Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding Information Processing in Human Brain by Interpreting Machine Learning Models

The thesis explores the role machine learning methods play in creating intuitive computational models of neural processing. Combined with interpretability techniques, machine learning could replace human modeler and shift the focus of human effort to extracting the knowledge from the ready-made models and articulating that knowledge into intuitive descroptions of reality. This perspective makes the case in favor of the larger role that exploratory and data-driven approach to computational neuroscience could play while coexisting alongside the traditional hypothesis-driven approach. We exemplify the proposed approach in the context of the knowledge representation taxonomy with three research projects that employ interpretability techniques on top of machine learning methods at three different levels of neural organization. The first study (Chapter 3) explores feature importance analysis of a random forest decoder trained on intracerebral recordings from 100 human subjects to identify spectrotemporal signatures that characterize local neural activity during the task of visual categorization. The second study (Chapter 4) employs representation similarity analysis to compare the neural responses of the areas along the ventral stream with the activations of the layers of a deep convolutional neural network. The third study (Chapter 5) proposes a method that allows test subjects to visually explore the state representation of their neural signal in real time. This is achieved by using a topology-preserving dimensionality reduction technique that allows to transform the neural data from the multidimensional representation used by the computer into a two-dimensional representation a human can grasp. The approach, the taxonomy, and the examples, present a strong case for the applicability of machine learning methods to automatic knowledge discovery in neuroscience.
A field perspective on effects of fire and temperature fluctuation on Cerrado legume seeds

Abstract Information from a field perspective on temperature thresholds related to physical dormancy (PY) alleviation and seed resistance to high temperatures of fire is crucial to disentangle fire- and non-fire-related germination cues. We investigated seed germination and survival of four leguminous species from a frequently burned open Neotropical savanna in Central Brazil. Three field experiments were conducted according to seed location in/on the soil: (1) fire effects on exposed seeds; (2) fire effects on buried seeds; and (3) effects of temperature fluctuations on exposed seeds in gaps and shaded microsites in vegetation. After field treatments, seeds were tested for germination in the laboratory, together with the control (non-treated seeds). Fire effects on exposed seeds decreased viability in all species. However, germination of buried Mimosa leiocephala seeds was enhanced by fire in an increased fuel load treatment, in which we doubled the amount of above-ground biomass. Germination of two species (M. leiocephala and Harpalyce brasiliana) was enhanced with temperature fluctuation in gaps, but this condition also decreased seed viability. Our main conclusions are: (1) most seeds died when exposed directly to fire; (2) PY could be alleviated during hotter fires when seeds were buried in the soil; and (3) daily temperature fluctuations in gaps also broke PY of seeds on the soil surface, so many seeds could be recruited or die before being incorporated into the soil seed banks. Thus seed dormancy-break and germination of legumes from Cerrado open savannas seem to be driven by both fire and temperature fluctuations.
End-to-End Variational Bayesian Training of Tensorized Neural Networks with Automatic Rank Determination

Low-rank tensor decomposition is one of the most effective approaches to reduce the memory and computing requirements of large-size neural networks, enabling their efficient deployment on various hardware platforms. While post-training tensor compression can greatly reduce the cost of inference, uncompressed training still consumes excessive hardware resources, run-time and energy. It is highly desirable to directly train a compact low-rank tensorized model from scratch with a low memory and computational cost. However, this is a very challenging task because it is hard to determine a proper tensor rank a priori, which controls the model complexity and compression ratio in the training process. This paper presents a novel end-to-end framework for low-rank tensorized training of neural networks. We first develop a flexible Bayesian model that can handle various low-rank tensor formats (e.g., CP, Tucker, tensor train and tensor-train matrix) that compress neural network parameters in training. This model can automatically determine the tensor ranks inside a nonlinear forward model, which is beyond the capability of existing Bayesian tensor methods. We further develop a scalable stochastic variational inference solver to estimate the posterior density of large-scale problems in training. Our work provides the first general-purpose rank-adaptive framework for end-to-end tensorized training. Our numerical results on various neural network architectures show orders-of-magnitude parameter reduction and little accuracy loss (or even better accuracy) in the training process.
The RELX Dataset and Matching the Multilingual Blanks for Cross-lingual Relation Classification

