoded l-Arabinitol-4-Dehydrogenase

ABSTRACT With the goal of the genetic characterization of the d-xylose pathway in Hypocrea jecorina (anamorph: Trichoderma reesei), we cloned the xdh1 gene, encoding NAD-xylitol dehydrogenase, which catalyzes the second step of fungal d-xylose catabolism. This gene encodes a 363-amino-acid protein which has a mass of 38 kDa, belongs to the zinc-containing alcohol dehydrogenase family, exhibits high sequence identity to the published sequences of xylitol dehydrogenases from yeast origins, but contains a second, additional binding site for Zn2+. The enzyme catalyzed the NAD-dependent oxidation of xylitol and d-sorbitol and the NADH-dependent reduction of d-xylulose and d-fructose. No activity was observed with NADP, l-arabinose, or l-arabinitol. A single 1.4-kb transcript was formed during growth on xylan, d-xylose, l-arabinose, l-arabinitol and, at a lower abundance, xylitol, d-galactose, galactitol, and lactose but not on d-glucose and glycerol. xdh1 deletion mutants exhibited 50% reduced growth rates on d-xylose, whereas growth rates on xylitol remained unaltered. These mutants contained 30% of the xylitol dehydrogenase activity of the parent strain, indicating the presence of a second xylitol dehydrogenase. This activity was shown to be due to lad1-encoded l-arabinitol-4-dehydrogenase, because H. jecorina xdh1 lad1 double-deletion strains failed to grow on d-xylose or xylitol. In contrast, lad1 deletion strains of H. jecorina grew normally on these carbon sources. These results show that H. jecorina contains a single xylitol dehydrogenase which is encoded by xdh1 and is involved in the metabolism of d-xylose and that lad1-encoded l-arabinitol-4-dehydrogenase can compensate for it partially in mutants with a loss of xdh1 function.
Boundary condition independent compact models of dynamic thermal networks with many heat sources

A novel approach, based both on multivariate moment matching and singular value decomposition, is proposed for generating passive compact models of boundary condition independent dynamic thermal networks with many heat sources. As main result, the generated passive compact dynamic thermal networks both assure a prescribed accuracy and have a number of elements linearly related to the number of heat sources
An outbreak of cholera among migrants living in a Thai-Myanmar border area.

OBJECTIVE To study epidemiologic characteristics of a cholera outbreak involving mainly Myanmar migrants living in overcrowded conditions with poor sanitation in a Thai-Myanmar border district, in 2007. MATERIAL AND METHOD Both passive and active case surveillances were carried out in Mae Sot District, Tak Province since the beginning of the outbreak. Samples of various types of drinking and non-drinking water from the infected areas, communal waters, and some selected foods were analyzed for the presence of cholera contamination. A case-control study was conducted to determine the vehicle of cholera transmission among Myanmar migrants in one municipal community with a cluster of 72 cholera cases. Preventive and control measures were primarily carried out by trained migrant health volunteers and workers. RESULTS Between May and October 2007, 477 cholera cases of biotype El Tor, serotype Inaba, were identified in the district. The majority of them (93.1%) were detected by active case surveillance in the communities. None died in this outbreak. Most (84.9%) were Myanmar migrants and the remainder were local Thai residents. The infection rates of cholera were significantly greater in communities with known passive cases than in those with no such cases. Three samples of seafood illegally imported from Myanmar were positive for cholera of the same biotype and serotype. Fifteen of 324 (4.6%) food handlers in the district were found to carry V. cholerae O1. A case-control study in one municipal community revealed a significant association between infection and frequently having food purchased from one infected food handler. CONCLUSION Active case finding and implementation of control measures by the assistance of trained migrant health volunteers and workers might reduce the morbidity and mortality in this population.
DNA polymorphism in strains of Listeria monocytogenes

