Miracle-dz/UniFAR-FTUs huggingface dataset:
Dimensions of phonological stress

Dimensions of phonological stress is a collection of eleven contributions on the analysis and acquisition of linguistic prominence, along with a short introduction. * Although its contributors were drawn from presenters at the Conference on Stress and Accent at the University of Delaware in 2012, its genre is not conference proceedings: the chapters are mostly journal-length (averaging 29 pages) and journal-quality, having undergone peer review by two or three anonymous referees as well as the three editors. The success of the conference and vetting is evident not just from the consistently high level of argumentation but also from authors’ frequent written responses to referees and other members of the project. One gets the impression of a more collective effort than is typical of collections. Dimensions of phonological stress follows upon a similarly conceived volume, van der Hulst (2014), though each stands on its own. These volumes contain some of the most important articles on stress in the last decade. Indeed, since only a few articles on stress appear in major journals each year, eleven such papers in one place is significant. Any linguist interested in metrical phenomena should have this book on hand. While it is geared towards specialists, several chapters begin with accessible summaries of their areas. For example, Keij & Kager summarise fifty years of protocols for investigating rhythmic preferences in infants, Dresher gives a crash course on grid operations, and so forth. Unlike the denser articles one might find in proceedings, one is here ushered more kindly into less familiar territory. Most of the contributors, like the editors, are well-known senior scholars, with only a few junior scholars joining their ranks. The editors are also known for their collaboration on StressTyp2, a typological database of stress patterns. Nevertheless, StressTyp2 receives no mention or use after the first page. This is a missed opportunity, given how excellent a resource it is (see van der Hulst 2014 for more background on the project). In terms of the coverage, most of the book concerns theoretical phonology, and generally follows mainstream conventions such as Optimality Theory1 and metrical grids parsed into feet. Acquisition also features prominently, being the focus of four chapters, if one construes it broadly to include learnability and infant-directed speech. Beyond these acquisition chapters, a few chapters are empirically oriented, using phonetic or experimental evidence to establish
S0031182017001846jra 1059..1064

Cathelicidins are antimicrobial peptides produced by humans and animals in response to various pathogenic microbes. Crotalicidin (Ctn), a cathelicidin-related vipericidin from the South American Crotalus durissus terrificus rattlesnake’s venom gland, and its fragments have demonstrated antimicrobial and antifungal activity, similarly to human cathelicidin LL-37. In order to provide templates for the development of modern trypanocidal agents, the present study evaluated the antichagasic effect of these four peptides (Ctn, Ctn[1-14], Ctn[15-34] and LL-37). Herein, Ctn and short derived peptides were tested against the epimastigote, trypomastigote and amastigote forms of Trypanosoma cruzi Y strain (benznidazole-resistant strain) and cytotoxicity in mammalian cells was evaluated against LLC-MK2 lineage cells. Ctn inhibited all T. cruzi developmental forms, including amastigotes, which is implicated in the burden of infection in the chronic phase of Chagas disease. Moreover, Ctn showed a high selective index against trypomastigote forms (>200). Ctn induced cell death in T. cruzi through necrosis, as determined by flow cytometry analyses with specific molecular probes and morphological alterations, such as loss of membrane integrity and cell shrinkage, as observed through scanning electron microscopy. Overall, Ctn seems to be a promising template for the development of antichagasic agents.
Humanoid Robot as Assistant Tutor for Autistic Children

This paper aims to propose an interactive tutoring environment, using humanoid robots, to support children with autism disorders. Statistics indicate an increase in the use of robotics (more than 5000 robots) in more than 50 countries for teaching and learning purposes. Therefore, the process of choosing the modern teaching method that appropriates to the level of the student is a crucial matter. The proposed interactive teaching framework consists of three phases, including preparing the suitable class materials, programming the NAO robot, and evaluating the performance of the teaching methods on students' communication and academic skills. Besides, it implements a multiple-scenario approach to developing lesson plans and verifying student acknowledgments to lessons and updating the curriculum as necessary. The robot is used as an assistant tool to help students read stories, spell words, and correct students’ answers to a particular Math question. The results show that the use of robots as tutors help students learn difficult topics with their native language easily and enjoy educational gaming activities. It also helped teachers to create new lessons by browsing online teaching resources and adding them to the lesson plan based on the course profile schedule and students' feedback. Also, the participants indicated that using a tutor robot is offering no bias against students' gender, race, socioeconomic status, personal preference, or other considerations.
Dissemination of antibiotic resistance genes in a Nile Tilapia cage culture

