Miracle-dz/UniFAR-FTUs huggingface dataset:
Tratamento de insuficiência cardíaca com benazepril em cães com cardiomiopatia dilatada e endocardiose (Treatment of congestive heart failure with benazepril in dogs with dilated cardiomyopathy and endocardiosis)

Clinical effects of benazepril, a long acting angiotensin-converting enzyme (ACEi), in dogs with naturally-occurring congestive heart failure (CHF) caused by mitral endocardiosis or dilated cardiomyopathy were studied. The drug was given orally at a dose of 0.25 to 0.5mg/kg/day. Diuretics, digitalics, and antiarrhtyhmic drugs were given as needed. Physical, radiographic, and eletrocardiographic examination were performed at days 0, 7, 28, and 56. Serum biochemistry and arterial blood gases were obtained at days 0 and 56. Signs of dyspnea and general condition improved in all dogs. Cough decreased in frequency. The electrocardiogram did not change with benazepril use except for a decrease in P wave amplitude and duration. No adverse effects related to the use of benazepril were observed. Benazepril is an effective and well tolerated ACEi for the treatment of CHF in dogs.
Conflicting Interests in Fund Families’ Acquisitions of Fund Sponsorships∗

Our study of fund families’ acquisitions of fund sponsorships documents novel evidence supporting the hypothesis that because their fiduciary responsibilities are for investors (partners or shareholders) of fund families, management of fund families prioritize family values over fund investor interests. Specifically, although the sponsorship acquisitions are motivated by sensible reasons from fund families’ perspective, they tend to destroy fund performance, especially for the acquired funds that remain intact and the acquiring families are “acquainted” with, and don’t benefit fund investors by enhancing liquidity, lowering fees, or expanding the set of fund investment opportunities as indicated by the pre-acquisition fund performance. JEL Classification: G23; G34
Loudness and Dynamic Range in broadcast audio-the Dolby solution

Digital delivery media offer a wider dynamic range for audio than their analogue predecessors. This entails adopting a larger difference between the average levels (and thus the implied loudness) and the signal peaks. Although it is possible to implement this larger difference in a TV station or media studio, problems will occur in the home – due to inconsistent loudness and electrical levels within the consumer receivers and audio equipment.
Evaluation of Italian Ryegrass Populations Naturalized in the Flooding Pampa of Argentina. II. Phenotypic Variability among Populations Growing in Different Soils

Naturalized populations of Italian ryegrass (Lolium multiflorum Lam.) grow in different environments in Buenos Aires Province (Argentina). Juvenile plants of a cultivar and 20 populations collected in central-east areas of this Province were analysed in two environments with different soil types (Argiudol and Natracuol). At each site, 40 plants per plot were transplanted isolated, in a complete randomized blocks with three replicates. Morphological and agronomic traits were registered and ANOVA between environments were performed, with the replicates nested in the environments, and the genetic determination grade (GDG) was estimated. With the data registered during the trial as well as other previously obtained for the same populations, the affinity among populations was calculated by multivariate analysis. The genotype × environmental interaction resulted negative only for plant diameter and dry matter production as a result of the poor variation of the populations in the Natracuol soil type. Differences among populations and between environments in the majority of the characters were detected and the GDG was low to medium. The best performance was detected in Argiudol soil as a result of the limiting conditions of the Natracuol soil on the populations. Cluster analysis of the data in each environment showed differences in the affinity of the populations. The provenance of the most different populations was in the periphery of the area collected, while the ones distributed in the central area presented an affinity superior to 50%. Principal components analysis and the minimum spanning tree contributed to establish the affinity of the populations. _____________________________________________________________________________________________________________
Nonclassicality of induced coherence without induced emission