DNA polymorphism in 35 Listeria monocytogenes strains belonging to serovars 1/2a, 1/2b, 1/2c, and 4b was studied by genomic DNA digestion. The restriction endonucleases ApaI and NotI, which cleave DNA at rare sequences, were used, and DNA fragments were analyzed by pulsed-field gel electrophoresis. Restriction fragment length polymorphism varied among different serovars and was used for epidemiological studies, but serovar 1/2c isolates could not be analyzed because their restriction patterns were indistinguishable. The genome sizes were calculated by addition of the sizes of the ApaI fragments and were found to be about 2,660 kb for serovar 1/2a strains, 2,640 kb for serovar 1/2b strains, and 2,710 kb for serovar 4b strains but only 2,340 kb for serovar 1/2c strains. This last group therefore appears to differ from the other serovar strains by the absence of restriction fragment length polymorphism and a chromosome that is 15% shorter, suggesting that strains of serovar 1/2c have quite recently emerged.
Cytotoxicity of carbon nanohorns in different human cells of the respiratory system

ABSTRACT One of the new synthetic carbon-based nanomaterials is carbon nanohorns (CNH). A potential risk for employees of production processes is an unintentional intake of these nanomaterials via inhalation. Once taken up, nanoparticles might interact with cells of different tissues as well as with intercellular substances. These interactions may have far-reaching consequences for human health. Currently, many gaps in available information on the CNH toxicological profile remain. The aim of this study was to determine the cytotoxicity of CNH particles on human epithelial cells of the respiratory system with special consideration given to different particle sizes. In all cell lines, cell viability was reduced after 24 h of exposure up to 60% and metabolic activity as evidenced by mitochondrial activity was lowered to 9% at a concentration of 1 g/L. The three respiratory cell lines differed in their sensitivity. The most robust cells were the bronchial epithelial cells. Further, particle size fractions induced different adverse effect strength, whereby no correlation between particle size fraction and toxicity was found. These findings demonstrate the need for further information regarding the behavior and effect strength of nanomaterial. To avoid the production of new harmful materials, a more comprehensive integration of results from toxicity studies in the development processes of engineered nanomaterials is recommended not only from an occupational viewpoint but also from an environmental perspective.
Classificação e resolução de defeitos em manutenção de software utilizando ODC e histórico de soluções

Nos dias atuais, com o aumento da demanda de servicos de suporte, como, por exemplo, no campo da alta disponibilidade e do desempenho para o cliente e da demanda de custos mais baixos para as empresas de manutencao e hospedagem de software, a resolucao de incidentes de software em um tempo menor junto ao cliente e a prevencao de defeitos tornaram-se um topicos fundamentais. Alem disso, a atividade de manutencao de software e uma das fases que consome mais tempo, esforco e consequentemente custo no ciclo de desenvolvimento de software. Balancear eficiencia e custo torna-se um desafio para qualquer empresa de suporte em manutencao de software. A abordagem utilizada nesta pesquisa estabelece um processo para classificar defeitos e delinear um conjunto de melhores solucoes para os defeitos classificados a partir do historico de defeitos e a base de conhecimento do cliente. Esta classificacao tambem separa complexidade de problemas para serem gerenciados pelo time de suporte mais adequado. O metodo base utilizado e o ODC (Classificacao Ortogonal de Defeitos) e extensoes voltadas ao suporte de software sao propostas e utilizadas. Por meio desta pesquisa, e possivel verificar se a classificacao e associacao de solucoes podem acarretar em uma reducao no tempo de atendimento dos incidentes de suporte. Foi observado em quatro amostras de dois clientes diferentes (X e Y) que utilizando a classificacao dos defeitos, direcionamento correto aos times de suporte e agrupamento de solucoes, promoveu uma reducao no tempo de atendimento em 70% dos incidentes de suporte no cliente Y e 92,5% dos incidentes de suporte para o cliente X. A reducao de tempo foi obtida pela reducao no numero de transferencias entre os times de suporte e a reducao de incidentes. O processo apresentado e incremental, pois e baseado no aumento das informacoes historicas e na eficacia das solucoes propostas. Este metodo de solucoes pode favorecer a reducao dos recursos necessarios para suportar sistemas computacionais em provedores de servico.
Feature Conservation in Adversarial Classifier Evasion: A Case Study