Antibiotic resistance is one of the world most threatening health problems. It results from unnecessary and inappropriate use of antibiotics, which to a greater part is caused by excessive amounts of antibiotics being used in husbandry, mostly for preventive measures. The effects of antibiotics on bacterial resistances in the environment is a matter of increasing concern and is largely unknown. In addition, little information is available about how the use of antibiotics in farming systems can be managed in a manner that has negligible effects on the natural microbiome of the environment. In this study, we established the dynamics of antibiotic resistance in a caging fish farm in the Taasan-Bangpla canal, Kamphaeng Saen, in January 2018. Water samples were collected in the fishing-cage, as well as at upstream and downstream sites, for a comparison of the occurrence of antibiotic resistance genes through metagenome analysis. The results demonstrated that the antibiotic resistance genes in the downstream sample were different and occurred at lower frequency than those in the fish cage, suggesting that the fish farming did not spread any sources of antibiotic resistance to the surface of the canal. Our observations were explained by the high-water flow in the canal, 36.47 x 10 m/month, during the period of our study and/or the absence of active antibiotics residues downstream. Future studies will require additional analyses under various conditions and different periods in the year.
FACTORS ON PHYTOCHEMICAL ASSISTED BIOSYNTHESIS OF AU-AG BIMETALLIC NANOPARTICLES : A SHORT

nanotechnology because it does not require hazardous chemicals, solvents, and high energy, and not the production of toxic chemicals as byproducts which can harm living beings as well as the environment along with the fascinating properties which can be inculcated in various fields. This present work elaborately discussed the efficacy of photochemical like phenolic acids, Flavones, Alkaloids, terpenes, carbohydrates as a reducing and stabilizing agents for the biosynthesis of bmnps and influencing factors such as Ph of plant extract, the molar ratio of metal ions, concentration of plant extract, reaction temperature, and reaction time on the biosynthesis of bimetallic (Ag-Au) nanoparticles using the plant extract. This study will help to predict optimal conditions for the biosynthesis of bimetallic nanoparticles. ORIGINAL RESEARCH PAPER Chemistry INFLUENCING FACTORS ON PHYTOCHEMICAL ASSISTED BIOSYNTHESIS OF AU-AG BIMETALLIC NANOPARTICLES: A SHORT REVIEW
Discrete immune response signature to SARS-CoV-2 mRNA vaccination versus infection

Both SARS-CoV-2 infection and vaccination elicit potent immune responses. A number of studies have described immune responses to SARS-CoV-2 infection. However, beyond antibody production, immune responses to COVID-19 vaccines remain largely uncharacterized. Here, we performed multimodal single-cell sequencing on peripheral blood of patients with acute COVID- 19 and healthy volunteers before and after receiving the SARS-CoV-2 BNT162b2 mRNA vaccine to compare the immune responses elicited by the virus and by this vaccine. Phenotypic and transcriptional profiling of immune cells, coupled with reconstruction of the B and T cell antigen receptor rearrangement of individual lymphocytes, enabled us to characterize and compare the host responses to the virus and to defined viral antigens. While both infection and vaccination induced robust innate and adaptive immune responses, our analysis revealed significant qualitative differences between the two types of immune challenges. In COVID-19 patients, immune responses were characterized by a highly augmented interferon response which was largely absent in vaccine recipients. Increased interferon signaling likely contributed to the observed dramatic upregulation of cytotoxic genes in the peripheral T cells and innate-like lymphocytes in patients but not in immunized subjects. Analysis of B and T cell receptor repertoires revealed that while the majority of clonal B and T cells in COVID-19 patients were effector cells, in vaccine recipients clonally expanded cells were primarily circulating memory cells. Importantly, the divergence in immune subsets engaged, the transcriptional differences in key immune populations, and the differences in maturation of adaptive immune cells revealed by our analysis have far-ranging implications for immunity to this novel pathogen.
Bias-Aware Loss for Training Image and Speech Quality Prediction Models from Multiple Datasets

