Miracle-dz/UniFAR-FTUs huggingface dataset:
Differential Parametric Formalism for the Evolution of Gaussian States: Nonunitary Evolution and Invariant States

In the differential approach elaborated, we study the evolution of the parameters of Gaussian, mixed, continuous variable density matrices, whose dynamics are given by Hermitian Hamiltonians expressed as quadratic forms of the position and momentum operators or quadrature components. Specifically, we obtain in generic form the differential equations for the covariance matrix, the mean values, and the density matrix parameters of a multipartite Gaussian state, unitarily evolving according to a Hamiltonian H^. We also present the corresponding differential equations, which describe the nonunitary evolution of the subsystems. The resulting nonlinear equations are used to solve the dynamics of the system instead of the Schrödinger equation. The formalism elaborated allows us to define new specific invariant and quasi-invariant states, as well as states with invariant covariance matrices, i.e., states were only the mean values evolve according to the classical Hamilton equations. By using density matrices in the position and in the tomographic-probability representations, we study examples of these properties. As examples, we present novel invariant states for the two-mode frequency converter and quasi-invariant states for the bipartite parametric amplifier.
A Novel Distributed Representation of News (DRNews) for Stock Market Predictions

In this study, a novel Distributed Representation of News (DRNews) model is developed and applied in deep learning-based stock market predictions. With the merit of integrating contextual information and cross-documental knowledge, the DRNews model creates news vectors that describe both the semantic information and potential linkages among news events through an attributed news network. Two stock market prediction tasks, namely the short-term stock movement prediction and stock crises early warning, are implemented in the framework of the attention-based Long Short Term-Memory (LSTM) network. It is suggested that DRNews substantially enhances the results of both tasks comparing with five baselines of news embedding models. Further, the attention mechanism suggests that short-term stock trend and stock market crises both receive influences from daily news with the former demonstrates more critical responses on the information related to the stock market {\em per se}, whilst the latter draws more concerns on the banking sector and economic policies.
Hashing-based Non-Maximum Suppression for Crowded Object Detection

In this paper, we propose an algorithm, named hashing-based non-maximum suppression (HNMS) to efficiently suppress the non-maximum boxes for object detection. Non-maximum suppression (NMS) is an essential component to suppress the boxes at closely located locations with similar shapes. The time cost tends to be huge when the number of boxes becomes large, especially for crowded scenes. The basic idea of HNMS is to firstly map each box to a discrete code (hash cell) and then remove the boxes with lower confidences if they are in the same cell. Considering the intersection-over-union (IoU) as the metric, we propose a simple yet effective hashing algorithm, named IoUHash, which guarantees that the boxes within the same cell are close enough by a lower IoU bound. For two-stage detectors, we replace NMS in region proposal network with HNMS, and observe significant speed-up with comparable accuracy. For one-stage detectors, HNMS is used as a pre-filter to speed up the suppression with a large margin. Extensive experiments are conducted on CARPK, SKU-110K, CrowdHuman datasets to demonstrate the efficiency and effectiveness of HNMS. Code is released at \url{this https URL}.
Learning Force Control for Contact-Rich Manipulation Tasks With Rigid Position-Controlled Robots

Reinforcement Learning (RL) methods have been proven successful in solving manipulation tasks autonomously. However, RL is still not widely adopted on real robotic systems because working with real hardware entails additional challenges, especially when using rigid position-controlled manipulators. These challenges include the need for a robust controller to avoid undesired behavior, that risk damaging the robot and its environment, and constant supervision from a human operator. The main contributions of this work are, first, we proposed a learning-based force control framework combining RL techniques with traditional force control. Within said control scheme, we implemented two different conventional approaches to achieve force control with position-controlled robots; one is a modified parallel position/force control, and the other is an admittance control. Secondly, we empirically study both control schemes when used as the action space of the RL agent. Thirdly, we developed a fail-safe mechanism for safely training an RL agent on manipulation tasks using a real rigid robot manipulator. The proposed methods are validated both on simulation and a real robot with an UR3 e-series robotic arm.
User behavior and token adoption on ERC20

