Miracle-dz/UniFAR-FTUs huggingface dataset:
Altered Fast Synaptic Transmission in an in vitro Mouse Model of Epileptic Encephalopathy

Epileptic encephalopathies (EEs) are severe seizure disorders that occur in infants and young children, characterized by developmental delay, and cognitive decline. Recent efforts have identified a wide variety of genetic variants that cause EEs. Among these, variants in the DNM1 gene have emerged as definitive causes of EEs, including Infantile Spasms and Lennox-Gastaut syndrome. A mouse model of Dnm1-associated EE, known as “Fitful” (Dnm1Ftfl), recapitulates key features of the disease, including spontaneous seizures, developmental delay, and neuronal degeneration. Previous work showed that Dnm1 is a key regulator of synaptic vesicle (SV) endocytosis and synaptic transmission, and suggested that inhibitory neurotransmission is more reliant on Dnm1 function than excitatory transmission. The Dnm1Ftfl variant is thought to encode a dominant negative Dnm1 protein, however, the effects of the Dnm1Ftfl variant on synaptic transmission are largely unknown. To better understand these synaptic effects, we recorded from pairs of cultured mouse cortical neurons and characterized all four major connection types (E-E, EI, I-E, I-I). Miniature and spontaneous EPSCs and IPSCs were larger, but less frequent, at all Dnm1Ftfl synaptic types, and Dnm1Ftfl/Ftfl neurons had reduced expression of excitatory and inhibitory SV markers. Baseline evoked transmission, however, was reduced only at inhibitory synapses onto excitatory neurons, due to a smaller pool of releasable SVs. In addition to these synaptic alterations, Dnm1Ftfl neurons degenerated after two weeks in vitro, even though their activity levels were not elevated, suggesting that Dnm1Ftfl may impair synaptic transmission and neuronal health through distinct mechanisms.
Vitamin D concentration in health care and sport performance

The molecular structure of Vitamin D is closely allied to that of classical steroids such as cholesterol. Technically Vitamin D is a seco-steroid hormone. Vitamin D3 (cholecalciferol) is normally produced by exposure to sunlight of the precursor (7-dehydrocholesterol), present in the skin. Vitamin D supplementation vary between 200 IU to 1000 IU. Serum levels of 25(OH)D are generally consider as a indicator of Vitamin D status. Vitamin D plays an essential role in healthcare, related not only with developing and maintaining a healthy skeleton. Its adequate supplementation reduce even the risk of caries and oral infections. Furthermore Vitamin D as a steroid hormone, modulates many gene transcription and has anti-inflammatory and cardiovascular protective effect. Uncertainly low serum levels of 25(OH)D are associated with misbalance in lipid profile and dyslipidemia, sacropenia and muscle weakness. Its insufficiency is also a risk factor for enhanced reception of pain, risk of type 2 diabetes, and often falls occurrences. Adequate Vitamin D status is protective against musculoskeletal disorders, infection disease, depression, diabetes mellitus, autoimmune diseases and neurocognitive dysfunctions. In sport activities and athletic population adequate serum levels of 25(OH)D increase muscle strength, and physical performance, and should be monitored.
Interoception, Trait Anxiety, and the Gut Microbiome: A Cognitive and Physiological Model

Trait anxiety is characterized as a constant and often subliminal state that persists during daily life. Interoception is the perception of internal states and sensations, including from the autonomic nervous system. This review aims to develop a predictive model to explain the emergence, manifestations, and maintenance of trait anxiety. The model begins with the assumption that anxiety states arise from active interoceptive inference. The subsequent activation of autonomic responses results from aversive sensory encounters. A cognitive model is proposed for trait anxiety that includes the aversive sensory components from interoception, exteroception, and proprioception. A further component of the hypothesis is that repeated exposure to subliminal anxiety-evoking sensory elements can lead to an overgeneralization of this response to other inputs that are generally non-aversive. Increased uncertainty may result when predicting the sensory environment, resulting in arbitrary interoceptive anxiety responses that may be due to unjustifiable causes. Arbitrary successful or unsuccessful matching of predictions and responses reduces the individual’s confidence to maintain the anxiety trait. In this review, the application of the proposed model is illustrated using gut microbial dysbiosis or imbalance of the gut microbiome.
Belief Convergence under Misspecified Learning: A Martingale Approach