The ground truth used for training image, video, or speech quality prediction models is based on the Mean Opinion Scores (MOS) obtained from subjective experiments. Usually, it is necessary to conduct multiple experiments, mostly with different test participants, to obtain enough data to train quality models based on machine learning. Each of these experiments is subject to an experiment-specific bias, where the rating of the same file may be substantially different in two experiments (e.g. depending on the overall quality distribution). These different ratings for the same distortion levels confuse neural networks during training and lead to lower performance. To overcome this problem, we propose a bias-aware loss function that estimates each dataset's biases during training with a linear function and considers it while optimising the network weights. We prove the efficiency of the proposed method by training and validating quality prediction models on synthetic and subjective image and speech quality datasets.
Bipedal Walking on Constrained Footholds: Momentum Regulation via Vertical COM Control

This paper presents an online walking synthesis methodology to enable dynamic and stable walking on constrained footholds for underactuated bipedal robots. Our approach modulates the change of angular momentum about the foot-ground contact pivot at discrete impact using pre-impact vertical center of mass (COM) velocity. To this end, we utilize the underactuated Linear Inverted Pendulum (LIP) model for approximating the underactuated walking dynamics to provide the desired post-impact angular momentum for each step. Desired outputs are constructed via online optimization combined with closed-form polynomials and tracked via a quadratic program (QP) based controller. This method is demonstrated on two robots, AMBER and 3D Cassie, for which stable walking behaviors with constrained footholds are realized on flat ground, stairs, and randomly located stepping stones.
A Meta-Learning Approach for Medical Image Registration

Non-rigid registration is a necessary but challenging task in medical imaging studies. Recently, unsupervised registration models have shown good performance, but they often require a large-scale training dataset and long training time. Therefore, in real world application where only dozens to hundreds of image pairs are available, existing models cannot be practically used. To address these limitations, we propose a novel unsupervised registration model which is integrated with a gradient-based meta learning framework. In particular, we train a meta learner which finds an optimal initialization point of parameters by utilizing various registration datasets. To quickly adapt to diverse tasks, the meta learner was updated to get close to the center of parameters which are fine-tuned for each registration task. Thereby, our model can adapt to unseen domain tasks via a short fine-tuning process and perform accurate registration. To verify the superiority of our model, we train the model using various types of medical data sets such as retinal Optical Coherence Tomography Angiography (OCTA) for choroidal vasculature, body CT scans, and brain MRI scans and then test it on registration of unseen retinal OCTA for Superficial Capillary Plexus (SCP). In our experiments, the proposed model obtained significantly improved performance in terms of accuracy and training time compared to other registration models.
Chandra, NuSTAR, and Optical Observations of the Cataclysmic Variables IGR J17528-2022 and IGR J20063+3641

We report on Chandra, NuSTAR, and MDM observations of two International Gamma-ray Astrophysics Laboratory (INTEGRAL) sources, namely IGR J17528−2022 and IGR J20063+3641. IGR J17528−2022 is an unidentified INTEGRAL source, while IGR J20063+3641 was recently identified as a magnetic cataclysmic variable (mCV) by Halpern et al. The Chandra observation of IGR J17528−2022 has allowed us to locate the optical counterpart to the source and to obtain its optical spectrum, which shows a strong Hα emission line. The optical spectrum and flickering observed in the optical time-series photometry in combination with the X-ray spectrum, which is well fit by an absorbed partially covered thermal bremsstrahlung model, suggest that this source is a strong mCV candidate. The X-ray observations of IGR J20063+3641 reveal a clear modulation with a period of 172.46 ± 0.01 s, which we attribute to the white dwarf spin period. Additional MDM spectroscopy of the source has also allowed for a clear determination of the orbital period at 0.731 ± 0.015 days. The X-ray spectrum of this source is also well fit by an absorbed partially covered thermal bremsstrahlung model. The X-ray spectrum, spin periodicity, and orbital periodicity allow this source to be further classified as an intermediate polar.
A q-binomial extension of the CRR asset pricing model