Relation classification is one of the key topics in information extraction, which can be used to construct knowledge bases or to provide useful information for question answering. Current approaches for relation classification are mainly focused on the English language and require lots of training data with human annotations. Creating and annotating a large amount of training data for low-resource languages is impractical and expensive. To overcome this issue, we propose two cross-lingual relation classification models: a baseline model based on Multilingual BERT and a new multilingual pretraining setup, which significantly improves the baseline with distant supervision. For evaluation, we introduce a new public benchmark dataset for cross-lingual relation classification in English, French, German, Spanish, and Turkish, called RELX. We also provide the RELX-Distant dataset, which includes hundreds of thousands of sentences with relations from Wikipedia and Wikidata collected by distant supervision for these languages. Our code and data are available at: https://github.com/boun-tabi/RELX
Inheritance study of different agronomic traits in mung × mash interspecific recombinant genotypes.

Inheritance study for various agronomic traits was first time conducted in 5 x 5 mungbean × mashbean interspecific recombinant genotypes. Highly significant general and specific combining ability (GCA & SCA) effects were observed among parents and their hybrids for days to flowering, days to maturity, clusters per plant and pods per plant however, pod length, 100-seed weight and seed yield per plant had significant GCA but non-significant SCA effects. The GCA/SCA ratios for all traits except 100-seed weight were less than unity indicating the preponderance of non-additive gene action. GCA/SCA ratio was more than unity for 100-seed weight showing the preponderance of additive gene action. Heterosis breeding for the improvement of all traits while, recombination breeding for 100-seed weight were suggested. The genotype MMH 4224 was observed to be good general combiner for seed yield and their related trait i.e. clusters per plant, pods per plant and pod length. The best specific cross combinations for the improvement of days to flower and maturity, plant height and clusters per plant were MMH 4224 × MMH 7124, MMH 7124 × MMH 4255 and MMH 1115 × MMH 2112. Special consideration must be given to cross combination MMH 7124 × MMH 4255 to improve clusters per plant in mungbean.
Hutch++: Optimal Stochastic Trace Estimation

We study the problem of estimating the trace of a matrix A that can only be accessed through matrix-vector multiplication. We introduce a new randomized algorithm, Hutch++, which computes a (1 ± ε) approximation to tr( A ) for any positive semidefinite (PSD) A using just O(1/ε) matrix-vector products. This improves on the ubiquitous Hutchinson's estimator, which requires O(1/ε 2) matrix-vector products. Our approach is based on a simple technique for reducing the variance of Hutchinson's estimator using a low-rank approximation step, and is easy to implement and analyze. Moreover, we prove that, up to a logarithmic factor, the complexity of Hutch++ is optimal amongst all matrix-vector query algorithms, even when queries can be chosen adaptively. We show that it significantly outperforms Hutchinson's method in experiments. While our theory requires A to be positive semidefinite, empirical gains extend to applications involving non-PSD matrices, such as triangle estimation in networks.
Effects of Demographic and Weather Parameters on COVID-19 Basic Reproduction Number