We present an approach to analyze learning outcomes in a broad class of misspecified environments, spanning both single-agent and social learning. We introduce a novel “prediction accuracy” order over subjective models, and observe that this makes it possible to partially restore standard martingale convergence arguments that apply under correctly specified learning. Based on this, we derive general conditions to determine when beliefs in a given environment converge to some long-run belief either locally or globally (i.e., from some or all initial beliefs). We show that these conditions can be applied, first, to unify and generalize various convergence results in previously studied settings. Second, they enable us to analyze environments where learning is “slow”, such as costly information acquisition and sequential social learning. In such environments, we illustrate that even if agents learn the truth when they are correctly specified, vanishingly small amounts of misspecification can generate extreme failures of learning.
Overview of Methods of Visualisation and Navigation in the Context of Maps and Geographical Data HCI Winter School Literature Review : Group 7

In geovisualisation, the datasets used are often quite extensive. There can be multiple dimensions to the variables of interest, making it complicated to represent the data understandably for exploration and analysis. Moreover, the standard multi-scale navigation techniques are cumbersome to use and not adapted to tasks such as comparing objects of interest and collaborative work. In this paper, we present a brief overview of geovisualisation, layercompositing,andmulti-scalenavigation. We coverthemethod-ology of visualising multidimensional data and how animation can represent and visualise geographical data. Moreover, we present the studies and techniques to increase the capacity for visualising, compositing, and navigating geographical maps and big datasets. Visualisation and navigation techniques are highly dependent on the domain, datasets and tasks. Thus we try to present a diversified view of the methods from various fields.
Recent Developments in Computational Typology and Multilingual Natural Language Processing

This is SIGTYP’s tenth newsletter on recent developments in computational typology and multilingual natural language processing. Each month, various members of SIGTYP will endeavour to summarize recent papers that focus on these topics. The papers or scholarly works that we review are selected to reflect a diverse set of research directions. They represent works that the editors found to be interesting and wanted to share. Given the fast-paced nature of research in our field, we find that brief summaries of interesting papers are a useful way to cut the wheat from the chaff. We expressly encourage people working in computational typology and multilingual NLP to submit summaries of their own research, which we will collate, edit and announce on SIGTYP’s website. In this issue,
Numerical Simulation of Dynamic Fluid-Structure Interaction with Elastic Structure–Rigid Obstacle Contact

An implicit scheme by partitioned procedures is proposed to solve a dynamic fluid–structure interaction problem in the case when the structure displacements are limited by a rigid obstacle. For the fluid equations (Sokes or Navier–Stokes), the fictitious domain method with penalization was used. The equality of the fluid and structure velocities at the interface was obtained using the penalization technique. The surface forces at the fluid–structure interface were computed using the fluid solution in the structure domain. A quadratic optimization problem with linear inequalities constraints was solved to obtain the structure displacements. Numerical results are presented.
Improving the Self-Esteem and Aggression Control of Deaf Adolescent Girls: The Effectiveness of Life Skills Training

Objective: Many deaf adolescents tend to show more aggression, due to their less social and emotional skills. This also affects their self-evaluation and self-esteem. Life skills are related to the personal, intellectual, emotional, and physical development of an individual. The present study was done to examine the effectiveness of a life skills training program on improvement of the self-esteem and aggression control of deaf adolescents girls. Methods: This quasi-experimental study with a pre-test and post-test design using a control group Was done on 34 female deaf adolescents who were studying in first, second, and third grade of high school in Nezam Mafi High School, Tehran, Iran. Participants were selected using a purposive convenience sampling method and were randomly divided into two groups (experimental and control groups). The Coopersmith Self-Esteem Inventory (SEI) and the Buss-Perry Aggression Questionnaire (AQ) were used to collect data. The life skills training program was delivered to the experimental group in eight 60-min sessions. Results: The results showed that the eight sessions of life skills training program significantly increased self-esteem (P<0.001) and decreased aggression of the experimental group of deaf adolescents girls (P<0.001) while there was no change in the control group. Conclusion: According to the results, it can be concluded that life skills training should be considered as a method for improving the mental status of deaf adolescent girls. Since there may be less social experiences in deaf adolescents, a training program may help them to achieve more self-esteem skills and be more successful in social communications.
SCCS: Streaming cooperative computing system for edge environment

With the development and popularity of smart devices, streaming data is continually generated at the edge of network. Under those edge scenarios, it takes high latency to process the streaming data by cloud computing. And uploading the raw data to cloud server by networks faces the data privacy issues. Streaming edge computing provides an opportunity to 􀅭ill the gap between the real-time process and data privacy protection by conducting the major computing at the edge of networks where the streaming data is generated. In the paper, we propose and build SCCS, a Streaming data Cooperative Computing System which is suitable for edge environment. This system (i) cooperatively conducts data analysis tasks on several servers at the edge of network topology; (ii) utilizes the geographically distributed data set; and (iii) protects data privacy and data value. A case study of analyzing real sharedbicycledistributiondemonstrateshowedgenodesserverscooperatewithotherstoprocessstreamingdata on real time.
Direct determination of impurities in arsenic by atomic spectrometry

