Miracle-dz/UniFAR-FTUs huggingface dataset:
Recognition of continuous persian speech using a medium-sized vocabulary speech corpus

Speech recognition in Persian (Farsi) has recently been addressed by a few native speaking researchers and some approaches to isolated word and phoneme recognition have been reported. A main bottleneck in this research field is the lack of a recognition-specific speech corpus. In this work, a phonetically balanced speech database of Persian has been modified and used in continuous speech recognition. A basic continuous speech recognizer using HMMs has been designed for this language and recognition tests have been performed. Using mixture-Gaussian monophone models, a word recognition rate of about 68% in no-grammar tests were obtained while word-pair grammar tests increased this rate to an unexpectedly high value of 99.5%. The reason is found to be the low grammar perplexity of the database which is not suitable for recognition applications. This obviates the need for a Persian speech corpus specifically designed for such tasks.
Characterization of CRISPR-Cas Systems in Clinical Klebsiella pneumoniae Isolates Uncovers Its Potential Association With Antibiotic Susceptibility

Prokaryotic CRISPR-Cas systems limit the acquisition of genetic elements and provide immunity against invasive bacteriophage. The characteristics of CRISPR-Cas systems in clinical Klebsiella pneumoniae isolates are still unknown. Here, 97 K. pneumoniae genomes retrieved from the Integrated Microbial Genomes & Microbiomes genome database and 176 clinical isolates obtained from patients with bloodstream (BSI, n = 87) or urinary tract infections (UTI, n = 89) in Taiwan, were used for analysis. Forty out of ninety-seven genomes (41.2%) had CRISPR-Cas systems identified by the combination of CRISPRFinder and cas1 gene sequence alignment. The phylogenetic trees revealed that CRISPR-Cas systems in K. pneumoniae were divided into two types (type I-E, 23; subtype I-E∗, 17) based on the sequences of Cas1 and Cas3 proteins and their location in the chromosome. The distribution of type I-E and I-E∗ CRISPR-Cas systems was associated with the multilocus sequence typing and the pulsed-field gel electrophoresis results. Importantly, no CRISPR-Cas system was identified in published genomes of clonal complex 258 isolates (ST11 and ST258), which comprise the largest multi-drug resistant K. pneumoniae clonal group worldwide. PCR with cas-specific primers showed that 30.7% (54/176) of the clinical isolates had a CRISPR-Cas system. Among clinical isolates, more type I-E CRISPR-Cas systems were found in UTI isolates (BSI, 5.7%; UTI, 11.2%), and subtype I-E∗ CRISPR-Cas systems were dominant in BSI isolates (BSI, 28.7%; UTI, 15.7%) (p = 0.042). Isolates which had subtype I-E∗ CRISPR-Cas system were more susceptible to ampicillin-sulbactam (p = 0.009), cefazolin (p = 0.016), cefuroxime (p = 0.039), and gentamicin (p = 0.012), compared to the CRISPR-negative isolates. The strains containing subtype I-E∗ CRISPR-Cas systems had decreased numbers of plasmids, prophage regions, and acquired antibiotic resistance genes in their published genomes. Here, we first revealed subtype I-E∗ CRISPR-Cas system in K. pneumoniae potentially interfering with the acquisition of phages and plasmids harboring antibiotic resistance determinants, and thus maintained these isolates susceptible to antibiotics.
Problem Definition : Maximizing Invariant Data Perturbation

Feature attribution methods, or saliency maps, are one of the most popular approaches for explaining the decisions of complex machine learning models such as deep neural networks. In this study, we propose a stochastic optimization approach for the perturbation-based feature attribution method. While the original optimization problem of the perturbation-based feature attribution is difficult to solve because of the complex constraints, we propose to reformulate the problem as the maximization of a differentiable function, which can be solved using gradient-based algorithms. In particular, stochastic optimization is well-suited for the proposed reformulation, and we can solve the problem using popular algorithms such as SGD, RMSProp, and Adam. The experiment on the image classification with VGG16 shows that the proposed method could identify relevant parts of the images effectively.
Application of Operational Research Techniques in Operating Room Scheduling Problems: Literature Overview

