Miracle-dz/UniFAR-FTUs huggingface dataset:
DCT-Conv: Coding filters in convolutional networks with Discrete Cosine Transform

Convolutional neural networks are based on a huge number of trained weights. Consequently, they are often data-greedy, sensitive to overtraining, and learn slowly. We follow the line of research in which filters of convolutional neural layers are determined on the basis of a smaller number of trained parameters. In this paper, the trained parameters define a frequency spectrum which is transformed into convolutional filters with Inverse Discrete Cosine Transform (IDCT, the same is applied in decompression from JPEG). We analyze how switching off selected components of the spectra, thereby reducing the number of trained weights of the network, affects its performance. Our experiments show that coding the filters with trained DCT parameters leads to improvement over traditional convolution. Also, the performance of the networks modified this way decreases very slowly with the increasing extent of switching off these parameters. In some experiments, a good performance is observed when even 99.9% of these parameters are switched off.
TLT-school: a Corpus of Non Native Children Speech

This paper describes “TLT-school” a corpus of speech utterances collected in schools of northern Italy for assessing the performance of students learning both English and German. The corpus was recorded in the years 2017 and 2018 from students aged between nine and sixteen years, attending primary, middle and high school. All utterances have been scored, in terms of some predefined proficiency indicators, by human experts. In addition, most of utterances recorded in 2017 have been manually transcribed carefully. Guidelines and procedures used for manual transcriptions of utterances will be described in detail, as well as results achieved by means of an automatic speech recognition system developed by us. Part of the corpus is going to be freely distributed to scientific community particularly interested both in non-native speech recognition and automatic assessment of second language proficiency.
Loss-annealed GAIL for sample efficient and stable Imitation Learning

Imitation learning is the problem of learning a policy from an expert policy without access to a reward signal. Often, the expert policy is only available in the form of expert demonstrations. Behavior cloning and GAIL are two popularly used methods for performing imitation learning in this setting. Behavior cloning converges in a few training iterations, but doesn't reach peak performance and suffers from compounding errors due to its supervised training framework and iid assumption. GAIL attempts to tackle this problem by accounting for the temporal dependencies between states while matching occupancy measures of the expert and the policy. Although GAIL has shown successes in a number of environments, it takes a lot of environment interactions. Given their complementary benefits, existing methods have mentioned trying or tried to combine the two methods, without much success. We look at some of the limitations of existing ideas that try to combine BC and GAIL, and present an algorithm that combines the best of both worlds to enable faster and stable training while not compromising on performance. Our algorithm is embarrassingly simple to implement and seamlessly integrates with different policy gradient algorithms. We demonstrate the effectiveness of the algorithm both in low dimensional control tasks in a limited data setting, and in high dimensional grid world environments.
Expected Information Maximization: Using the I-Projection for Mixture Density Estimation

Modelling highly multi-modal data is a challenging problem in machine learning. Most algorithms are based on maximizing the likelihood, which corresponds to the M(oment)-projection of the data distribution to the model distribution. The M-projection forces the model to average over modes it cannot represent. In contrast, the I(information)-projection ignores such modes in the data and concentrates on the modes the model can represent. Such behavior is appealing whenever we deal with highly multi-modal data where modelling single modes correctly is more important than covering all the modes. Despite this advantage, the I-projection is rarely used in practice due to the lack of algorithms that can efficiently optimize it based on data. In this work, we present a new algorithm called Expected Information Maximization (EIM) for computing the I-projection solely based on samples for general latent variable models, where we focus on Gaussian mixtures models and Gaussian mixtures of experts. Our approach applies a variational upper bound to the I-projection objective which decomposes the original objective into single objectives for each mixture component as well as for the coefficients, allowing an efficient optimization. Similar to GANs, our approach employs discriminators but uses a more stable optimization procedure, using a tight upper bound. We show that our algorithm is much more effective in computing the I-projection than recent GAN approaches and we illustrate the effectiveness of our approach for modelling multi-modal behavior on two pedestrian and traffic prediction datasets.
Replica Exchange for Non-Convex Optimization

Gradient descent (GD) is known to converge quickly for convex objective functions, but it can be trapped at local minimums. On the other hand, Langevin dynamics (LD) can explore the state space and find global minimums, but in order to give accurate estimates, LD needs to run with small discretization stepsize and weak stochastic force, which in general slow down its convergence. This paper shows that these two algorithms can "collaborate" through a simple exchange mechanism, in which they swap their current positions if LD yields a lower objective function. This idea can be seen as the singular limit of the replica exchange technique from the sampling literature. We show that this new algorithm converges to the global minimum linearly with high probability, assuming the objective function is strongly convex in a neighborhood of the unique global minimum. By replacing gradients with stochastic gradients, and adding a proper threshold to the exchange mechanism, our algorithm can also be used in online settings. We further verify our theoretical results through some numerical experiments, and observe superior performance of the proposed algorithm over running GD or LD alone.
EventMapper: Detecting Real-World Physical Events Using Corroborative and Probabilistic Sources