Abstract We propose an extension of the Cox-Ross-Rubinstein (CRR) model based on q-binomial (or Kemp) random walks, with application to default with logistic failure rates. This model allows us to consider time-dependent switching probabilities varying according to a trend parameter on a non-self-similar binomial tree. In particular, it includes tilt and stretch parameters that control increment sizes. Option pricing formulas are written using q-binomial coefficients, and we study the convergence of this model to a Black-Scholes type formula in continuous time. A convergence rate of order is obtained.
Causal-TGAN: Generating Tabular Data Using Causal Generative Adversarial Networks

Synthetic data generation becomes prevalent as a solution to privacy leakage and data shortage. Generative models are designed to generate a realistic synthetic dataset, which can precisely express the data distribution for the real dataset. The generative adversarial networks (GAN), which gain great success in the computer vision fields, are doubtlessly used for synthetic data generation. Though there are prior works that have demonstrated great progress, most of them learn the correlations in the data distributions rather than the true processes in which the datasets are naturally generated. Correlation is not reliable for it is a statistical technique that only tells linear dependencies and is easily affected by the dataset's bias. Causality, which encodes all underlying factors of how the real data be naturally generated, is more reliable than correlation. In this work, we propose a causal model named Causal Tabular Generative Neural Network (Causal-TGAN) to generate synthetic tabular data using the tabular data's causal information. Extensive experiments on both simulated datasets and real datasets demonstrate the better performance of our method when given the true causal graph and a comparable performance when using the estimated causal graph.
Searching with Opponent-Awareness

We propose Searching with Opponent-Awareness (SOA), an approach to leverage opponent-aware planning without explicit or a priori opponent models for improving performance and social welfare in multi-agent systems. To this end, we develop an opponent-aware MCTS scheme using multi-armed bandits based on Learning with Opponent-Learning Awareness (LOLA) and compare its effectiveness with other bandits, including UCB1. Our evaluations include several different settings and show the benefits of SOA are especially evident with increasing number of agents.
Predicting Human Trajectories by Learning and Matching Patterns

As more and more robots are envisioned to cooperate with humans sharing the same space, it is desired for robots to be able to predict others’ trajectories to navigate in a safe and self-explanatory way. We propose a Convolutional Neural Network-based approach to learn, detect, and extract patterns in sequential trajectory data, known here as Social Pattern Extraction Convolution (Social-PEC). A set of experiments carried out on the human trajectory prediction problem shows that our model performs comparably to the state of the art and outperforms in some cases. More importantly, the proposed approach unveils the obscurity in the previous use of a pooling layer, presenting a way to intuitively explain the decision-making process.
DNA Binding Specificity of Mu Transcription Factor C and Crystallization of C : DNA Complex