Cryptocurrencies and Blockchain-based technologies are disrupting all markets. While the potential of such technologies remains to be seen, there is a current need to understand emergent patterns of user behavior and token adoption in order to design future products. In this paper we analyze the social dynamics taking place during one arbitrary day on the ERC20 platform. We characterize the network of token transactions among agents. We show heterogeneous profiles of user behavior, portfolio diversity, and token adoption. While most users are specialized in transacting with a few tokens, those that have diverse portfolios are bridging across large parts of the network and may jeopardize the system stability. We believe this work to be a foundation for unveiling the usage dynamics of crypto-currencies networks.
Attention-Based Neural Bag-of-Features Learning for Sequence Data

In this paper, we propose 2D-Attention (2DA), a generic attention formulation for sequence data, which acts as a complementary computation block that can detect and focus on relevant sources of information for the given learning objective. The proposed attention module is incorporated into the recently proposed Neural Bag of Feature (NBoF) model to enhance its learning capacity. Since 2DA acts as a plug-in layer, injecting it into different computation stages of the NBoF model results in different 2DA-NBoF architectures, each of which possesses a unique interpretation. We conducted extensive experiments in financial forecasting, audio analysis as well as medical diagnosis problems to benchmark the proposed formulations in comparison with existing methods, including the widely used Gated Recurrent Units. Our empirical analysis shows that the proposed attention formulations can not only improve performances of NBoF models but also make them resilient to noisy data.
Effect of Sanitizers on Planktonic Edwardsiella tarda Isolated from Asian Stinging Catfish Heteropneustes fossilis (Bloch 1794)

Incidence of Edwardsiella tarda in Asian stinging catfish, Heteropneustes fossilis (Bloch), and its sensitivity to antibiotics and sanitizers was studied. Five out of 10 representative lots of H. fossilis were positive for E. tarda. The minimum bactericidal concentration (MBC) of sanitizers (hydrogen peroxide, glutaraldehyde, benzalkonium chloride, sodium hypochlorite, and iodophor) on planktonic E. tarda varied with strains, suspending media (distilled water, physiological saline, pond water, and tryptic soy broth), and combination of factors. The time taken by the sanitizers to achieve 5-log reductions varied greatly between 1 min and 180 min at X and 2X MBCs of sanitizers. Benzalkonium chloride, glutaraldehyde, and sodium hypochlorite were effective in reducing the planktonic E. tarda at concentrations ranging from 3.13 ppm for glutaraldehyde to 25 ppm for sodium hypochlorite. Edwardsiella tarda strains were highly sensitive to ciprofloxacin and exhibited varying degrees of resistance to other antibiotics.
AGVNet: Attention Guided Velocity Learning for 3D Human Motion Prediction

Prediction of human motion plays a significant role in human-machine interactions for a variety of real-life applications. In this paper, we propose a novel attention-guided velocity learning network, AGVNet, that utilizes multi-order information such as positions and velocities derived from the dynamic states of the human body for predicting human motion. Unlike existing methods, our network formulates the human motion system as a dynamic system and predicts human motion using the position and velocity of poses. Specifically, a multi-level Encoder is proposed to model the dynamics of moving joints at the axis level and joint level. A recursive feedforward Decoder generates future poses recursively by reusing the predictions at the previous time-steps and fusing multiple order information from both the velocity and position space. To avoid the error accumulation, a unique loss function, ATPL (Attention Temporal Prediction Loss), is designed with decreasing attention to the later predictions, making the network more accurate for predictions at the early time-steps. The experiments on two benchmark datasets (i.e., Human$3.6$M and $3$DPW) confirm that our method achieves state-of-the-art performance with improved effectiveness. The code will be made public once the paper is accepted.
Variance of Longest Run Duration in a Random Bitstring