Increased healthcare costs are pushing hospitals to reduce costs and increase the quality of care. Operating rooms are the most important source of income and expense for hospitals. Therefore, the hospital management focuses on the effectiveness of schedules and plans. This study includes analyses of recent research on operating room scheduling and planning. Most studies in the literature, from 2000 to the present day, were evaluated according to patient characteristics, performance measures, solution techniques used in the research, the uncertainty of the problem, applicability of the research, and the planning strategy to be dealt within the solution. One hundred seventy studies were examined in detail, after scanning the Emerald, Science Direct, JSTOR, Springer, Taylor and Francis, and Google Scholar databases. To facilitate the identification of these studies, they are grouped according to the different criteria of concern and then, a detailed overview is presented.
A Structured Review of the Validity of BLEU

The BLEU metric has been widely used in NLP for over 15 years to evaluate NLP systems, especially in machine translation and natural language generation. I present a structured review of the evidence on whether BLEU is a valid evaluation technique—in other words, whether BLEU scores correlate with real-world utility and user-satisfaction of NLP systems; this review covers 284 correlations reported in 34 papers. Overall, the evidence supports using BLEU for diagnostic evaluation of MT systems (which is what it was originally proposed for), but does not support using BLEU outside of MT, for evaluation of individual texts, or for scientific hypothesis testing.
Leveraging Catalog Knowledge Graphs for Query Attribute Identification in E-Commerce Sites

Millions of people use online e-commerce platforms to search and buy products. Identifying attributes in a query is a critical component in connecting users to relevant items. However, in many cases, the queries have multiple attributes, and some of them will be in conflict with each other. For example, the query "maroon 5 dvds" has two candidate attributes, the color "maroon" or the band "maroon 5", where only one of the attributes can be present. In this paper, we address the problem of resolving conflicting attributes in e-commerce queries. A challenge in this problem is that knowledge bases like Wikipedia that are used to understand web queries are not focused on the e-commerce domain. E-commerce search engines, however, have access to the catalog which contains detailed information about the items and its attributes. We propose a framework that constructs knowledge graphs from catalog to resolve conflicting attributes in e-commerce queries. Our experiments on real-world queries on e-commerce platforms demonstrate that resolving conflicting attributes by leveraging catalog information significantly improves attribute identification, and also gives out more relevant search results.
Responses of the Differentiated Intestinal Epithelial Cell Line Caco-2 to Infection With the Giardia intestinalis GS Isolate

Giardia intestinalis is a parasitic protist that causes diarrhea in humans, affecting mainly children of the developing world, elderly and immunocompromised individuals. Humans are infected by two major Giardia assemblages (i.e. genetic subtypes), A and B, with the latter being the most common. So far, there is little information on molecular or cellular changes during infections with assemblage B. Here, we used RNA sequencing to study transcriptional changes in Caco-2 intestinal epithelial cells (IECs) co-incubated with assemblage B (GS isolate) trophozoites for 1.5, 3, and 4.5 h. We aimed to identify early molecular events associated with the establishment of infection and followed cellular protein changes up to 10 h. IEC transcriptomes showed a dominance of immediate early response genes which was sustained across all time points. Transcription of inflammatory cytokines (e.g., cxcl1-3, ccl2, 1l1a, and il1b) peaked at 1.5 and 3 h of infection. Compared to co-incubation with assemblage A Giardia, we identified the induction of novel cytokines (cxcl8, cxcl10, csf1, cx3cl1, il12a, il11) and showed that inflammatory signaling is mediated by Erk1/2 phosphorylation (mitogen activated protein kinase, MAPK), nuclear factor kappa B (NFκB) and adaptor protein-1 (AP-1). We also showed that GS trophozoites attenuate P38 (MAPK) phosphorylation in IECs. Low amounts of IL-8, CXCL1 and CCL20 proteins were measured in the interaction medium, which was attributed to cytokine degradation by trophozoite secreted proteases. Based on the transcriptome, the decay of cytokines mRNA mediated by zinc finger protein 36 might be another mechanism controlling cytokine levels at later time points. IEC transcriptomes suggested homeostatic responses to counter oxidative stress, glucose starvation, and disturbances in amino acid and lipid metabolism. A large group of differentially transcribed genes were associated with cell cycle arrest and induction of apoptosis, which was validated at protein level. IEC transcriptomes also suggested changes in tight junction's integrity, microvilli structure and the extracellular mucin layer. This is the first study to illuminate transcriptional and protein regulatory events underlying IECs responses and pathogenesis during Giardia assemblage B infection. It highlights differences compared to assemblage A infections which might account for the differences observed in human infections with the two assemblages.
Multi-task dialog act and sentiment recognition on Mastodon

