Miracle-dz/UniFAR-FTUs huggingface dataset:
Reactive Molecular Dynamics at Constant Pressure via Non-Reactive Force Fields: Extending the Empirical Valence Bond Method to the Isothermal-Isobaric Ensemble.

The Empirical Valence Bond (EVB) method offers a suitable framework to obtain reactive potentials through the coupling of non-reactive force fields. In this formalism, most of the implemented coupling terms are built using functional forms that depend on spatial coordinates, while parameters are fitted against reference data to model the change of chemistry between the participating non-reactive states. In this work, we demonstrate that the use of such coupling terms precludes the computation of the stress tensor for condensed phase systems and prevents the possibility to carry out EVB molecular dynamics in the isothermal-isobaric (NPT) ensemble. Alternatively, we make use of coupling terms that depend on the energy gaps, defined as the energy differences between the participating non-reactive force fields, and derive an general expression for the EVB stress tensor suitable for computation. Implementation of this new methodology is tested for a model of a single reactive malonaldehyde solvated in non-reactive water. Mass densities and probability distributions for the values of the energy gaps computed in the NPT ensemble reveal a negligible role of the reactive potential in the limit of low concentrated solutions, thus corroborating for the first time the validity of standard approximations based on the canonical NVT ensemble, customarily adopted for EVB simulations. The presented formalism also aims to contribute to future implementations and extensions of the EVB method to research the limit of highly-concentrated solutions.
Speech-to-Singing Conversion based on Boundary Equilibrium GAN

This paper investigates the use of generative adversarial network (GAN)-based models for converting the spectrogram of a speech signal into that of a singing one, without reference to the phoneme sequence underlying the speech. This is achieved by viewing speech-to-singing conversion as a style transfer problem. Specifically, given a speech input, and optionally the F0 contour of the target singing, the proposed model generates as the output a singing signal with a progressive-growing encoder/decoder architecture and boundary equilibrium GAN loss functions. Our quantitative and qualitative analysis show that the proposed model generates singing voices with much higher naturalness than an existing non adversarially-trained baseline. For reproducibility, the code will be publicly available at a GitHub repository upon paper publication.
Laboratory rearing of the squirrel flea Ceratophyllus sciurorum sciurorum with notes on its biology

A rearing method for the flea Ceratophyllus (Monopsyllus) sciurorum sciurorum, a pest on farmed mink in Europe, has been developed. The behaviour of egg‐laying and the development period are described at 23 °C and 80% r.h. The eggs of C. sciurorum sciurorum hatched after 4–7 days and cocoons were formed 10–12 days later. The development period from egg to newly emerged adult was 23–32 days. Females emerged, in general, earlier than males.
Performance Analysis and Optimization of Power Systems With Spatially Correlated Noise

Based on stochastic differential equations (SDEs), we analyse the overall performance of heterogeneous power systems network, subject to spatially distributed and correlated noise with random initial conditions. We determine bounds on the $\mathcal {H}_{2}$ norm of the heterogeneous system based on a closed-form of the norm of the homogeneous power system. Then, we formulate possible scenarios for performance optimization and link these to applications for network design and control problems in power systems. Our results are corroborated by numerical simulations from Kundur’s four-machine two-area network after adaption to our setup.
Improving Generalized Zero-Shot Learning by Semantic Discriminator

It is a recognized fact that the classification accuracy of unseen classes in the setting of Generalized Zero-Shot Learning (GZSL) is much lower than that of traditional Zero-Shot Leaning (ZSL). One of the reasons is that an instance is always misclassified to the wrong domain. Here we refer to the seen and unseen classes as two domains respectively. We propose a new approach to distinguish whether the instances come from the seen or unseen classes. First the visual feature of instance is projected into the semantic space. Then the absolute norm difference between the projected semantic vector and the class semantic embedding vector, and the minimum distance between the projected semantic vectors and the semantic embedding vectors of the seen classes are used as discrimination basis. This approach is termed as SD (Semantic Discriminator) because domain judgement of instance is performed in the semantic space. Our approach can be combined with any existing ZSL method and fully supervision classification model to form a new GZSL method. Furthermore, our approach is very simple and does not need any fixed parameters.
Mitigating Advanced Adversarial Attacks with More Advanced Gradient Obfuscation Techniques