We continue an earlier study, starting with unconstrained $n$-bitstrings, focusing now less on average behavior and more on uncertainty. The interplay between $\bullet$ longest runs of 0s and of 1s, when bitstrings are multus $\bullet$ longest runs of 0s and bitsums (# of 1s), when bitstrings are solus $\\$ is examined. While negative correlations approach zero as $n \rightarrow \infty$ in the former (for clumped 1s), the limit is evidently nonzero in the latter (for separated 1s). Similar analysis is possible when both 0s and 1s are clumped (bimultus), and when 0s are clumped but 1s are separated (persolus). Our methods are experimentally-based.
How Useful are Reviews for Recommendation? A Critical Review and Potential Improvements

We investigate a growing body of work that seeks to improve recommender systems through the use of review text. Generally, these papers argue that since reviews 'explain' users' opinions, they ought to be useful to infer the underlying dimensions that predict ratings or purchases. Schemes to incorporate reviews range from simple regularizers to neural network approaches. Our initial findings reveal several discrepancies in reported results, partly due to (e.g.) copying results across papers despite changes in experimental settings or data pre-processing. First, we attempt a comprehensive analysis to resolve these ambiguities. Further investigation calls for discussion on a much larger problem about the "importance" of user reviews for recommendation. Through a wide range of experiments, we observe several cases where state-of-the-art methods fail to outperform existing baselines, especially as we deviate from a few narrowly-defined settings where reviews are useful. We conclude by providing hypotheses for our observations, that seek to characterize under what conditions reviews are likely to be helpful. Through this work, we aim to evaluate the direction in which the field is progressing and encourage robust empirical evaluation.
The Threat to the Validity of Predictive Mutation Testing: The Impact of Uncovered Mutants

Predictive Mutation Testing (PMT) is a technique to predict whether a mutant will be killed by using machine learning approaches. Researchers have proposed various machine learning methods for PMT under the cross-project setting. However, they did not consider the impact of uncovered mutants. A mutant is uncovered if the statement on which the mutant is generated is not executed by any test cases. We show that uncovered mutants inflate previous PMT results. Moreover, we aim at proposing an alternative approach to improve PMT and suggesting a different interpretation for cross-project PMT. We replicated the previous PMT research. We also proposed an approach based on the combination of Random Forest and Gradient Boosting to improve the PMT results. We empirically evaluated our approach on the same 654 Java projects provided by the previous PMT literature. Our results indicate that the performance of PMT drastically decreases in terms of AUC from 0.83 to 0.51. Furthermore, PMT performs worse than random guesses on 27% of the test projects. The proposed approach improves the PMT results by achieving the average AUC value of 0.61.
Monitoring and Diagnosability of Perception Systems

Perception is a critical component of high-integrity applications of robotics and autonomous systems, such as self-driving vehicles. In these applications, failure of perception systems may put human life at risk, and a broad adoption of these technologies requires the development of methodologies to guarantee and monitor safe operation. Despite the paramount importance of perception systems, currently there is no formal approach for system-level monitoring. In this work, we propose a mathematical model for runtime monitoring and fault detection and identification in perception systems. Towards this goal, we draw connections with the literature on diagnosability in multiprocessor systems, and generalize it to account for modules with heterogeneous outputs that interact over time. The resulting temporal diagnostic graphs (i) provide a framework to reason over the consistency of perception outputs –across modules and over time– thus enabling fault detection, (ii) allow us to establish formal guarantees on the maximum number of faults that can be uniquely identified in a given perception system, and (iii) enable the design of efficient algorithms for fault identification. We demonstrate our monitoring system, dubbed PerSyS, in realistic simulations using the LGSVL self-driving simulator and the Apollo Auto autonomy software stack, and show that PerSyS is able to detect failures in challenging scenarios (including scenarios that have caused self-driving car accidents in recent years), and is able to correctly identify faults while entailing a minimal computation overhead (< 5 ms on a single-core CPU).
Variabilidade espacial da resistência do solo à penetração em plantio direto