Because of license restrictions, it often becomes impossible to strictly reproduce most research results on Twitter data already a few months after the creation of the corpus. This situation worsened gradually as time passes and tweets become inaccessible. This is a critical issue for reproducible and accountable research on social media. We partly solve this challenge by annotating a new Twitter-like corpus from an alternative large social medium with licenses that are compatible with reproducible experiments: Mastodon. We manually annotate both dialogues and sentiments on this corpus, and train a multi-task hierarchical recurrent network on joint sentiment and dialog act recognition. We experimentally demonstrate that transfer learning may be efficiently achieved between both tasks, and further analyze some specific correlations between sentiments and dialogues on social media. Both the annotated corpus and deep network are released with an open-source license.
Analysis Dictionary Learning Based Classification: Structure for Robustness

A discriminative structured analysis dictionary is proposed for the classification task. A structure of the union of subspaces (UoS) is integrated into the conventional analysis dictionary learning to enhance the capability of discrimination. A simple classifier is also simultaneously included into the formulated function to ensure a more complete consistent classification. The solution of the algorithm is efficiently obtained by the linearized alternating direction method of multipliers. Moreover, a distributed structured analysis dictionary learning is also presented to address large-scale datasets. It can group-(class-) independently train the structured analysis dictionaries by different machines/cores/threads, and therefore avoid a high computational cost. A consensus structured analysis dictionary and a global classifier are jointly learned in the distributed approach to safeguard the discriminative power and the efficiency of classification. Experiments demonstrate that our method achieves a comparable or better performance than the state-of-the-art algorithms in a variety of visual classification tasks. In addition, the training and testing computational complexity are also greatly reduced.
Dynamic large-scale network synchronization from perception to action

Abstract Sensory-guided actions entail the processing of sensory information, generation of perceptual decisions, and the generation of appropriate actions. Neuronal activity underlying these processes is distributed into sensory, fronto-parietal, and motor brain areas, respectively. How the neuronal processing is coordinated across these brain areas to support functions from perception to action remains unknown. We investigated whether phase synchronization in large-scale networks coordinate these processes. We recorded human cortical activity with magnetoencephalography (MEG) during a task in which weak somatosensory stimuli remained unperceived or were perceived. We then assessed dynamic evolution of phase synchronization in large-scale networks from source-reconstructed MEG data by using advanced analysis approaches combined with graph theory. Here we show that perceiving and reporting of weak somatosensory stimuli is correlated with sustained strengthening of large-scale synchrony concurrently in delta/theta (3–7 Hz) and gamma (40–60 Hz) frequency bands. In a data-driven network localization, we found this synchronization to dynamically connect the task-relevant, that is, the fronto-parietal, sensory, and motor systems. The strength and temporal pattern of interareal synchronization were also correlated with the response times. These data thus show that key brain areas underlying perception, decision-making, and actions are transiently connected by large-scale dynamic phase synchronization in the delta/theta and gamma bands.
Complementing Cancer Metastasis