Interference of two beams produced at separate biphoton sources was first observed more than two decades ago. The phenomenon, often called ``induced coherence without induced emission,'' has recently gained attention after its applications to imaging, spectroscopy, and measuring biphoton correlations have been discovered. The sources used in the corresponding experiments are nonlinear crystals pumped by laser light. The use of a laser pump makes the occurrence of induced (stimulated) emission unavoidable, and the effect of stimulated emission can be observed in the joint detection rate of the two beams. This fact raises the question whether the stimulated emission also plays a role in inducing the coherence. Here we investigate a case in which the crystals are pumped with a single-photon Fock state. We find that coherence is induced even though the possibility of stimulated emission is now fully ruled out. Furthermore, the joint detection rate of the two beams becomes ideally zero. Our results rule out any classical or semiclassical explanation of the phenomenon and also suggest that it is, in principle, possible to perform similar experiments with fermions, for which stimulated emission is strictly forbidden.
Techniques to Reduce (π/4)-Parity-Phase Circuits, Motivated by the ZX Calculus

To approximate arbitrary unitary transformations on one or more qubits, one must perform transformations which are outside of the Clifford group. The gate most commonly considered for this purpose is the T = diag(1, exp(i \pi/4)) gate. As T gates are computationally expensive to perform fault-tolerantly in the most promising error-correction technologies, minimising the "T-count" (the number of T gates) required to realise a given unitary in a Clifford+T circuit is of great interest. We describe techniques to find circuits with reduced T-count in unitary circuits, which develop on the ideas of Heyfron and Campbell [arXiv:1712.01557] with the help of the ZX calculus. Following [arXiv:1712.01557], we reduce the problem to that of minimising the T count of a CNOT+T circuit. The ZX calculus motivates a further reduction to simplifying a product of commuting "\pi/4-parity-phase" operations: diagonal unitary transformations which induce a relative phase of exp(i \pi/4) depending on the outcome of a parity computation on the standard basis (which motivated Kissinger and van de Wetering [1903.10477] to introduce "phase gadgets"). For a number of standard benchmark circuits, we show that these techniques -- in some cases supplemented by the TODD subroutine of Heyfron and Campbell [arXiv:1712.01557] -- yield T-counts comparable to or better than the best previously known results.
Quasitilings and Applications

This thesis aims to serve as an introduction to the theory of quasitilings for amenable groups. In order to showcase the power of this theory, we focus on the study of the Sofic L\"uck Approximation Conjecture, which can be proven for amenable groups by making use of quasitilings. The first four chapters of the thesis are an exposition of the aforementioned topics, collected from the literature. After that, in the fifth and final chapter we present some new results related to the Sofic L\"uck Approximation Conjecture for amenable groups over discrete valuation rings and number fields.
DRNet: Dissect and Reconstruct the Convolutional Neural Network via Interpretable Manners

Convolutional neural networks (ConvNets) are widely used in real life. People usually use ConvNets which pre-trained on a fixed number of classes. However, for different application scenarios, we usually do not need all of the classes, which means ConvNets are redundant when dealing with these tasks. This paper focuses on the redundancy of ConvNet channels. We proposed a novel idea: using an interpretable manner to find the most important channels for every single class (dissect), and dynamically run channels according to classes in need (reconstruct). For VGG16 pre-trained on CIFAR-10, we only run 11\% parameters for two-classes sub-tasks on average with negligible accuracy loss. For VGG16 pre-trained on ImageNet, our method averagely gains 14.29\% accuracy promotion for two-classes sub-tasks. In addition, analysis show that our method captures some semantic meanings of channels, and uses the context information more targeted for sub-tasks of ConvNets.
Accuracy of the finite-temperature Lanczos method compared to simple typicality-based estimates

We study trace estimators for equilibrium thermodynamic observables that rely on the idea of typicality and derivatives thereof such as the finite-temperature Lanczos method (FTLM). As numerical examples quantum spin systems are studied. Our initial aim was to identify pathological examples or circumstances, such as strong frustration or unusual densities of states, where these methods could fail. Instead we failed with the attempt. All investigated systems allow such approximations, only at temperatures of the order of the lowest energy gap the convergence is somewhat slower in the number of random vectors over which observables are averaged.
Generate (non-software) Bugs to Fool Classifiers