The objective of this work was to characterize the spatial variability of soil resistance to penetration (RP) in no tillage farming, identifying the degree of soil compaction. The RP was determined directly in the field, in systematic way in three soils types. The first experimental area was in Santa Maria, in Hapludalf, with 120g clay kg-1 in the layer of 0-20cm. The second, located in Cruz Alta, in a Haplortox, with 500g clay kg-1 in the 0-20cm layer and the third experimental area located in Colonel Barros, in a Hapludox, with 600g clay kg-1 in the 0-20cm layer. The values of RP were analyzed by descriptive statistics and for geoestatistical techniques. In the three soils, the technique of the krigeagem was used to make surface maps, identifying three classes of RP, the one that, we called degree compaction (EC). In Hapludalf, smallest EC was considered the values of RP among 0.58MPa to 0.88MPa, the middleman EC among 0.89MPa to 1.18MPa and largest EC, values of RP between 1.19MPa and 1.50MPa. In Haplortox, in the smallest EC the values of RP were selected varying among 1.70MPa to 2.22MPa, the middleman EC, RP among 2.23MPa to 2.75MPa and, in largest EC, values of RP among 2.76MPa to 3.20MPa. In Hapludox the values of RP in smallest EC were among 0.20MPa to 1.59MPa, in the middleman EC among 1.60MPa to 2.10MPa and in largest EC, among 2.11MPa to 2.80MPa. In the three areas, the distribution of the soil degree compaction decreased of the headboard area (larger EC) for the center of the farming (smaller EC).
Parasitism and the Dynamics of Ungulate Grazing Systems

This study examines the potential impact of directly transmitted macroparasites on the population dynamics of ungulate grazing systems. It combines simple, previously published mathematical models for plant-herbivore and host-parasite interactions in a model that captures the essential features of the plant-herbivore-parasite system. The most ecologically complex and interesting case, in which herbivore numbers are food limited and allowed to vary more or less naturally, is considered in detail. On one hand, this analysis confirms the potential role of parasites as functional "top predators" and in particular the potential of parasite-induced host mortality to increase the plant equilibrium of otherwise overgrazed systems. This result underlines the dangers of indiscriminate anthelmintic application in marginal grazing systems without a consideration of the possible ecological consequences of parasite control. By contrast, the analysis also underlines two unique features of parasitism in the context of grazing system dynamics. First, the effect of the coupling of gut parasite transmission to food consumption simplifies grazing system dynamics by linearizing the herbivore's functional response near the parasite equilibrium. Second, the destabilizing impact of parasite-induced reductions in host reproductive rate can throw the system into chaotic plant-herbivore-parasite cycles. The ecological implications of these results are discussed.
Palaeobiology and evolution of the late Cenozoic freshwater molluscs of the Turkana Basin: Unionidae Rafinesque, 1820, partim Coelatura (Bivalvia: Unionoidea)

The Plio-Pleistocene freshwater molluscs of the Omo-Turkana Basin were long considered to be a prima facie example of punctuated equilibrium evolution. However, the absence of a study on their taxonomy has significantly hampered the debate on assumed evolutionary patterns. This paper addresses the systematics and palaeobiology of the bivalve genus Coelatura Conrad, 1853 in the basin. It reveals that a Coelatura species was assigned to the extinct genus Pseudobovaria Adam, 1957 in earlier studies, while another species formerly assigned to Coelatura is not discussed here because it belongs to the extinct Pseudodiplon Adam, 1957. Six Coelatura species are recognized in the Plio-Pleistocene Omo-Turkana Basin, two of which are new to science: C. rhomboidalis sp. nov. and C. magna sp. nov. These two species are considered lacustrine adapted species, both with a strongly dorsally protruded umbo. This adaptation apparently evolved twice independently in the basin within a ∼1 Ma period, but it is not observed in .
Search for exotic neutrino-electron interactions using solar neutrinos in XMASS-I

