Miracle-dz/UniFAR-FTUs huggingface dataset:
Recurrent Neural Network Transducer for Audio-Visual Speech Recognition

This work presents a large-scale audio-visual speech recognition system based on a recurrent neural network transducer (RNN-T) architecture. To support the development of such a system, we built a large audio-visual (A/V) dataset of segmented utterances extracted from YouTube public videos, leading to 31k hours of audio-visual training content. The performance of an audio-only, visual-only, and audio-visual system are compared on two large-vocabulary test sets: a set of utterance segments from public YouTube videos called YTDEV18 and the publicly available LRS3-TED set. To highlight the contribution of the visual modality, we also evaluated the performance of our system on the YTDEV18 set artificially corrupted with background noise and overlapping speech. To the best of our knowledge, our system significantly improves the state-of-the-art on the LRS3-TED set.
SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery

In drug-discovery-related tasks such as virtual screening, machine learning is emerging as a promising way to predict molecular properties. Conventionally, molecular fingerprints (numerical representations of molecules) are calculated through rule-based algorithms that map molecules to a sparse discrete space. However, these algorithms perform poorly for shallow prediction models or small datasets. To address this issue, we present SMILES Transformer. Inspired by Transformer and pre-trained language models from natural language processing, SMILES Transformer learns molecular fingerprints through unsupervised pre-training of the sequence-to-sequence language model using a huge corpus of SMILES, a text representation system for molecules. We performed benchmarks on 10 datasets against existing fingerprints and graph-based methods and demonstrated the superiority of the proposed algorithms in small-data settings where pre-training facilitated good generalization. Moreover, we define a novel metric to concurrently measure model accuracy and data efficiency.
On Policy Gradients

The goal of policy gradient approaches is to find a policy in a given class of policies which maximizes the expected return. Given a differentiable model of the policy, we want to apply a gradient-ascent technique to reach a local optimum. We mainly use gradient ascent, because it is theoretically well researched. The main issue is that the policy gradient with respect to the expected return is not available, thus we need to estimate it. As policy gradient algorithms also tend to require on-policy data for the gradient estimate, their biggest weakness is sample efficiency. For this reason, most research is focused on finding algorithms with improved sample efficiency. This paper provides a formal introduction to policy gradient that shows the development of policy gradient approaches, and should enable the reader to follow current research on the topic.
The role of residue M 232 in the C-terminal helix of human GLUTATHIONE TRANSFERASE T 1-1 by

The role of residue M232 in the C-terminal helix of human GLUTATHIONE TRANSFERASE T1-1 Michael D. Ianni Advisor: University of Guelph Dr. P. David Josephy Advisory Committee: Dr. Cezar Khursigara Dr. Gordon M. Kirby Glutathione transferases (GSTs) catalyze the glutathione (GSH) conjugation of various xenobiotics and endogenous compounds. GSTs play a pivotal role in cellular defence, as they are the main contributors to the inactivation of genotoxic compounds. A primary determinant for divergent substrate acceptance among GSTs appears to be the electrophilic binding site or “Hsite”. Shokeer et al. (2010) reported that replacement of residue methionine 232 by alanine in mouse GST T1-1 greatly altered the enzyme’s catalytic activities. The corresponding residue in human enzyme is also methionine, but, in other mammalian GST T1-1 orthologues, arginine (horse; Equus caballus) or leucine (pig; Sus scrofa) residues are found at this position. The goal of the present study aims to investigate the role of residue 232 (located in the C-terminal α-helix) in the structure and function of human GST T1-1 protein. Three variants (M232R, M232L and M232W) were constructed and expressed in Escherichia coli. The effects of these mutations on protein expression, catalytic activity and structure were tested. Variants M232R and M232L results were generally unremarkable, showing only minor differences from the wild-type protein. However, variant M232W showed significant decreases in protein expression, had reduced specific activities and showed substantial changes in the CD spectrum. The spectral shift was consistent with the interpretation that the C-terminal α-helix unwinds into a random-coil conformation. Unlike horse and pig, no mammalian species is known to have a GST T1-1 enzyme with a tryptophan residue at position 232. Results from this study shows that residue 232 does not critically affect the structure and function of human GST T1-1.
Sound propagation and speech hearing in a curved reverberant tunnel.