In adversarial attacks intended to confound deep learning models, most studies have focused on limiting the magnitude of the modification so that humans do not notice the attack. On the other hand, during an attack against autonomous cars, for example, most drivers would not find it strange if a small insect image were placed on a stop sign, or they may overlook it. In this paper, we present a systematic approach to generate natural adversarial examples against classification models by employing such natural-appearing perturbations that imitate a certain object or signal. We first show the feasibility of this approach in an attack against an image classifier by employing generative adversarial networks that produce image patches that have the appearance of a natural object to fool the target model. We also introduce an algorithm to optimize placement of the perturbation in accordance with the input image, which makes the generation of adversarial examples fast and likely to succeed. Moreover, we experimentally show that the proposed approach can be extended to the audio domain, for example, to generate perturbations that sound like the chirping of birds to fool a speech classifier.
Information Retrieval with Actions and Change: an ASP-Based Solution

Information Retrieval (IR) aims at retrieving documents that are most relevant to a query provided by a user. Traditional techniques rely mostly on syntactic methods. In some cases, however, links at a deeper semantic level must be considered. In this paper, we explore a type of IR task in which documents describe sequences of events, and queries are about the state of the world after such events. In this context, successfully matching documents and query requires considering the events’ possibly implicit, uncertain effects and side-effects. We begin by analyzing the problem, then propose an action language based formalization, and finally automate the corresponding IR task using Answer Set Programming.
Coherent GPU Path Tracing: Improving Data Locality

Path Tracing algorithms can be used to render photo-realistic images in a considerable amount of hours. The efforts to make this process faster have received more attention with the popularization of the GPU. One of the main issues is the poor cache use when the rendered scenes are complex. This work presents a novel approach that aims to improve its use. Our method performs an iterative tiled BVH traversal, where the BVH is split into sub-BVHs and reorganized in a breadth first l ayout. At each s tep, the rays traverse a sub-BVH, then they are sorted based on the last node intersected. Which increases the data coherence and the GPU usage, leveraging the performance.
RefineDetLite: A Lightweight One-stage Object Detection Framework for CPU-only Devices

Previous state-of-the-art real-time object detectors have been reported on GPUs which are extremely expensive for processing massive data and in resource-restricted scenarios. Therefore, high efficiency object detectors on CPU-only devices are urgently-needed in industry. The floating-point operations (FLOPs1) of networks are not strictly proportional to the running speed on CPU devices, which inspires the design of an exactly "fast" and "accurate" object detector. After investigating the concern gaps between classification networks and detection backbones, and following the design principles of efficient networks, we propose a lightweight residual-like backbone with large receptive fields and wide dimensions for low-level features, which are crucial for detection tasks. Correspondingly, we also design a light-head detection part to match the backbone capability. Furthermore, by analyzing the drawbacks of current one-stage detector training strategies, we also propose three orthogonal training strategies—IOU-guided loss, classes-aware weighting method and balanced multitask training approach. Without bells and whistles, our proposed RefineDetLite achieves 26.8 mAP on the MSCO-CO benchmark at a speed of 130 ms/pic on a single-thread CPU. The detection accuracy can be further increased to 29.6 mAP by integrating all the proposed training strategies, without apparent speed drop.
Excitonic Transport and Intervalley Scattering in Exfoliated MoSe2 Monolayer Revealed by Four-Wave-Mixing Transient Grating Spectroscopy