The lytic cycle of phage Mu is regulated by a transcriptional cascade consisting of early, middle and late transcription. The Mor protein is an activator of the middle promoter Pm and is encoded by the last gene of the early transcript. The C protein is an activator of the four late promoters Plys, PI, PP, and Pmom and is expressed from the middle transcript. Both Mor and C proteins bind an imperfect dyad-symmetry element just upstream and overlapping the –35 region of Pm and Plys respectively. The main aims of this study was, (1) To understand the binding specificity of C and determine a possible consensus sequence for C binding, and (2) To crystallize the C : DNA complex as a first step towards structure determination. In previous work, single base substitution mutations in Plys identified bases and positions important for C binding and activation. To get a consensus sequence for C binding, we tested additional candidate mutations within and flanking the C binding sequence. Wild-type C protein was used in gel mobility shift assays with annealed oligonucleotides containing mutations, insertions and deletions. The assay showed that, (1) mutation in positions –53, –52 and –32 did not affect C binding, (2) mutations flanking the IR spacer (–40, –41, –46, –47) influence C binding, and (3) insertion or deletion of a single base pair in the IR spacer abolished C binding. Mor and C proteins are the founding members of a new class of transcription factors. The Mor structure revealed that it has a classical DNA-binding HTH motif and a dimerization domain. Based on the structure it has been proposed that Mor has to undergo conformational changes to bind DNA. Modelling of C based on the Mor
FIERY: Future Instance Prediction in Bird’s-Eye View from Surround Monocular Cameras

Driving requires interacting with road agents and predicting their future behaviour in order to navigate safely. We present FIERY: a probabilistic future prediction model in bird’s-eye view from monocular cameras. Our model predicts future instance segmentation and motion of dynamic agents that can be transformed into non-parametric future trajectories. Our approach combines the perception, sensor fusion and prediction components of a traditional autonomous driving stack by estimating bird’s-eye-view prediction directly from surround RGB monocular camera inputs. FIERY learns to model the inherent stochastic nature of the future solely from camera driving data in an end-to-end manner, without relying on HD maps, and predicts multimodal future trajectories. We show that our model outperforms previous prediction baselines on the NuScenes and Lyft datasets. The code and trained models are available at https://github.com/wayveai/fiery.
Open and Anisotropic Soft Regions in a Model Polymer Glass

The vibrational dynamics of a model polymer glass is studied by Molecular Dynamics simulations. The focus is on the “soft” monomers with high participation to the lower-frequency vibrational modes contributing to the thermodynamic anomalies of glasses. To better evidence their role, the threshold to qualify monomers as soft is made severe, allowing for the use of systems with limited size. A marked tendency of soft monomers to form quasi-local clusters involving up to 15 monomers is evidenced. Each chain contributes to a cluster up to about three monomers and a single cluster involves a monomer belonging to about 2–3 chains. Clusters with monomers belonging to a single chain are rare. The open and tenuous character of the clusters is revealed by their fractal dimension df<2. The inertia tensor of the soft clusters evidences their strong anisotropy in shape and remarkable linear correlation of the two largest eigenvalues. Owing to the limited size of the system, finite-size effects, as well as dependence of the results on the adopted polymer length, cannot be ruled out.
Identifying botnet IP address clusters using natural language processing techniques on honeypot command logs

Computer security has been plagued by increasing formidable, dynamic, hard-to-detect, hard-to-predict, and hard-to-characterize hacking techniques. Such techniques are very often deployed in self-propagating worms capable of automatically infecting vulnerable computer systems and then building large bot networks, which are then used to launch coordinated attacks on designated targets. In this work, we investigate novel applications of Natural Language Processing (NLP) methods to detect and correlate botnet behaviors through the analysis of honeypot data. In our approach we take observed behaviors in shell commands issued by intruders during captured internet sessions and reduce them to collections of stochastic processes that are, in turn, processed with machine learning techniques to build classifiers and predictors. Our technique results in a new ability to cluster botnet source IP address even in the face of their desire to obfuscate their penetration attempts through rapid or random permutation techniques.
Modeling Event Plausibility with Consistent Conceptual Abstraction

Understanding natural language requires common sense, one aspect of which is the ability to discern the plausibility of events. While distributional models—most recently pre-trained, Transformer language models—have demonstrated improvements in modeling event plausibility, their performance still falls short of humans’. In this work, we show that Transformer-based plausibility models are markedly inconsistent across the conceptual classes of a lexical hierarchy, inferring that “a person breathing” is plausible while “a dentist breathing” is not, for example. We find this inconsistency persists even when models are softly injected with lexical knowledge, and we present a simple post-hoc method of forcing model consistency that improves correlation with human plausibility judgements.