Transmission of correct speech in underground is essential to keep people safe in case of emergency. On the other hand, sound propagation characteristics is influenced by acoustical conditions of the sound field. From the viewpoints, sound propagation experiments, hearing tests of speech by subjects and measurements of RASTI are conducted in a reverberant curved tunnel. From measurements of pink noise propagation, relative attenuations less than 315Hz are much small, and those at higher frequencies rapidly increase with distance. The attenuations may be influenced by not only absorption characteristics but also the curved feature of the tunnel. While hearing scores of speech decrease with distance, differences between the scores are observed and may depend on the kinds of speech. From analyzing the hearing results, mistakes are occurred by influences of both reverberant condition near the sound source and degradation of signal-to-noise ratio of speech signal due to many times of reflection at further points. Positive correlation is observed between RASTI and scores of the articulation syllables. Relationships between RASTI and scores of the other speech are indicated through the results of articulation tests. Possibility on prediction of speech transmission by the objective physical measurements is suggested.
Testing the weak efficient market hypothesis: using Bangladeshi panel data

This empirical study investigates whether the Dhaka Stock Exchange market in Bangladesh is weakly efficient by modifying and estimating Dockery and Kavussanos’ multivariate model using a set of panel data. Consistent with a priori expectation, and previous research results, the findings suggest that the Dhaka Stock Market is not informationally efficient. The results also indicate that when the number of stocks included in the sample exceeds 3, the null hypothesis of the efficient market hypothesis is rejected throughout. A well developed market economy is a necessary condition for an efficient equity market. Thus, to build an efficient stock market, Bangladeshi authority should first concentrate its effort on developing its market infrastructure.
Brownian motion in trapping enclosures: steep potential wells, bistable wells and false bistability of induced Feynman–Kac (well) potentials

We investigate signatures of convergence for a sequence of diffusion processes on a line, in conservative force fields stemming from superharmonic potentials U(x) ∼ x m , m = 2n ⩾ 2. This is paralleled by a transformation of each mth diffusion generator L = DΔ + b(x)∇, and likewise the related Fokker–Planck operator L* = DΔ − ∇[b(x) ⋅], into the affiliated Schrödinger one Ĥ=−DΔ+V(x) . Upon a proper adjustment of operator domains, the dynamics is set by semigroups exp(tL), exp(tL*) and exp(−tĤ), with t ⩾ 0. The Feynman–Kac integral kernel of exp(−tĤ) is the major building block of the relaxation process transition probability density, from which L and L* actually follow. The spectral ‘closeness’ of the pertinent Ĥ and the Neumann Laplacian −ΔN in the interval is analyzed for m even and large. As a byproduct of the discussion, we give a detailed description of an analogous affinity, in terms of the m-family of operators Ĥ with a priori chosen V(x)∼xm , when Ĥ becomes spectrally ‘close’ to the Dirichlet Laplacian −ΔD for large m. For completness, a somewhat puzzling issue of the absence of negative eigenvalues for Ĥ with a bistable-looking potential }0, m{ >}2$? > V(x)=ax2m−2−bxm−2,a,b,>0,m>2 has been addressed.
Asymmetric Doping Effects and Sanctions in Sporting Contests

This paper analyses a one-shot game where, prior to a contest, two athletes simultaneously decide whether to engage in doping that is not certain to be detected. Doping is assumed to have at least as great a proportional effect on a naturally weaker athlete’s win probability. Given an explicit contest success function, the paper derives an optimal sanction scheme, where sanctions are identically proportional to prizes, sufficient to always induce a no-doping equilibrium. In comparison to previous papers, the winner’s optimal sanction, expressed in terms of talent and doping levels and doping costs, may be lower for all detection probabilities.
No apparent superluminal motion in the first-known jetted tidal disruption event Swift J1644+5734

The first-known tidal disruption event (TDE) with strong evidence for a relativistic jet - based on extensive multiwavelength campaigns - is Swift J1644+5734. In order to directly measure the apparent speed of the radio jet, we performed very long baseline interferometry (VLBI) observations with the European VLBI network (EVN) at 5 GHz. Our observing strategy was to identify a very nearby and compact radio source with the real-time e-EVN, and then utilize this source as a stationary astrometry reference point in the later five deep EVN observations. With respect to the in-beam source FIRST J1644+5736, we have achieved a statistical astrometric precision about 12 mu as (68 per cent confidence level) per epoch. This is one of the best phase-referencing measurements available to date. No proper motion has been detected in the Swift J1644+5734 radio ejecta. We conclude that the apparent average ejection speed between 2012.2 and 2015.2 was less than 0.3c with a confidence level of 99 per cent. This tight limit is direct observational evidence for either a very small viewing angle or a strong jet deceleration due to interactions with a dense circum-nuclear medium, in agreement with some recent theoretical studies.
A Universal Self-Attention Graph Neural Network.