Deep Neural Networks (DNNs) are well-known to be vulnerable to Adversarial Examples (AEs). A large amount of efforts have been spent to launch and heat the arms race between the attackers and defenders. Recently, advanced gradient-based attack techniques were proposed (e.g., BPDA and EOT), which have defeated a considerable number of existing defense methods. Up to today, there are still no satisfactory solutions that can effectively and efficiently defend against those attacks. In this paper, we make a steady step towards mitigating those advanced gradient-based attacks with two major contributions. First, we perform an in-depth analysis about the root causes of those attacks, and propose four properties that can break the fundamental assumptions of those attacks. Second, we identify a set of operations that can meet those properties. By integrating these operations, we design two preprocessing functions that can invalidate these powerful attacks. Extensive evaluations indicate that our solutions can effectively mitigate all existing standard and advanced attack techniques, and beat 11 state-of-the-art defense solutions published in top-tier conferences over the past 2 years. The defender can employ our solutions to constrain the attack success rate below 7% for the strongest attacks even the adversary has spent dozens of GPU hours.
Parallel Load Balancing on Constrained Client-Server Topologies

We study parallel Load Balancing protocols for a client-server distributed model defined as follows. There is a set C of n clients and a set S of n servers where each client has (at most) a constant number d ≥ 1 of requests that must be assigned to some server. The client set and the server one are connected to each other via a fixed bipartite graph: the requests of client v can only be sent to the servers in its neighborhood N(v). The goal is to assign every client request so as to minimize the maximum load of the servers. In this setting, efficient parallel protocols are available only for dense topolgies. In particular, a simple symmetric, non-adaptive protocol achieving constant maximum load has been recently introduced by Becchetti et al [4] for regular dense bipartite graphs. The parallel completion time is O(log n) and the overall work is O(n), w.h. p. Motivated by proximity constraints arising in some client-server systems, we devise a simple variant of Becchetti et al's protocol [4] and we analyse it over almost-regular bipartite graphs where nodes may have neighborhoods of small size. In detail, we prove that, w.h. p., this new version has a cost equivalent to that of Becchetti et al's protocol (in terms of maximum load, completion time, and work complexity, respectively) on every almost-regular bipartite graph with degree Ω(log2n). Our analysis significantly departs from that in [4] for the original protocol and requires to cope with non-trivial stochastic-dependence issues on the random choices of the algorithmic process which are due to the worst-case, sparse topology of the underlying graph.
L2UWE: A Framework for the Efficient Enhancement of Low-Light Underwater Images Using Local Contrast and Multi-Scale Fusion

Images captured underwater often suffer from suboptimal illumination settings that can hide important visual features, reducing their quality. We present a novel single-image low-light underwater image enhancer, L2 UWE, that builds on our observation that an efficient model of atmospheric lighting can be derived from local contrast information. We create two distinct models and generate two enhanced images from them: one that highlights finer details, the other focused on darkness removal. A multi-scale fusion process is employed to combine these images while emphasizing regions of higher luminance, saliency and local contrast. We demonstrate the performance of L2 UWE by using seven metrics to test it against seven state-of-the-art enhancement methods specific to underwater and low-light scenes.
A Technical Look At The Indian Personal Data Protection Bill

The Indian Personal Data Protection Bill 2019 provides a legal framework for protecting personal data. It is modeled after the European Union's General Data Protection Regulation(GDPR). We present a detailed description of the Bill, the differences with GDPR, the challenges and limitations in implementing it. We look at the technical aspects of the bill and suggest ways to address the different clauses of the bill. We mostly explore cryptographic solutions for implementing the bill. There are two broad outcomes of this study. Firstly, we show that better technical understanding of privacy is important to clearly define the clauses of the bill. Secondly, we also show how technical and legal solutions can be used together to enforce the bill.
In search of isoglosses: continuous and discrete language embeddings in Slavic historical phonology

This paper investigates the ability of neural network architectures to effectively learn diachronic phonological generalizations in amultilingual setting. We employ models using three different types of language embedding (dense, sigmoid, and straight-through). We find that the Straight-Through model out-performs the other two in terms of accuracy, but the Sigmoid model’s language embeddings show the strongest agreement with the traditional subgrouping of the Slavic languages. We find that the Straight-Through model has learned coherent, semi-interpretable information about sound change, and outline directions for future research.
Dirac-type nodal spin liquid revealed by machine learning