In recent years, the larger size and lower background of dark matter search detectors have opened up a new frontier of searching for new physics other than dark matter using these detectors. One of them is the search for new properties of neutrinos by observing the interaction of neutrinos at low energy. XMASS is a multi-purpose experiment using xenon exclusively in ites liquid xenon (LXe) and is located at the Kamioka Observatory in Japan. We searched for exotic neutrino-electron interactions that could be produced by either a neutrino millicharge, a neutrino magnetic moment, or by dark photons, which might affect the intearction cross section of solar neutrinos in XMASS. We analyzed data taken between November 2013 and March 2016 amounting to 711 live days. No significant signal has been found above the predicted background level in detector. We obtained an upper limit for neutrino millicharge of 5.4 × 10–11e for all flavors of neutrino. We also set individual flavors limits for υμ and ν τ, which are the best limits obtained by direct detection. We also obtain an upper limit for the neutrino magnetic moment of 1.8× 10–10μ B . In addition, we obtain upper limits for the coupling constant of dark photons in the U(1) B–L model. This result almost excludes the possibility to understand the muon g — 2 anomaly by dark photons.
Leader-Follower Synchronization and ISS Analysis for a Network of Boundary-Controlled Wave PDEs

A network of agents, modeled by a class of wave PDEs, is under investigation. One agent in the network plays the role of a leader, and all the remaining “follower” agents are required to asymptotically track the state of the leader. Only boundary sensing of the agent’s state is assumed, and each agent is controlled through the boundary by Neumann-type actuation. A linear interaction protocol is proposed and analyzed by means of a Lyapunov-based approach. A simple set of tuning rules, guaranteeing the exponential achievement of synchronization, is obtained. In addition, an exponential ISS relation, characterizing the effects on the tracking accuracy of boundary and in-domain disturbances, is derived for the closed loop system.
Bat Optimized Watershed based Segmentation of Lamina Cribrosa

The segmentation of Lamina Cribrosa(LC) is a challenging task to detect the glaucomatous damage. In this paper a new method of segmenting the LC using bat optimized Watershed segmentation is done. By using wavelet transform LC structures are decomposed. Then, the decomposed image is optimized using Bat algorithm and by applying histogram equalization the optimized image is normalized. Watershed algorithm is used to segment the Lamina Cribrosa from its outer layer. Using some parameters like PSNR, MSE, F-Measure, rand index, sensitivity, specificity, SSIM and accuracy, the performance of the proposed system is calculated. The results show that the proposed method provides higher accuracy of 99.29%.
The Maki-Thompson rumor model with $k$-stifling on homogeneous trees

In this paper we study the Maki-Thompson rumor model on homogeneous trees. The basic version of the model may be defined by assuming that a population represented by a graph is subdivided into three classes of individuals: ignorants, spreaders and stiflers. A spreader tells the rumor to any of its (nearest) ignorant neighbors at rate one. At the same rate, a spreader becomes a stifler after a contact with other (nearest neighbor) spreaders, or stiflers. In this work we study this model on homogeneous trees, which is formulated as a continuous-times Markov chain, and we extend our analysis to the generalization in which each spreader ceases to propagate the rumor right after being involved in a given number of stifling experiences. We study sufficient conditions under which the rumor either becomes extinct or survives with positive probability.
S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation

We propose a sequential variational autoencoder to learn disentangled representations of sequential data (e.g., videos and audios) under self-supervision. Specifically, we exploit the benefits of some readily accessible supervision signals from input data itself or some off-the-shelf functional models and accordingly design auxiliary tasks for our model to utilize these signals. With the supervision of the signals, our model can easily disentangle the representation of an input sequence into static factors and dynamic factors (i.e., time-invariant and time-varying parts). Comprehensive experiments across videos and audios verify the effectiveness of our model on representation disentanglement and generation of sequential data, and demonstrate that, our model with self-supervision performs comparable to, if not better than, the fully-supervised model with ground truth labels, and outperforms state-of-the-art unsupervised models by a large margin.