Existing graph embedding models often have weaknesses in exploiting graph structure similarities, potential dependencies among nodes and global network properties. To this end, we present U2GNN, a novel embedding model leveraging on the strength of the recently introduced universal self-attention network (Dehghani et al., 2019), to learn low-dimensional embeddings of graphs which can be used for graph classification. In particular, given an input graph, U2GNN first applies a self-attention computation, which is then followed by a recurrent transition to iteratively memorize its attention on vector representations of each node and its neighbors across each iteration. Thus, U2GNN can address the weaknesses in the existing models in order to produce plausible node embeddings whose sum is the final embedding of the whole graph. Experimental results in both supervised and unsupervised fashions show that our U2GNN produces new state-of-the-art performances on a range of well-known benchmark datasets for the graph classification task.
Unitarity in Multi-Higgs Production

It has long been known that perturbative calculations in scalar multi-particle production could break down since fast growing amplitudes appear. A recent calculation in the regime $ \lambda n \gg 1$, where $ n $ is the multiplicity and $ \lambda $ is the self-coupling, gives an amplitude which grows exponentially with the energy, resulting in a divergent propagator and leading to the violation of perturbative unitarity. In this paper the transition rate is calculated from the solution of the self-consistent Schwinger-Dyson equation in spectral representation. We get an amplitude growing quadratically with the energy which leads to an asymptotically decreasing propagator contrary to previous results. Hence, unitarity is not violated, as expected in the Standard Model.
Neuronal expression in Drosophila of an evolutionarily conserved metallophosphodiesterase reveals pleiotropic roles in longevity and odorant response

Evolutionarily conserved genes often play critical roles in organismal physiology. Here, we describe multiple roles of a previously uncharacterized Class III metallophosphodiesterase in Drosophila, an ortholog of the MPPED1 and MPPED2 proteins expressed in the mammalian brain. dMpped, the product of CG16717, hydrolyzed phosphodiester substrates including cAMP and cGMP in a metal-dependent manner. dMpped is expressed during development and in the adult fly. RNA-seq analysis of dMppedKO flies revealed misregulation of innate immune pathways. dMppedKO flies showed a reduced lifespan, which could be restored in Dredd hypomorphs, indicating that excessive production of antimicrobial peptides contributed to reduced longevity. Elevated levels of cAMP and cGMP in the brain of dMppedKO flies was restored on neuronal expression of dMpped, with a concomitant reduction in levels of antimicrobial peptides and restoration of normal life span. We observed that dMpped is expressed in the antennal lobe in the fly brain. dMppedKO flies showed defective specific attractant perception and desiccation sensitivity, correlated with the overexpression of Obp28 and Obp59 in knock-out flies. Importantly, neuronal expression of mammalian MPPED2 restored lifespan in dMppedKO flies. This is the first description of the pleiotropic roles of an evolutionarily conserved metallophosphodiesterase that may moonlight in diverse signaling pathways in an organism.
A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse Data

Endowing dialogue systems with personas is essential to deliver more human-like conversations. However, this problem is still far from well explored due to the difficulties of both embodying personalities in natural languages and the persona sparsity issue observed in most dialogue corpora. This paper proposes a pre-training based personalized dialogue model that can generate coherent responses using persona-sparse dialogue data. In this method, a pre-trained language model is used to initialize an encoder and decoder, and personal attribute embeddings are devised to model richer dialogue contexts by encoding speakers' personas together with dialogue histories. Further, to incorporate the target persona in the decoding process and to balance its contribution, an attention routing structure is devised in the decoder to merge features extracted from the target persona and dialogue contexts using dynamically predicted weights. Our model can utilize persona-sparse dialogues in a unified manner during the training process, and can also control the amount of persona-related features to exhibit during the inference process. Both automatic and manual evaluation demonstrates that the proposed model outperforms state-of-the-art methods for generating more coherent and persona consistent responses with persona-sparse data.
Bayesian Graph Convolutional Neural Networks using Node Copying

Graph convolutional neural networks (GCNN) have numerous applications in different graph based learning tasks. Although the techniques obtain impressive results, they often fall short in accounting for the uncertainty associated with the underlying graph structure. In the recently proposed Bayesian GCNN (BGCN) framework, this issue is tackled by viewing the observed graph as a sample from a parametric random graph model and targeting joint inference of the graph and the GCNN weights. In this paper, we introduce an alternative generative model for graphs based on copying nodes and incorporate it within the BGCN framework. Our approach has the benefit that it uses information provided by the node features and training labels in the graph topology inference. Experiments show that the proposed algorithm compares favorably to the state-of-the-art in benchmark node classification tasks.
Weight gain in preterm low birth weight infants with multisensory intervention