The present paper describes development of Inductively Coupled Plasma-Atomic Emission Spectrometry (ICP-AES) and Electrothermal Atomization-Atomic Absorption Spectrometry (ETA-AAS) for the direct determination of fifteen common metallic and semi-conducting elements in high purity arsenic without prior separation of the major matrix. With a view to improving determination of Cd in arsenic matrix and for providing independent check on determination of few representative elements such as Cu, Cr, Mn and Ni by ICP-AES method, ETA-AAS technique also has been developed for direct determination of these elements in arsenic matrix. A number of synthetic samples have been prepared and analysed by both the techniques to validate the analytical procedures adopted here.
Grapheme-to-Phoneme Transformer Model for Transfer Learning Dialects

Grapheme-to-Phoneme (G2P) models convert words to their phonetic pronunciations. Classic G2P methods include rule-based systems and pronunciation dictionaries, while modern G2P systems incorporate learning, such as, LSTM and Transformer-based attention models. Usually, dictionary-based methods require significant manual effort to build, and have limited adaptivity on unseen words. And transformer-based models require significant training data, and do not generalize well, especially for dialects with limited data. We propose a novel use of transformer-based attention model that can adapt to unseen dialects of English language, while using a small dictionary. We show that our method has potential applications for accent transfer for text-to-speech, and for building robust G2P models for dialects with limited pronunciation dictionary size. We experiment with two English dialects: Indian and British. A model trained from scratch using 1000 words from British English dictionary, with 14211 words held out, leads to phoneme error rate (PER) of 26.877%, on a test set generated using the full dictionary. The same model pretrained on CMUDict American English dictionary, and fine-tuned on the same dataset leads to PER of 2.469% on the test set.
Plug-and-Blend: A Framework for Controllable Story Generation with Blended Control Codes

We describe a Plug-and-Play controllable language generation framework, Plug-and-Blend, that allows a human user to input multiple control codes (topics). In the context of automated story generation, this allows a human user lose or fine grained control of the topics that will appear in the generated story, and can even allow for overlapping, blended topics. We show that our framework, working with different generation models, controls the generation towards given continuous-weighted control codes while keeping the generated sentences fluent, demonstrating strong blending capability.
Evaluating Explanations for Reading Comprehension with Realistic Counterfactuals

Token-level attributions have been extensively studied to explain model predictions for a wide range of classiﬁcation tasks in NLP (e.g., sentiment analysis), but such explanation techniques are less explored for machine reading comprehension (RC) tasks. Although the transformer-based models used here are identical to those used for classiﬁcation, the underlying reasoning these models perform is very different and different types of explanations are required. We propose a methodology to evaluate explanations: an explanation should allow us to understand the RC model’s high-level behavior with respect to a set of realistic counterfactual input scenarios. We deﬁne these counterfactuals for several RC settings, and by connecting explanation techniques’ outputs to high-level model behavior, we can evaluate how useful different explanations really are. Our analysis suggests that pairwise explanation techniques are better suited to RC than token-level attributions, which are often unfaithful in the scenarios we consider. We additionally propose an improvement to an attention-based attribution technique, resulting in explanations which better reveal the model’s behavior. 1
Joint Online Multichannel Acoustic Echo Cancellation, Speech Dereverberation and Source Separation

This paper presents a joint source separation algorithm that simultaneously reduces acoustic echo, reverberation and interfering sources. Target speeches are separated from the mixture by maximizing independence with respect to the other sources. It is shown that the separation process can be decomposed into cascading sub-processes that separately relate to acoustic echo cancellation, speech dereverberation and source separation, all of which are solved using the auxiliary function based independent component/vector analysis techniques, and their solving orders are exchangeable. The cascaded solution not only leads to lower computational complexity but also better separation performance than the vanilla joint algorithm.
Small field polynomial inflation: reheating, radiative stability and lower bound