It is hard to overstate the importance of a timely prediction of the COVID-19 pandemic progression. Yet, this is not possible without a comprehensive understanding of environmental factors that may affect the infection transmissibility. Studies addressing parameters that may influence COVID-19 progression relied on either the total numbers of detected cases and similar proxies (which are highly sensitive to the testing capacity, levels of introduced social distancing measures, etc.), and/or a small number of analyzed factors, including analysis of regions that display a narrow range of these parameters. We here apply a novel approach, exploiting widespread growth regimes in COVID-19 detected case counts. By applying nonlinear dynamics methods to the exponential regime, we extract basic reproductive number R0 (i.e., the measure of COVID-19 inherent biological transmissibility), applying to the completely naïve population in the absence of social distancing, for 118 different countries. We then use bioinformatics methods to systematically collect data on a large number of potentially interesting demographics and weather parameters for these countries (where data was available), and seek their correlations with the rate of COVID-19 spread. While some of the already reported or assumed tendencies (e.g., negative correlation of transmissibility with temperature and humidity, significant correlation with UV, generally positive correlation with pollution levels) are also confirmed by our analysis, we report a number of both novel results and those that help settle existing disputes: the absence of dependence on wind speed and air pressure, negative correlation with precipitation; significant positive correlation with society development level (human development index) irrespective of testing policies, and percent of the urban population, but absence of correlation with population density per se. We find a strong positive correlation of transmissibility on alcohol consumption, and the absence of correlation on refugee numbers, contrary to some widespread beliefs. Significant tendencies with health-related factors are reported, including a detailed analysis of the blood type group showing consistent tendencies on Rh factor, and a strong positive correlation of transmissibility with cholesterol levels. Detailed comparisons of obtained results with previous findings, and limitations of our approach, are also provided.
Social-VRNN: One-Shot Multi-modal Trajectory Prediction for Interacting Pedestrians

Prediction of human motions is key for safe navigation of autonomous robots among humans. In cluttered environments, several motion hypotheses may exist for a pedestrian, due to its interactions with the environment and other pedestrians. Previous works for estimating multiple motion hypotheses require a large number of samples which limits their applicability in real-time motion planning. In this paper, we present a variational learning approach for interaction-aware and multi-modal trajectory prediction based on deep generative neural networks. Our approach can achieve faster convergence and requires significantly fewer samples comparing to state-of-the-art methods. Experimental results on real and simulation data show that our model can effectively learn to infer different trajectories. We compare our method with three baseline approaches and present performance results demonstrating that our generative model can achieve higher accuracy for trajectory prediction by producing diverse trajectories.
The threshold for the square of a Hamilton cycle

<p>Resolving a conjecture of Kühn and Osthus from 2012, we show that <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="p equals 1 slash StartRoot n EndRoot"> <mml:semantics> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>=</mml:mo> <mml:mn>1</mml:mn> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>/</mml:mo> </mml:mrow> <mml:msqrt> <mml:mi>n</mml:mi> </mml:msqrt> </mml:mrow> <mml:annotation encoding="application/x-tex">p= 1/\sqrt {n}</mml:annotation> </mml:semantics> </mml:math> </inline-formula> is the threshold for the random graph <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="upper G Subscript n comma p"> <mml:semantics> <mml:msub> <mml:mi>G</mml:mi> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi>n</mml:mi> <mml:mo>,</mml:mo> <mml:mi>p</mml:mi> </mml:mrow> </mml:msub> <mml:annotation encoding="application/x-tex">G_{n,p}</mml:annotation> </mml:semantics> </mml:math> </inline-formula> to contain the square of a Hamilton cycle. </p>
Information-Theoretic Bounds on Transfer Generalization Gap Based on Jensen-Shannon Divergence

In transfer learning, training and testing data sets are drawn from different data distributions. The transfer generalization gap is the difference between the population loss on the target data distribution and the training loss. The training data set generally includes data drawn from both source and target distributions. This work presents novel information-theoretic upper bounds on the average transfer generalization gap that capture ($i$) the domain shift between the target data distribution $P_{Z}^{\prime}$ and the source distribution $P_{Z}$ through a two-parameter family of generalized $(\alpha_{1},\ \alpha_{2})$ -Jensen-Shannon (JS) divergences; and (ii) the sensitivity of the transfer learner output $W$ to each individual sample of the data set $Z_{i}$ via the mutual information $I(W;Z_{i})$. For $\alpha_{1}\in(0,1)$, the $(\alpha_{1},\ \alpha_{2})$ - JS divergence can be bounded even when the support of $P_{Z}$ is not included in that of $P_{Z}^{\prime}$. This contrasts the Kullback-Leibler (KL) divergence $D_{\text{KL}}(P_{Z}\Vert P_{Z}^{\prime})$ -based bounds of Wu et al. [1], which are vacuous under this assumption. Moreover, the obtained bounds hold for unbounded loss functions with bounded cumulant generating functions, unlike the $\phi$ -divergence based bound of Wu et al. [1]. We also obtain new upper bounds on the average transfer excess risk in terms of the $(\alpha_{1},\ \alpha_{2})$ -JS divergence for empirical weighted risk minimization (EWRM), which minimizes the weighted average training losses over source and target data sets. Finally, we provide a numerical example to illustrate the merits of the introduced bounds.
Testing the quasar Hubble diagram with LISA standard sirens