The ubiquity of social media makes it a rich source for physical event detection, such as disasters, and as a potential resource for crisis management resource allocation. There have been some recent works on leveraging social media sources for retrospective, after-the-fact event detection of large events such as earthquakes or hurricanes. Similarly, there is a long history of using traditional physical sensors such as climate satellites to perform regional event detection. However, combining social media with corroborative physical sensors for real-time, accurate, and global physical detection has remained unexplored. This paper presents EventMapper, a framework to support event recognition of small yet equally costly events (landslides, flooding, wildfires). EventMapper integrates high-latency, high-accuracy corroborative sources such as physical sensors with low-latency, noisy probabilistic sources such as social media streams to deliver real-time, global event recognition. Furthermore, EventMapper is resilient to the concept drift phenomenon, where machine learning models require continuous fine-tuning to maintain high performance. By exploiting the common features of probabilistic and corroborative sources, EventMapper automates machine learning model updates, maintenance, and fine-tuning. We describe three applications built on EventMapper for landslide, wildfire, and flooding detection.
Visual Summary of Value-level Feature Attribution in Prediction Classes with Recurrent Neural Networks

Deep Recurrent Neural Networks (RNN) is increasingly used in decision-making with temporal sequences. However, understanding how RNN models produce final predictions remains a major challenge. Existing work on interpreting RNN models for sequence predictions often focuses on explaining predictions for individual data instances (e.g., patients or students). Because state-of-the-art predictive models are formed with millions of parameters optimized over millions of instances, explaining predictions for single data instances can easily miss a bigger picture. Besides, many outperforming RNN models use multi-hot encoding to represent the presence/absence of features, where the interpretability of feature value attribution is missing. We present ViSFA, an interactive system that visually summarizes feature attribution over time for different feature values. ViSFA scales to large data such as the MIMIC dataset containing the electronic health records of 1.2 million high-dimensional temporal events. We demonstrate that ViSFA can help us reason RNN prediction and uncover insights from data by distilling complex attribution into compact and easy-to-interpret visualizations.
Quantum phases of collective SU(3) spin systems with bipartite symmetry

We study a bipartite collective spin-$1$ model with exchange interaction between the spins. The bipartite nature of the model manifests itself by the spins being divided into two equal-sized subsystems; within each subsystem the spin-spin interactions are of the same strength, across the subsystems they are also equal, but the two coupling values within and across the subsystem are different. Such a set-up is inspired by recent experiments with ultracold atoms. Using the $\mathrm{SU}(3)$-symmetry of the exchange interaction and the permutation symmetry within the subsystems, we can employ representation theoretic methods to diagonalize the Hamiltonian of the system in the entire parameter space of the two coupling-strengths. These techniques then allow us to explicitly construct and explore the ground-state phase diagram. The phase diagram turns out to be rich containing both gapped and gapless phases. An interesting observation is that one of the five phases features a strong bipartite symmetry breaking, meaning that the two subsystems in the ground states are in different $\mathrm{SU}(3)$ representations.
Arabidopsis thaliana WRKY25 Transcription Factor Mediates Oxidative Stress Tolerance and Regulates Senescence in a Redox-Dependent Manner

Senescence is the last developmental step in plant life and is accompanied by a massive change in gene expression implying a strong participation of transcriptional regulators. In the past decade, the WRKY53 transcription factor was disclosed to be a central node of a complex regulatory network of leaf senescence and to underlie a tight multi-layer control of expression, activity and protein stability. Here, we identify WRKY25 as a redox-sensitive up-stream regulatory factor of WRKY53 expression. Under non-oxidizing conditions, WRKY25 binds to a specific W-box in the WRKY53 promoter and acts as a positive regulator of WRKY53 expression in a transient expression system using Arabidopsis protoplasts, whereas oxidizing conditions dampened the action of WRKY25. However, overexpression of WRKY25 did not accelerate senescence but increased lifespan of Arabidopsis plants, whereas the knock-out of the gene resulted in the opposite phenotype, indicating a more complex regulatory function of WRKY25 within the WRKY subnetwork of senescence regulation. In addition, overexpression of WRKY25 mediated higher tolerance to oxidative stress and the intracellular H2O2 level is lower in WRKY25 overexpressing plants and higher in wrky25 mutants compared to wildtype plants suggesting that WRKY25 is also involved in controlling intracellular redox conditions. Consistently, WRKY25 overexpressers had higher and wrky mutants lower H2O2 scavenging capacity. Like already shown for WRKY53, MEKK1 positively influenced the activation potential of WRKY25 on the WRKY53 promoter. Taken together, WRKY53, WRKY25, MEKK1 and H2O2 interplay with each other in a complex network. As H2O2 signaling molecule participates in many stress responses, WRKK25 acts most likely as integrators of environmental signals into senescence regulation.
Optimizing Dryland Crop Management to Regional Climate via Simulation. Part I: U.S. Southern High Plains Cotton Production