Background: Birth weight is a significant determinant of newborn survival. In neonatal deaths, LBW is the underlying cause in 60-80% cases. This study was aimed to assess whether preterm infants receiving Multisensory intervention had more rapid weight gain compared to control group infants during NICU stay.Methods: Subjects for the study were preterm infants between 32 to 37 weeks of gestation age with birth weight between 1500 g to 2500 g and staying in NICU. Participants meeting inclusion criteria were randomly divided into either experimental group (n=15) or control group (n=15). The experimental group received Multisensory stimulation such as Auditory, Tactile, Visual and Vestibular stimulation for 10 consecutive days (15 min. /day) in NICU. Control group received routine NICU care. Weight was measured using an electronic weighing scale at day-1, day-5 and day-10 of intervention in both the groups and collected data was analysed.Results: The preterm infants of the experimental group had shown better growth than those in the control group with statistically significant differences (p<0.05) between the study and the control group at end of 10 days of intervention.Conclusions: From Analysed data, it is concluded that 5-day of Multisensory intervention could not elicit statistically significant improvement in weight gain, but 10 days intervention could elicit significant improvement. The result supports the use of multisensory intervention as a cost-effective therapy to promote growth.
A new strategy for controlling wind turbines against sensor faults and wake effects to harvest more electrical energy

This paper describes a new method for harvesting maximum electrical energy in wind farms. In proposing technique, the stochastic process principles are applied for detecting fault measurements of sensors. On the other hand, the wind farm is modeled by using fuzzy concept. Thereby the turbines are controlled against continuous changes in speed, direction and eddy currents of the blowing wind. To evaluate the performance of the proposed method three practical conditions of wind blowing are simulated. In the first scenario, the normal wind is simulated with low turbulence and slow changes. The second scenario belongs to high turbulence winds with sudden shifts in their parameters, and finally in the most complex scenario, several eddy currents are considered in blowing winds too. The obtained results show that the proposed method provides greater and more uniform harvested power compared to alternative methods. Furthermore, its superiority against other techniques has increased in parallel with the scenario become more complicated.
SENSITIVITY OF INTERMITTENT STREAMS TO CLIMATE VARIATIONS IN THE USA

There is a great deal of interest in the literature on streamflow changes caused by climate change because of the potential negative effects on aquatic biota and water supplies. Most previous studies have primarily focused on perennial streams, and there have been only a few studies examining the effect of climate variability on intermittent streams. Our objectives in this study were to (1) identify regions of similar zero-flow behaviour and (2) evaluate the sensitivity of intermittent streams to historical variability in climate in the USA. This study was carried out at 265 intermittent streams by evaluating (1) correlations among time series of flow metrics (number of zero-flow events, the average of the central 50% and largest 10% of flows) with climate (magnitudes, durations and intensity) and (2) decadal changes in the seasonality and long-term trends of these flow metrics. Results identified five distinct seasonality patterns in the zero-flow events. In addition, strong associations between the low-flowmetrics and historical changes in climate were found. The decadal analysis suggested no significant seasonal shifts or decade-to-decade trends in the low-flow metrics. The lack of trends or changes in seasonality is likely due to unchanged long-term patterns in precipitation over the time period examined. Published 2015. This article is a U.S. Government work and is in the public domain in the USA. key words: intermittent streams; climate change; classification; ephemeral streams Received 10 March 2015; Revised 2 July 2015; Accepted 7 July 2015
Determinants of Foreign Direct Investment (FDI) Inflows to Nigeria (1970-2010)

This paper summarizes the findings of causality and long-term relationship between Foreign Direct Investment (FDI), economic growth and other socio-political determinants. Although a considerable literature gives the evidence of relationship between FDI and economic growth. There is a need for a detailed examination of socio-political determinants of FDI. The paper considers economic growth, exchange rate, and level of interest rates, unemployment, and political instability as determinants of the level of FDI inflows for Nigeria over the period 1970 to 2010. Almost all variables are found to have the theoretically expected signs with two-way causality relationship. The present study also estimates an error correction model by ordinary least squares, based on cointegrating VAR (2).
Understanding Why Public Employment Services and Small-Business Assistance Programs Work at Getting the Unemployed Back to Work Evidence from Romania

Recent empirical evidence has found that public employment services (PES) and small-business assistance programs (SBA) are often successful at getting the unemployed back to work. One important concern of policy makers is to understand the suitability of these programs for different population subgroups, as well as the channels through which these programs work. Using unusually rich (for transition economies) survey data and matching methods, this evaluation study enhances our understanding of the potential of PES and SBA in transition economies, in general, and Romania, in particular. The main result of the paper is that heterogeneity matters and that ALMPs need to be tailored to the problem at hand, rather than following a one-size-fits-all approach. Our results show that PES are effective for workers with little access to informal job-search channels, and that SBA works for workers with less access to the primary labor market sector (that is, high productivity wage and salary jobs). The paper concludes with interesting policy recommendations for implementing these programs both in transition countries and in countries with large informal sectors, such as developing countries.