Pursuing fractionalized particles that do not bear properties of conventional bare particles such as electrons or magnons is a challenge in physics. Here we show that machine-learning methods for quantum many-body systems reveal the existence of a quantum spin liquid state with fractionalized spinons in spin-1/2 frustrated Heisenberg model convincingly, if it is combined with the state-of-the-art computational schemes known as the correlation ratio and level spectroscopy methods. The spin excitation spectra signal the emergence of gapless fractionalized spin-1/2 Dirac-type spinons in the distinctive quantum spin liquid phase. Unexplored critical behavior with coexisting power-law-decaying antiferromagnetic and dimer correlations emerges as well. The isomorph of excitations with the cuprate d-wave superconductors revealed here implies tight connection between the present spin liquid and superconductivity. This achievement manifests the power of machine learning for grand challenges in quantum many-body physics.
Larval And Pupal Descriptions of Two Aegopsis Species (Coleoptera: Melolonthidae: Dynastinae)

Abstract The third-instar larvae and pupae of Aegopsis curvicornis Burmeister, and A. bolboceridus (Thomson) are described for the first time based on specimens collected and reared in Cundinamarca, Colombia and Planaltina, DF, Brazil, respectively. A key to the known thirdinstar larvae of the Agaocephalini is provided. Some data on the biology of both species are also included.
Deep Learning for Portfolio Optimisation

We adopt deep learning models to directly optimise the portfolio Sharpe ratio. The framework we present circumvents the requirements for forecasting expected returns and allows us to directly optimise portfolio weights by updating model parameters. Instead of selecting individual assets, we trade Exchange-Traded Funds (ETFs) of market indices to form a portfolio. Indices of different asset classes show robust correlations and trading them substantially reduces the spectrum of available assets to choose from. We compare our method with a wide range of algorithms with results showing that our model obtains the best performance over the testing period, from 2011 to the end of April 2020, including the financial instabilities of the first quarter of 2020. A sensitivity analysis is included to understand the relevance of input features and we further study the performance of our approach under different cost rates and different risk levels via volatility scaling.
Memory and communication efficient algorithm for decentralized counting of nodes in networks

Node counting on a graph is subject to some fundamental theoretical limitations, yet a solution to such problems is necessary in many applications of graph theory to real-world systems, such as collective robotics and distributed sensor networks. Thus several stochastic and naïve deterministic algorithms for distributed graph size estimation or calculation have been provided. Here we present a deterministic and distributed algorithm that allows every node of a connected graph to determine the graph size in finite time, if an upper bound on the graph size is provided. The algorithm consists in the iterative aggregation of information in local hubs which then broadcast it throughout the whole graph. The proposed node-counting algorithm is on average more efficient in terms of node memory and communication cost than its previous deterministic counterpart for node counting, and appears comparable or more efficient in terms of average-case time complexity. As well as node counting, the algorithm is more broadly applicable to problems such as summation over graphs, quorum sensing, and spontaneous hierarchy creation.
Predator avoidance ability of juvenile chinook salmon (Oncorhynchus tshawytscha) subjected to sublethal exposures of gas-supersaturated water

To assess the effects of gas bubble trauma (GBT) on the predator avoidance ability of juvenile chinook salmon (Oncorhynchus tshawytscha), we created groups of fish that differed in prevalence and severity of gas emboli in their lateral lines, fins, and gills by exposing them to 112% total dissolved gas (TDG) for 13 days, 120% TDG for 8 h, or 130% TDG for 3.5 h. We subjected exposed and unexposed control fish simultaneously to predation by northern squawfish (Ptychocheilus oregonensis) in water of normal gas saturation in 6, 18, and 10 tests using prey exposed to 112, 120, and 130% TDG, respectively. Only fish exposed to 130% TDG showed a significant increase in vulnerability to predation. The signs of GBT exhibited by fish sampled just prior to predator exposure were generally more severe in fish exposed to 130% TDG, which had the most extensive occlusion of the lateral line and gill filaments with gas emboli. Fish exposed to 112% TDG had the most severe signs of GBT in the fins. Our results suggest that .
Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments

Deep Reinforcement Learning has been successfully applied in various computer games [8]. However, it is still rarely used in real-world applications, especially for the navigation and continuous control of real mobile robots [13]. Previous approaches lack safety and robustness and/or need a structured environment. In this paper we present our proof of concept for autonomous self-learning robot navigation in an unknown environment for a real robot without a map or planner. The input for the robot is only the fused data from a 2D laser scanner and a RGB-D camera as well as the orientation to the goal. The map of the environment is unknown. The output actions of an Asynchronous Advantage Actor-Critic network (GA3C) are the linear and angular velocities for the robot. The navigator/controller network is pretrained in a high-speed, parallel, and self-implemented simulation environment to speed up the learning process and then deployed to the real robot. To avoid overfitting, we train relatively small networks, and we add random Gaussian noise to the input laser data. The sensor data fusion with the RGB-D camera allows the robot to navigate in real environments with real 3D obstacle avoidance and without the need to fit the environment to the sensory capabilities of the robot. To further increase the robustness, we train on environments of varying difficulties and run 32 training instances simultaneously. Video: supplementary File / YouTube, Code: GitHub
Machine learning and excited-state molecular dynamics