Exciton intervalley scattering, annihilation and relaxation dynamics, and diffusive transport in a monolayer transition metal dichalcogenides are central to the functionality of devices based on them. This motivated us to investigate these properties in exfoliated high-quality monolayer MoSe2 using heterodyned nonlinear four-wave-mixing transient grating spectroscopy. While free exciton excitations are found to be long-lived (~230 ps), an extremely fast intervalley scattering ( less than 120 fs) is observed leading to a negligible valley polarization, consistent with steady state photoluminescence measurements. The exciton population decay shows an appreciable contribution from exciton-exciton annihilation reactions with an annihilation constant of ~ 0.01 cm2s-1, which in addition leads to an extra contribution to the transient grating response. The underlying excitonic dynamics were numerically modeled by including exciton-exciton annihilation, also in the diffusion equation, which allows extraction of the diffusion constant, D ~1.4 cm2s-1. Our results provide a method that allows for the disentanglement of the intricate dynamics involving many-body annihilation processes and a detailed characterization of the excitonic properties of monolayer MoSe2
Real-Time Emotion Recognition via Attention Gated Hierarchical Memory Network

Real-time emotion recognition (RTER) in conversations is significant for developing emotionally intelligent chatting machines. Without the future context in RTER, it becomes critical to build the memory bank carefully for capturing historical context and summarize the memories appropriately to retrieve relevant information. We propose an Attention Gated Hierarchical Memory Network (AGHMN) to address the problems of prior work: (1) Commonly used convolutional neural networks (CNNs) for utterance feature extraction are less compatible in the memory modules; (2) Unidirectional gated recurrent units (GRUs) only allow each historical utterance to have context before it, preventing information propagation in the opposite direction; (3) The Soft Attention for summarizing loses the positional and ordering information of memories, regardless of how the memory bank is built. Particularly, we propose a Hierarchical Memory Network (HMN) with a bidirectional GRU (BiGRU) as the utterance reader and a BiGRU fusion layer for the interaction between historical utterances. For memory summarizing, we propose an Attention GRU (AGRU) where we utilize the attention weights to update the internal state of GRU. We further promote the AGRU to a bidirectional variant (BiAGRU) to balance the contextual information from recent memories and that from distant memories. We conduct experiments on two emotion conversation datasets with extensive analysis, demonstrating the efficacy of our AGHMN models.
Bayesian Curiosity for Efficient Exploration in Reinforcement Learning

Balancing exploration and exploitation is a fundamental part of reinforcement learning, yet most state-of-the-art algorithms use a naive exploration protocol like $\epsilon$-greedy. This contributes to the problem of high sample complexity, as the algorithm wastes effort by repeatedly visiting parts of the state space that have already been explored. We introduce a novel method based on Bayesian linear regression and latent space embedding to generate an intrinsic reward signal that encourages the learning agent to seek out unexplored parts of the state space. This method is computationally efficient, simple to implement, and can extend any state-of-the-art reinforcement learning algorithm. We evaluate the method on a range of algorithms and challenging control tasks, on both simulated and physical robots, demonstrating how the proposed method can significantly improve sample complexity.
HAND-EYE COORDINATION: COOPERATIVE AND COMPETITIVE SYSTEMS

INTRODUCTION In order to interact effectively with the everyday world, man depends heavily on the close coupling between perceptual information and motoric action. When reaching for an object in the visual surrounding, the actor typically performs a combination of eye, head and arm movements. Researchers have devoted considerable effort to understanding the mechanisms that underlie this behavior. Particularly, studies addressed to the question how eye and hand movements are coordinated with respect to a visual target have provided significant insights about the motor control mechanisms involved. Hand-eye coordination has been studied separately at least at three different levels of visual-motor behavior. Firstly, a considerable amount of work has been done to determine whether eye and hand share common mechanisms during the preparation of the movements (e.g., Bekkering et al. 1994,1995; Biguer et al. 1982). Secondly, interactions between the oculomotor and manual motor systems have been investigated during the execution of respectively the eye and hand movements (e.g., Abrams et al. 1990; Bock 1987). Finally, the visual-spatial information for eye and hand movement's terminal accuracy have been investigated (e.g., Prablanc et al. 1979). While investigating the preparation processes of coordinated eye and hand movements, Bekkering and colleagues (1994,1995) found an interference effect, i.e. longer eye latencies when the eye is accompanied by a goal-directed movement than when the eyes moves alone. It was suggested that a close coupling exists between the attentional mechanisms necessary to select an object in the environment and the preparation of motor actions to interact with the object. It is well known from earlier studies on smooth pursuit eye movements that a person can more accurately track a visual target that he or she moves himor herself than a target that is moved without internal control (e.g., Steinbach 1969), indicating that the oculomotor system has access to the efference (outflow) from the hand system which produces the target movements. Recently, Bekkering and Koch (1996) investigated interactions between eye and hand performances in the case that the externally generated visual information was presented discontinuously. The finding that interactions in the motor output of one of the two systems can be found even during the absence of a visual target, provided a strong argument in favor of the assumption that the two motor systems share common mechanisms during the execution of their respective movements who are not related to the perceptual input side. The visual-spatial information that is used in order to plan the location of a movement target for coordinated eye and hand movements has been the focus of considerable research scrutiny. At least two types of spatial information have been identified as being relevant for movement termination: (1) distance information and (2) location information. In this tutorial lecture I would like to present the main findings in the area of coordinated eye and hand movements with help of the scheme presented below and I
Keyword expansion for understanding crisis events in Indonesian tweets