Quasars have recently been used as an absolute distance indicator, extending the Hubble diagram to high redshift to reveal a deviation from the expansion history predicted for the standard, $\Lambda$CDM cosmology. Here we show that the Laser Interferometer Space Antenna (LISA) will efficiently test this claim with standard sirens at high redshift, defined by the coincident gravitational wave (GW) and electromagnetic (EM) observations of the merger of massive black hole binaries (MBHBs). Assuming a fiducial $\Lambda$CDM cosmology for generating mock standard siren datasets, the evidence for the $\Lambda$CDM model with respect to an alternative model inferred from quasar data [Nat. Astron. 3, 272 (2019)] is investigated. By simulating many realizations of possible future LISA observations, we find that for $50\%$ of these realizations (median result) 4 MBHB standard siren measurements will suffice to strongly differentiate between the two models, while 14 standard sirens will yield a similar result in $95\%$ of the realizations. In addition, we investigate the measurement precision of cosmological parameters as a function of the number of observed LISA MBHB standard sirens, finding that 15 events will on average achieve a relative precision of $5\%$ for $H_0$, reducing to $3\%$ and $2\%$ with 25 and 40 events, respectively. Our investigation clearly highlights the potential of LISA as a cosmological probe able to accurately map the expansion of the universe at $z\gtrsim 2$, and as a tool to cross-check and cross-validate cosmological EM measurements with complementary GW observations.
Assessing the effect of environmental factors on the spawning activity of the sea urchin Strongylocentrotus intermedius through video recording observations

: We used video recordings of spawning behaviour of the sea urchin Strongylocentrotus intermedius conducted in parallel with an analysis of gonadal state and measurement of environmental variables (tidal level, phytoplankton, temperature, salinity and oxygen concentration) in situ to study the role of environmental factors in triggering sea urchin spawning. The observations were performed during 3 reproductive seasons, each of 2 mo duration, in 2 bays with different levels of phytoplankton (measured as chlorophyll a [chl a] ). The only significant positive correlation found was between sea urchin spawning frequency and chl a concentration. Despite a high level of gonad maturity, sea urchin spawning was rarely recorded in the bay with low levels of chl a whereas mass spawning occurred in the bay with higher chl a concentration, with the peak of spawning activity centred around the full moon. Spawning was predominantly nocturnal. Immediately before and during spawning, males actively moved. Females began to spawn only when there were several spawning males nearby. In both sexes, each spawning event included several episodes of gamete release of different duration and with different intervals between them. The same male likely can participate in several spawning events. The following hierarchy of drivers of S. intermedius spawning activity was revealed: (1) increasing the phytoplankton concentration induced active motion and spawning in males, (2) both an elevated phytoplankton level and the presence of sperm stimulated spawning in females and (3) the night time and the new and full lunar phases increased the probability of spawning.
Exploring the Complex Dynamics of an Ion Channel Voltage Sensor Domain via Computation