Machine learning is employed at an increasing rate in the research field of quantum chemistry. While the majority of approaches target the investigation of chemical systems in their electronic ground state, the inclusion of light into the processes leads to electronically excited states and gives rise to several new challenges. Here, we survey recent advances for excited-state dynamics based on machine learning. In doing so, we highlight successes, pitfalls, challenges and future avenues for machine learning approaches for light-induced molecular processes.
Image Restoration from Parametric Transformations using Generative Models

When images are statistically described by a generative model we can use this information to develop optimum techniques for various image restoration problems as inpainting, super-resolution, image coloring, generative model inversion, etc. With the help of the generative model it is possible to formulate, in a natural way, these restoration problems as Statistical estimation problems. Our approach, by combining maximum a-posteriori probability with maximum likelihood estimation, is capable of restoring images that are distorted by transformations even when the latter contain unknown parameters. The resulting optimization is completely defined with no parameters requiring tuning. This must be compared with the current state of the art which requires exact knowledge of the transformations and contains regularizer terms with weights that must be properly defined. Finally, we must mention that we extend our method to accommodate mixtures of multiple images where each image is described by its own generative model and we are able of successfully separating each participating image from a single mixture.
Analysis of Tree-Algorithms with Multi-Packet Reception

In this paper, we analyze binary-tree algorithms in a setup in which the receiver can perform multi-packet reception (MPR) of up to and including K packets simultaneously. The analysis addresses both traffic-independent performance as well as performance under Poisson arrivals. For the former case, we show that the throughput, when normalized with respect to the assumed linear increase in resources required to achieve K-MPR capability, tends to the same value that holds for the singlereception setup. However, when coupled with Poisson arrivals in the windowed access scheme, the normalized throughput increases with K, and we present evidence that it asymptotically tends to 1. We also provide performance results for the modified tree algorithm with K-MPR in the clipped access scheme. To the best of our knowledge, this is the first paper that provides an analytical treatment and a number of fundamental insights in the performance of tree-algorithms with MPR.
Conservation of the false water rat (Xeromys myoides) depends on landscape complementation

Identification of habitat variables that are associated with patch occupancy can provide a solid foundation for conservation, especially when habitats and landscapes are affected by human activities. As a case study to investigate the importance of landscape complementation and habitat quality for conservation, we examined the habitat preferences of a vulnerable native rodent, the false water rat (Xeromys myoides Thomas). In this study, results from surveys for animal presence and recording of habitat attributes in localities in south-east Queensland showed that X. myoides prefers a habitat with a wide mangrove zone of short mangroves and a high percentage of relatively tall vegetation cover in the sedge/saltmarsh zone. The absence of X. myoides from areas of likely habitat at a large scale can be attributed, at least in part, to the local habitat variables that overrode predictions based at a large scale. The findings suggest that availability of suitable habitat is a factor limiting the distribution and abundance of X. myoides. It is clear that landscape complementation influences patch occupancy and should be an essential consideration for conservation programs.
Wheat bran extracts: a potent source of natural antioxidants for the stabilization of canola oil

In the present work, the antioxidant activity of different solvent extracts of wheat (var. Inqalab 91) bran was evaluated following different antioxidant assays using canola oil as the oxidation substrate. The bran samples were extracted with 80% and 100% methanol and acetone. A preliminary assessment of the antioxidant activity of the 80 and 100% acetone and methanolic extracts of wheat bran was done by the measurement of % inhibition of peroxidation in a linoleic acid system, total phenolic contents (TPC) and bleachability of β-carotene in the linoleic acid system. Additionally, the canola oil samples were stabilized with crude concentrated extracts and subjected to ambient aging (6 months). The extent of oxidative deterioration was followed by the measurement of peroxide-, p -anisidine-, conjugated dienes-, and trienes- values. The results of ambient stored samples revealed a significant improvement in these oxidation parameters. The overall order of antioxidant activity of the extracts as determined by various antioxidant assays was determined to be; 80% methanolic extract > 100% methanolic extract > 80% acetone extracts . 100% acetone extract. The results of the present comprehensive analysis demonstrate that extracts of the wheat bran indigenous to Pakistan are a viable source of natural antioxidants and might be exploited for commercial and neutraceutical applications.