Posts of emergency crises such as forest fires or terror attacks pervade social media. In identifying and analyzing the messages related to such events, keywords play an important role. However, it is difficult to come up with the relevant keywords for many of the resource-limited languages because they lack linguistic knowledge bases such as WordNet. In this paper, we suggest a statistical iterative and interactive learning for keyword expansion (SILK). SILK is a framework that produces a set of candidate keywords based on text modeling techniques, and human experts filter out any irrelevant keywords.
The dynamics of the stomatognathic system from 4D multimodal data

The purpose of this chapter is to discuss methods of acquisition, visualization and analysis of the dynamics of a complex biomedical system, illustrated by the human stomatognathic system. The stomatognathic system consists of the teeth and the skull bones with the maxilla and the mandible. Its dynamics can be described by the change of mutual position of the lower/mandibular part versus the upper/maxillary one due to the physiological motion of opening, chewing and swallowing. In order to analyse the dynamics of the stomatognathic system its morphology and motion has to be digitized, which is done using static and dynamic multimodal imagery like CBCT and 3D scans data and temporal measurements of motion. The integration of multimodal data incorporates different direct and indirect methods of registration - aligning of all the data in the same coordinate system. The integrated sets of data form 4D multimodal data which can be further visualized, modeled, and subjected to multivariate time series analysis. Example results are shown. Although there is no direct method of imaging the TMJ motion, the integration of multimodal data forms an adequate tool. As medical imaging becomes ever more diverse and ever more accessible, organizing the imagery and measurements into unified, comprehensive records can deliver to the doctor the most information in the most accessible form, creating a new quality in data simulation, analysis and interpretation.
Sparse random tensors: Concentration, regularization and applications

We prove a non-asymptotic concentration inequality of sparse inhomogeneous random tensors under the spectral norm. For an order-$k$ inhomogeneous random tensor $T$ with sparsity $p_{\max}\geq \frac{c\log n}{n }$, we show that $\|T-\mathbb E T\|=O(\sqrt{n p_{\max}}\log^{k-2}(n))$ with high probability. The optimality of this bound up to polylog factors is provided by an information theoretic lower bound. By tensor matricization, we extend the range of sparsity to $p_{\max}\geq \frac{c\log n}{n^{k-1}}$ and obtain $\|T-\mathbb E T\|=O(\sqrt{n^{k-1} p_{\max}})$ with high probability. We also provide a simple way to regularize $T$ such that $O(\sqrt{n^{k-1}p_{\max}})$ concentration still holds down to sparsity $p_{\max}\geq \frac{c}{n^{k-1}}$. We present our concentration and regularization results with two applications: (i) a randomized construction of hypergraphs of bounded degrees with good expander mixing properties, (ii) concentration of sparsified tensors under uniform sampling.