Voltage-gated ion channels are ubiquitous proteins that orchestrate electrical signaling across excitable membranes. Key to their function is activation of the voltage sensor domain (VSD), a transmembrane four alpha-helix bundle that triggers channel opening. Modeling of currents from electrophysiology experiments yields a set of kinetic parameters for a given channel, but no direct molecular insight. Here we use molecular dynamics (MD) simulations to determine the free energy landscape of VSD activation and to, ultimately, predict the time evolution of the resulting gating currents. Our study provides the long-sought-for bridge between electrophysiology and microscopic molecular dynamics and confirms, as already suggested on the basis of experiments, that rate-limiting barriers play a critical role in activation kinetics.
High Prevalence of Bovine Viral Diarrhea Virus-1 in Sheep Abortion Samples with Pestivirus Infection in Turkey

Received: Revised: Accepted: Published online: July 05, 2017 September 18, 2017 September 19, 2017 November 21, 2017 The aim of this study is the detection of prevalence of bovine viral diarrhea virus-1 (BVDV-1) in sheep abortion samples in Turkey. For this purpose, initially, the detection of pestiviruses, bluetongue virus (BTV) and akabane virus (AKAV) was performed by reverse transcription-polymerase chain reaction (RT-PCR) from the aborted sheep fetus specimens. Then, for differentiation of pestiviruses, further investigations were carried out from the pestivirus positive samples. Pestiviruses RNAs were detected from 98 (24.74%) of 396 aborted fetus samples collected from 13 provinces in Turkey. By using border disease virus (BDV) and bovine viral diarrhea virus (BVDV) specific primers, 58 (59.18%) of pestivirus positive 98 samples were detected as BDV and the remaining 40 (40.81%) were found as BVDV with RT-PCR. According to the results, BVDV RNAs were detected from 10.10% (40/396) of aborted fetus specimens. By sequencing and phylogenetic analysis of BVDV positive four samples, BVDV-1 was identified as the genotype of BVDV. The BTV and AKAV genome were not detected from the 396 aborted samples tested in this study. In conclusion, there's a high ratio of BVDV-1 among sheep abortions in Turkey. According to this study results, we think that the high prevalence of BVDV in sheep abortion cases may be important for the control programme against persistent BVDV infections. ©2017 PVJ. All rights reserved
Comparative evaluation of different diagnostic tests for B. gibsoni in dogs

Babesia gibsoni is the common cause of canine babesiosis in India. Its diagnosis by conventional diagnostic methods result in false negative condition, particularly in those cases in which parasitemia is low. Therefore the present study employed IFAT and PCR along with microscopic examination to overcome this situation. Microscopic examination of Giemsa-stained blood smears revealed presence of small pear-shaped, oval or signet ring shaped B. gibsoni within the erythrocytes of 1.34 % dogs. IFAT and PCR detected a total of 1.80 % and 1.82 % dogs positive for B. gibsoni, respectively. The sensitivity and specificity of IFAT was 93.10% and 88.89%, respectively whereas, sensitivity and specificity of microscopic examination was 71.26% and 95.56%, respectively. The agreement of IFAT and microscopic examination with PCR was 82.30% and 60.73%, respectively. The agreement between IFAT and microscopy was 60.99%. Therefore, IFAT is considered to be highly sensitive test for diagnosing B. gibsoni in subclinically or chronically infected dogs with significantly low level of parasitemia.
Towards Interpreting BERT for Reading Comprehension Based QA

BERT and its variants have achieved state-of-the-art performance in various NLP tasks. Since then, various works have been proposed to analyze the linguistic information being captured in BERT. However, the current works do not provide an insight into how BERT is able to achieve near human-level performance on the task of Reading Comprehension based Question Answering. In this work, we attempt to interpret BERT for RCQA. Since BERT layers do not have predefined roles, we define a layer's role or functionality using Integrated Gradients. Based on the defined roles, we perform a preliminary analysis across all layers. We observed that the initial layers focus on query-passage interaction, whereas later layers focus more on contextual understanding and enhancing the answer prediction. Specifically for quantifier questions (how much/how many), we notice that BERT focuses on confusing words (i.e., on other numerical quantities in the passage) in the later layers, but still manages to predict the answer correctly. The fine-tuning and analysis scripts will be publicly available at this https URL .