We revisit the renormalizable polynomial inflection point model of inflation, focusing on the small field scenario which can be treated fully analytically. In particular, the running of the spectral index is predicted to be α = -1.43 × 10-3 +5.56 × 10-5(NCMB-65 ), which might be tested in future. We also analyze reheating through perturbative inflaton decays to either fermionic or bosonic final states via a trilinear coupling. The lower bound on the reheating temperature from successful Big Bang nucleosynthesis gives lower bounds for these couplings; on the other hand radiative stability of the inflaton potential leads to upper bounds. In combination this leads to a lower bound on the location ϕ0 of the near inflection point, ϕ0 > 3 · 10-5 in Planckian units. The Hubble parameter during inflation can be as low as Hinf∼ 1 MeV, or as high as ∼ 1010 GeV. Similarly, the reheating temperature can lie between its lower bound of ∼ 4 MeV and about 4 · 108 (1011) GeV for fermionic (bosonic) inflaton decays. We finally speculate on the “prehistory” of the universe in this scenario, which might have included an epoch of eternal inflation.
Language model fusion for streaming end to end speech recognition

Streaming processing of speech audio is required for many contemporary practical speech recognition tasks. Even with the large corpora of manually transcribed speech data available today, it is impossible for such corpora to cover adequately the long tail of linguistic content that's important for tasks such as open-ended dictation and voice search. We seek to address both the streaming and the tail recognition challenges by using a language model (LM) trained on unpaired text data to enhance the end-to-end (E2E) model. We extend shallow fusion and cold fusion approaches to streaming Recurrent Neural Network Transducer (RNNT), and also propose two new competitive fusion approaches that further enhance the RNNT architecture. Our results on multiple languages with varying training set sizes show that these fusion methods improve streaming RNNT performance through introducing extra linguistic features. Cold fusion works consistently better on streaming RNNT with up to a 8.5% WER improvement.
SVDistNet: Self-Supervised Near-Field Distance Estimation on Surround View Fisheye Cameras

A 360° perception of scene geometry is essential for automated driving, notably for parking and urban driving scenarios. Typically, it is achieved using surround-view fisheye cameras, focusing on the near-field area around the vehicle. The majority of current depth estimation approaches focus on employing just a single camera, which cannot be straightforwardly generalized to multiple cameras. The depth estimation model must be tested on a variety of cameras equipped to millions of cars with varying camera geometries. Even within a single car, intrinsics vary due to manufacturing tolerances. Deep learning models are sensitive to these changes, and it is practically infeasible to train and test on each camera variant. As a result, we present novel camera-geometry adaptive multi-scale convolutions which utilize the camera parameters as a conditional input, enabling the model to generalize to previously unseen fisheye cameras. Additionally, we improve the distance estimation by pairwise and patchwise vector-based self-attention encoder networks. We evaluate our approach on the Fisheye WoodScape surround-view dataset, significantly improving over previous approaches. We also show a generalization of our approach across different camera viewing angles and perform extensive experiments to support our contributions. To enable comparison with other approaches, we evaluate the front camera data on the KITTI dataset (pinhole camera images) and achieve state-of-the-art performance among self-supervised monocular methods. An overview video with qualitative results is provided at https://youtu.be/bmX0UcU9wtA. Baseline code and dataset will be made public.1
External Photoevaporation of Protoplanetary Disks: Does Location Matter?

Many theoretical studies have shown that external photoevaporation from massive stars can severely truncate, or destroy altogether, the gaseous protoplanetary disks around young stars. In tandem, several observational studies report a correlation between the mass of a protoplanetary disk and its distance to massive ionizing stars in star-forming regions, and cite external photoevaporation by the massive stars as the origin of this correlation. We present N-body simulations of the dynamical evolution of star-forming regions and determine the mass loss in protoplanetary disks from external photoevaporation due to far-ultraviolet and extreme-ultraviolet radiation from massive stars. We find that projection effects can be significant, in that low-mass disk-hosting stars that appear close to the ionizing sources may be fore- or background stars in the star-forming region. We find very little evidence in our simulations for a trend in increasing disk mass with increasing distance from the massive star(s), even when projection effects are ignored. Furthermore, the dynamical evolution of these young star-forming regions moves stars whose disks have been photoevaporated to far-flung locations, away from the ionizing stars, and we suggest that any correlation between disk mass and distance from the ionizing star is either coincidental, or due to some process other than external photoevaporation.
Towards Objectively Evaluating the Quality of Generated Medical Summaries

We propose a method for evaluating the quality of generated text by asking evaluators to count facts, and computing precision, recall, f-score, and accuracy from the raw counts. We believe this approach leads to a more objective and easier to reproduce evaluation. We apply this to the task of medical report summarisation, where measuring objective quality and accuracy is of paramount importance.