Over semi-arid agricultural regions such as the U.S. Southern High Plains (SHP) producers of dryland crops need to know which management practices increase yields and decrease production risk. Here, a modeling approach is used to explore management options (MO) that increase dryland cotton yields and estimate those practice's yield risk effects under current SHP climate conditions. To simulate current dryland yield variability, dense distributions of lint yield outcomes were generated using the CROPGRO-Cotton crop model driven by weather inputs from 21 SHP weather stations during 2005–2016. Management effects were explored by repeating simulations over 32 MOs defined by 4 planting dates, 4 planting densities, and applying or not applying nitrogen. Both earlier planting date and decreased plant density increased median simulated yields, with earlier planting having the greatest positive yield effects. The simulated MO that produced the highest median lint yields planted on the earliest planting date (May 15), at the lowest density (3 plants m−1), and applied no nitrogen. Recent SHP field studies generally confirm the earlier planting date effect, but suggest insignificant yield effects for different seeding rates. Even so, negligible yield effects and lower input costs favor lower seeding densities from a profit standpoint. These crop simulations demonstrate a modeling-based method for climate-related agricultural risk management, and suggest mid-May planting dates and low plant densities as part of management practices that increase yields and profits in dryland SHP cotton production.
Grand Challenges in Gene and Epigenetic Editing for Neurologic Disease

The term genetics may evoke images of nucleic acid sequences for most researchers. However, the pragmatic filed of medicine might consider it as information about a patient that can be categorized as actionable, deterministic, probabilistic, or a variant of uncertain significance. Actionable information is any information that can inform a clinical decision, such as BRCA1/2 status informing a decision for prophylactic surgery. However, actionable information is currently the smallest category. There are over 6,000 single-gene disorders, the vast majority of which have no targeted clinical treatment. Such variants are merely deterministic, increasingly discoverable even before birth by a cell-free fetal DNA test but unable to inform any treatment that could alter the disease trajectory. A good example is the APOE ε4 allele, which imparts a high risk of Alzheimer’s disease, but currently informs no action that can change clinical outcome. Today, the discussion is whether it is ethical to test for such variants that could provide only a burden with little hope for benefit. An even greater numbers of less-penetrant variants are either certain to increase the probability of disease or have uncertain significance. Certainly, a grand challenge for all of genetic medicine over the next decade will be to move more of this information into the actionable category.
Sexing a Monomorphic Plumage Seabird Using Morphometrics and Assortative Mating

Abstract. This study aimed to establish a reliable method based on morphometrics to sex Long-tailed Jaeger (Stercorarius longicaudus), a species with slight differences in body size between sexes but no plumage differences. The presence of assortative mating based on size was also examined to determine if within-pair differences in size could improve sexing. Seventy-six Long-tailed Jaegers were measured, including 26 breeding pairs, on Bylot Island (Nunavut, Canada) during summers 2014-2018. Bird weight, wing chord, tarsus, head, and tail feathers were measured, and breast feathers were collected to determine sex with DNA extracts. A first discriminant function based on two variables (body mass and wing chord) accurately sexed 83% of birds. Some evidence for positive assortative mating based on size was found, as body mass of pair members was positively related, and 88% of females were heavier than their partner. A second discriminant function that included body mass, wing chord, length of the central tail feather, and partner's body mass accurately sexed 92% of birds. Adding a new morphometric and information from the partner allowed a reduction in sex misclassification by half (17% vs. 8%). In conclusion, external body measurements are useful to sex Long-tailed Jaegers, a slightly dimorphic species, and measurements of both members of a pair considerably improve the accuracy of sexing, likely due to the presence of assortative mating.
Synthetic Abortive HIV-1 RNAs Induce Potent Antiviral Immunity