—Machine learning is widely used in security applications, particularly in the form of statistical classiﬁcation aimed at distinguishing benign from malicious entities. Recent research has shown that such classiﬁers are often vulnerable to evasion attacks, whereby adversaries change behavior to be categorized as benign while preserving malicious functionality. Research into evasion attacks has followed two paradigms: attacks in problem space , where the actual malicious instance is modiﬁed, and attacks in feature space , where the attack is abstracted into modifying numerical features of an instance to evade a classiﬁer. In contrast, research into designing evasion-robust classiﬁers generally relies on feature space attack models. We make several contributions to address this gap, using PDF malware detection as a case study. First, we present a systematic retraining procedure which uses an automated problem space attack generator to design a more robust PDF malware detector. Second, we demonstrate that replacing problem space attacks with feature space attacks dramatically reduces the robustness of the resulting classiﬁer, severely undermining feature space defense methods to date. Third, we demonstrate the existence of conserved (or invariant) features, and show how these can be leveraged to design evasion-robust classiﬁers that are nearly as effective, and far more efﬁcient, than those relying on the problem space attack. Finally, we present a general approach for identifying conserved features.
Neurodegeneration in friedreich's ataxia is associated with a mixed activation pattern of the brain. A fMRI study

Friedreich's ataxia (FRDA) is associated with a distributed pattern of neurodegeneration in the spinal cord and the brain secondary to selective neuronal loss. We used functional MR Imaging (fMRI) to explore brain activation in FRDA patients during two motor‐sensory tasks of different complexity, i.e. continuous hand tapping and writing of “8” figure, with the right dominant hand and without visual feedback. Seventeen FRDA patients and two groups of age‐matched healthy controls were recruited. Task execution was monitored and recorded using MR‐compatible devices. Hand tapping was correctly performed by 11 (65%) patients and writing of the “8” by 7 (41%) patients. After correction for behavioral variables, FRDA patients showed in both tasks areas of significantly lower activation in the left primary sensory‐motor cortex and right cerebellum. Also left thalamus and right dorsolateral prefrontal cortex showed hypo‐activation during hand tapping. During writing of the “8” task FRDA patients showed areas of higher activation in the right parietal and precentral cortex, globus pallidus, and putamen. Activation of right parietal cortex, anterior cingulum, globus pallidus, and putamen during writing of the “8” increased with severity of the neurological deficit. In conclusion fMRI demonstrates in FRDA a mixed pattern constituted by areas of decreased activation and areas of increased activation. The decreased activation in the primary motor cortex and cerebellum presumably reflects a regional neuronal damage, the decreased activation of the left thalamus and primary sensory cortex could be secondary to deafferentation phenomena, and the increased activation of right parietal cortex and striatum might have a possible compensatory significance. Hum Brain Mapp, 2012. © 2011 Wiley Periodicals, Inc
Large Scale 3D Clustering and Abstraction

Typically, a user can understand a clear textual description of a small amount of relational information. However, when dealing with large amounts of relational information, a vast amount of text quickly becomes cumbersome. Existing methods and techniques, for the visualization of relational information, tend to effectively deal with only relatively small data sets. Here we present methods to generate a hierarchical compound graph for the large 3D visualisation of relational information. These large compound graphs can effectively be drawn using a modified force-directed algorithm with horizon drawings.
Performance Analysis and Scheduling of Stochastic Fork-Join Jobs in a Multicomputer System

The authors model a parallel processing system comprising several homogeneous computers interconnected by a communication network. Jobs arriving to this system have a linear fork-join structure. Each fork of the job gives rise to a random number of tasks that can be processed independently on any of the computers. Since exact analysis of fork-join models is known to be intractable, the authors resort to obtaining analytical bounds to the mean job response time of the fork-join job. For jobs with a single fork-join and, probabilistic allocation of tasks of the job to the N processors, they obtain upper and lower bounds to the mean job response time. Upper bounds are obtained using the concept of associated random variables and are found to be a good approximation to the mean job response time. A simple lower bound is obtained by neglecting queueing delays. They also find two lower bounds that include queueing delays. For multiple fork-join jobs, they study an approximation based on associated random variables. Finally, two versions of the join-the-shortest-queue (JSQ) allocation policy (i.e., JSQ by batch and JSQ by task) are studied and compared, via simulations and diffusion limits. >