Complement is an effector of innate immunity and a bridge connecting innate immunity and subsequent adaptive immune responses. It is essential for protection against infections and for orchestrating inflammatory responses. Recent studies have also demonstrated contribution of the complement system to several homeostatic processes that are traditionally not considered to be involved in immunity. Thus, complement regulates homeostasis and immunity. However, dysregulation of this system contributes to several pathologies including inflammatory and autoimmune diseases. Unexpectedly, studies of the last decade have also revealed that complement promotes cancer progression. Since the initial discovery of tumor promoting role of complement, numerous preclinical and clinical studies demonstrated contribution of several complement components to regulation of tumor growth through their direct interactions with the corresponding receptors on tumor cells or through suppression of antitumor immunity. Most of this work, however, focused on a role of complement in regulating growth of primary tumors. Only recently, a few studies showed that complement promotes cancer metastasis through its contribution to epithelial-to-mesenchymal transition and the premetastatic niche. This latter work has shown that complement activation and generation of complement effectors including C5a occur in organs that are target for metastasis prior to arrival of the very first tumor cells. C5a through its interactions with C5a receptor 1 inhibits antitumor immunity by activating and recruiting immunosuppressive cells from the bone marrow to the premetastatic niche and by regulating function and self-renewal of pulmonary tissue-resident alveolar macrophages. These new advancements provide additional evidence for multifaceted functions of complement in cancer.
AI Reasoning Systems: PAC and Applied Methods

Learning and logic are distinct and remarkable approaches to prediction. Machine learning has experienced a surge in popularity because it is robust to noise and achieves high performance; however, ML experiences many issues with knowledge transfer and extrapolation. In contrast, logic is easily intepreted, and logical rules are easy to chain and transfer between systems; however, inductive logic is brittle to noise. We then explore the premise of combining learning with inductive logic into AI Reasoning Systems. Specifically, we summarize findings from PAC learning (conceptual graphs, robust logics, knowledge infusion) and deep learning (DSRL, $\partial$ILP, DeepLogic) by reproducing proofs of tractability, presenting algorithms in pseudocode, highlighting results, and synthesizing between fields. We conclude with suggestions for integrated models by combining the modules listed above and with a list of unsolved (likely intractable) problems.
Convergence Theorems for Hybrid Learning Rules

Several heuristic hybrid algorithms for feedforward neural networks, which combine unsupervised learning of hidden units with supervised learning of output units [see, e.g., Moody and Darken (1989); Nowlan (1990); Poggio and Girosi (1990); Benaim and Tomasini (1992); Benaim (1994)] have recently been proposed. The purpose of this note is to present some convergence theorems for such learning rules.
Resting-state brain network features associated with short-term skill learning ability in humans and the influence of N-methyl-d-aspartate receptor antagonism

Abstract Graph theoretical functional magnetic resonance imaging (fMRI) studies have demonstrated that brain networks reorganize significantly during motor skill acquisition, yet the associations between motor learning ability, brain network features, and the underlying biological mechanisms remain unclear. In the current study, we applied a visually guided sequential pinch force learning task and graph theoretical analyses to investigate the associations between short-term motor learning ability and resting-state brain network metrics in 60 healthy subjects. We further probed the test-retest reliability (n = 26) and potential effects of the N-methyl-d-aspartate (NMDA) antagonist ketamine (n = 19) in independent healthy volunteers. Our results show that the improvement of motor performance after short-term training was positively correlated with small-worldness (p = 0.032) and global efficiency (p = 0.025), whereas negatively correlated with characteristic path length (p = 0.014) and transitivity (p = 0.025). In addition, using network-based statistics (NBS), we identified a learning ability–associated (p = 0.037) and ketamine-susceptible (p = 0.027) cerebellar-cortical network with fair to good reliability (intraclass correlation coefficient [ICC] > 0.7) and higher functional connectivity in better learners. Our results provide new evidence for the association of intrinsic brain network features with motor learning and suggest a role of NMDA-related glutamatergic processes in learning-associated subnetworks.
Fission yeast telosomes: non-canonical histone-containing chromatin structures dependent on shelterin and RNA