Strong innate and adaptive immune responses are paramount in combating viral infections. Dendritic cells (DCs) detect viral infections via cytosolic RIG-I like receptors (RLRs) RIG-I and MDA5 leading to MAVS-induced immunity. The DEAD-box RNA helicase DDX3 senses abortive human immunodeficiency virus 1 (HIV-1) transcripts and induces MAVS-dependent type I interferon (IFN) responses, suggesting that abortive HIV-1 RNA transcripts induce antiviral immunity. Little is known about the induction of antiviral immunity by DDX3-ligand abortive HIV-1 RNA. Here we synthesized a 58 nucleotide-long capped RNA (HIV-1 Cap-RNA58) that mimics abortive HIV-1 RNA transcripts. HIV-1 Cap-RNA58 induced potent type I IFN responses in monocyte-derived DCs, monocytes, macrophages and primary CD1c+ DCs. Compared with RLR agonist poly-I:C, HIV-1 Cap-RNA58 induced comparable levels of type I IFN responses, identifying HIV-1 Cap-RNA58 as a potent trigger of antiviral immunity. In monocyte-derived DCs, HIV-1 Cap-RNA58 activated the transcription factors IRF3 and NF-κB. Moreover, HIV-1 Cap-RNA58 induced DC maturation and the expression of pro-inflammatory cytokines. HIV-1 Cap-RNA58-stimulated DCs induced proliferation of CD4+ and CD8+ T cells and differentiated naïve T helper (TH) cells toward a TH2 phenotype. Importantly, treatment of DCs with HIV-1 Cap-RNA58 resulted in an efficient antiviral innate immune response that reduced ongoing HIV-1 replication in DCs. Our data strongly suggest that HIV-1 Cap-RNA58 induces potent innate and adaptive immune responses, making it an interesting addition in vaccine design strategies.
Left Amygdala Regulates the Cerebral Reading Network During Fast Emotion Word Processing

Emotion words constitute a special class of verbal stimuli which can quickly activate the limbic system outside the left-hemisphere language network. Such fast response to emotion words may arise independently of the left occipitotemporal area involved in visual word-form analysis and rely on a distinct amygdala-dependent emotion circuit involved in fearful face processing. Using a hemifield priming paradigm with fMRI, we explored how the left and right amygdala systems interact with the reading network during emotion word processing. On each trial, participants viewed a centrally presented target which was preceded by a masked prime flashed either to the left or right visual field. Primes and targets, each denoting negative or positive nouns, could be either affectively congruent or incongruent with each other. We observed that affective congruency produced parallel changes in neural priming between the left frontal and parietotemporal regions and the bilateral amygdala. However, we also found that the left, but not right, amygdala exhibited significant change in functional connectivity with the neural components of reading as a function of affective congruency. Collectively, these results suggest that emotion words activate the bilateral amygdala during early stages of emotion word processing, whereas only the left amygdala exerts a long-distance regulatory influence over the reading network via its strong within-hemisphere connectivity.
The Balance Metabolism Safety Net: Integration of Stress Signals by Interacting Transcriptional Factors in Streptomyces and Related Actinobacteria

Soil dwelling Streptomyces species are faced with large variations in carbon or nitrogen sources, phosphate, oxygen, iron, sulfur, and other nutrients. These drastic changes in key nutrients result in an unbalanced metabolism that have undesirable consequences for growth, cell differentiation, reproduction, and secondary metabolites biosynthesis. In the last decades evidence has accumulated indicating that mechanisms to correct metabolic unbalances in Streptomyces species take place at the transcriptional level, mediated by different transcriptional factors. For example, the master regulator PhoP and the large SARP-type regulator AfsR bind to overlapping sequences in the afsS promoter and, therefore, compete in the integration of signals of phosphate starvation and S-adenosylmethionine (SAM) concentrations. The cross-talk between phosphate control of metabolism, mediated by the PhoR–PhoP system, and the pleiotropic orphan nitrogen regulator GlnR, is very interesting; PhoP represses GlnR and other nitrogen metabolism genes. The mechanisms of control by GlnR of several promoters of ATP binding cassettes (ABC) sugar transporters and carbon metabolism are highly elaborated. Another important cross-talk that governs nitrogen metabolism involves the competition between GlnR and the transcriptional factor MtrA. GlnR and MtrA exert opposite effects on expression of nitrogen metabolism genes. MtrA, under nitrogen rich conditions, represses expression of nitrogen assimilation and regulatory genes, including GlnR, and competes with GlnR for the GlnR binding sites. Strikingly, these sites also bind to PhoP. Novel examples of interacting transcriptional factors, discovered recently, are discussed to provide a broad view of this interactions. Altogether, these findings indicate that cross-talks between the major transcriptional factors protect the cell metabolic balance. A detailed analysis of the transcriptional factors binding sequences suggests that the transcriptional factors interact with specific regions, either by overlapping the recognition sequence of other factors or by binding to adjacent sites in those regions. Additional interactions on the regulatory backbone are provided by sigma factors, highly phosphorylated nucleotides, cyclic dinucleotides, and small ligands that interact with cognate receptor proteins and with TetR-type transcriptional regulators. We propose to define the signal integration DNA regions (so called integrator sites) that assemble responses to different stress, nutritional or environmental signals. These integrator sites constitute nodes recognized by two, three, or more transcriptional factors to compensate the unbalances produced by metabolic stresses. This interplay mechanism acts as a safety net to prevent major damage to the metabolism under extreme nutritional and environmental conditions.