Abstract Despite the prime importance of telomeres in chromosome stability, significant mysteries surround the architecture of telomeric chromatin. Through micrococcal nuclease mapping, we show that fission yeast chromosome ends are assembled into distinct protected structures (‘telosomes’) encompassing the telomeric DNA repeats and over half a kilobase of subtelomeric DNA. Telosome formation depends on the conserved telomeric proteins Taz1 and Rap1, and surprisingly, RNA. Although yeast telomeres have long been thought to be free of histones, we show that this is not the case; telomere repeats contain histones. While telomeric histone H3 bears the heterochromatic lys9-methyl mark, we show that this mark is dispensable for telosome formation. Therefore, telomeric chromatin is organized at an architectural level, in which telomere-binding proteins and RNAs impose a unique nucleosome arrangement, and a second level, in which histone modifications are superimposed upon the higher order architecture.
Numerical Evaluation and Prediction of Porous Implant Design and Flow Performance

Porous structure has been widely acknowledged as important factor for mass transfer and tissue regeneration. This study investigates effect of aimed-control design on mass transfer and tissue regeneration of porous implant with regular unit cell. Two shapes of unit cells (Octet truss, and Rhombic dodecahedron) were selected, which have similar symmetrical structure and are commonly used in practice. Through parametric design, porous scaffolds with the strut sizes of φ 0.5, 0.7, 0.9, and 1.1mm were created, respectively. Then using fluid flow simulation method, flow velocity, permeability, and shear stress which could reflect the properties of mass transfer and tissue regeneration were compared and evaluated, and the relationships between porous structure's physical parameters and flow performance were studied. Results demonstrated that unit cell shape and strut size greatly determine and influence other physical parameters and flow performances of porous implant. With the increasing of strut size, pore size and porosity linearly decrease, but the volume, surface area, and specific surface area increased. Importantly, implant with smaller strut size resulted in smaller flow velocity directly but greater permeability and more appropriate shear stress, which should be beneficial to cell attachment and proliferation. This study confirmed that porous implant with different unit cell shows different performances of mass transfer and tissue regeneration, and unit cell shape and strut size play vital roles in the control design. These findings could facilitate the quantitative assessment and optimization of the porous implant.
Antecedents and Consequences of Perceiving a Source as Biased

ii Acknowledgments. iii Vita . iv List of Tables . vii List of Figures . ix Chapter 1: Introduction . 1 Chapter 2: Study 1 . 8 Chapter 3: Study 2 . 22 Chapter 4: Study 3 . 31 Chapter 5: Study 4 . 44 Chapter 6: General Discussion. 50 References . 58 Appendix A: Political candidate messages . 64
Could Prolonged Usage of GPS Navigation Implemented in Augmented Reality Smart Glasses Affect Hippocampal Functional Connectivity?

Background Augmented reality (AR) glasses with GPS navigation represent the rapidly evolving technology which spares (and externalizes) navigational capacities. Regarding the expected everyday usage of this device, its impact on neuroplastic brain changes and navigation abilities should be evaluated. Aims This study aimed to assess possible changes in functional connectivity (FC) of hippocampus and other brain regions involved in spatial navigation. Methods Thirty-three healthy participants completed two resting state functional magnetic resonance imaging (rsfMRI) measurements at the baseline and after 3 months. For this period, the experimental group (n = 17) has had used AR device (Vuzix M100) with incorporated GPS guidance system during navigation in real world. Participants from the control group (n = 16) have not used any GPS device while navigating during walking. The rsfMRI FC of right and left hippocampi was analyzed using a seed-driven approach. Virtual city task was used to test navigational abilities both before and after the usage of AR device. Results We identified strong functional coupling of right and left hippocampi at the baseline (p < 0.05, FDR corrected). Mild changes in bilateral hippocampal FC (p < 0.05, FDR uncorrected) were observed in both assessed groups mainly between the bilateral hippocampi and between each hippocampus and temporal regions and cerebellum. However, the experimental group showed FC decrease after three months of using GPS navigation implemented in AR glasses in contrast to FC increase in the control group without such intervention. Importantly, no effect of intervention on navigational abilities was observed. Discussion Our observation supports the assumption that externalization of spatial navigation to technological device (GPS in AR glasses) can decrease the functional coupling between hippocampus and associated brain regions. Considering some limitations of the present study, further studies should elucidate the mechanism of the observed changes and their impact on cognitive abilities.