Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of Probability weighted ARX models with arbitrary domains

Hybrid system identification is a key tool to achieve reliable models of Cyber-Physical Systems from data. PieceWise Affine models guarantees universal approximation, local linearity and equivalence to other classes of hybrid system. Still, PWA identification is a challenging problem, requiring the concurrent solution of regression and classification tasks. In this work, we focus on the identification of PieceWise Auto Regressive with eXogenous input models with arbitrary regions (NPWARX), thus not restricted to polyhedral domains, and characterized by discontinuous maps. To this end, we propose a method based on a probabilistic mixture model, where the discrete state is represented through a multinomial distribution conditioned by the input regressors. The architecture is conceived following the Mixture of Expert concept, developed within the machine learning field. To achieve nonlinear partitioning, we parametrize the discriminant function using a neural network. Then, the parameters of both the ARX submodels and the classifier are concurrently estimated by maximizing the likelihood of the overall model using Expectation Maximization. The proposed method is demonstrated on a nonlinear piece-wise problem with discontinuous maps.
Long-timescale X-Ray Variability of BAL and Mini-BAL Quasars

We investigated the rest-frame ≈0.1–5 yr X-ray variability properties of an unbiased and uniformly selected sample of 24 broad absorption line (BAL) and 35 mini-BAL quasars, making it the largest representative sample used to investigate such variability. We find that the distributions of X-ray variability amplitudes of these quasar populations are statistically similar to that of non-BAL, radio-quiet (typical) quasars.
Towards Formal Verification of Program Obfuscation

Code obfuscation involves transforming a program to a new version that performs the same computation but hides the functionality of the original code. An important property of such a transformation is that it preserves the behavior of the original program. In this paper, we lay the foundation for studying and reasoning about code obfuscating transformations, and show how the preservation of certain behaviours may be formally verified. To this end, we apply techniques of formal specification and verification using the Coq Proof Assistant. We use and extend an existing encoding of a simple imperative language in Coq along with an encoding of Hoare logic for reasoning about this language. We formulate what it means for a program’s semantics to be preserved by an obfuscating transformation, and give formal machine-checked proofs that these behaviours or properties hold. We also define a lower-level block-structured language which is "wrapped around" our imperative language, allowing us to model certain flattening transformations and treat blocks of codes as objects in their own right.
Pluteus dianae and P. punctatus resurrected, with first records from eastern and northern Europe

The type specimens of Pluteus dianae and P. punctatus from the Czech Republic were studied morphologically and molecularly. New collections identified by nrITS sequence analyses extend the distribution of P. dianae to Denmark, European Russia, and the Asian part of Turkey and of P. punctatus to Sweden. The application of these names is discussed; both belong in the P. plautus complex, and data on European and North American taxa in this complex are summarised and compared with P. dianae and P. punctatus. Pluteus aestivus is considered a nomen dubium. Key words—Agaricales, Czechia, Pluteaceae, Pluteus sect. Hispidoderma, taxonomy
Metagenomic Analysis of the Lacrimal Caruncle Region of Canis lupus familiaris Using MiSeq Technology

The identification of ocular microbiota may allow early diagnosis and treatment strategies against eye diseases and disorders with canines. However, clinical microbial identification has been limited to cloning and conventional culturebased studies, which typically underestimate community diversity. In this report, lllumina MiSeq analysis of the 16S rRNA gene was used to examine the microbiome of the lacrimal caruncle region from five healthy dogs. The breeds sampled were a Golden Retriever (Dog A), a Weimaraner (Dog B), a Shih Tzu mix (Dog C), a Yorkie mix (Dog D), and a Dachshund (Dog E). MiSeq analysis revealed a total of 370 operational taxonomic units (OTUs) representing 79 families of bacteria. Generally, Dog A had the most unique bacterial profile in terms of families that were represented, with samples from this dog having contributions from families that were not observed above 2% of total OTUs in the other dogs. For example, the Oxalobacteraceae (Massilia spp.), Micrococcaceae (Arthrobacterspp.), and Enterobacteriaceae (Pantoea spp.) families were uniquely found in Dog A at levels above 2% of the total OTUs. Dogs A and B harbored very high percentages of Pseudomonadaceae (up to 65% in the right eye of Dog A), which was attributed entirely to the genus Pseudomonas. These dogs also had relatively high percentages of Moraxellaceae (up to 21 % in the left eye of Dog A), which were almost entirely from the Psychrobacter and Acinetobacter genera. The microbiomes from Dog A and Dog B were similar with respect to the families present and their relative abundance, while the microbiomes of Dogs C, D, and E were more similar to each other. Overall, this
Cross Learning in Deep Q-Networks

In this work, we propose a novel cross Q-learning algorithm, aim at alleviating the well-known overestimation problem in value-based reinforcement learning methods, particularly in the deep Q-networks where the overestimation is exaggerated by function approximation errors. Our algorithm builds on double Q-learning, by maintaining a set of parallel models and estimate the Q-value based on a randomly selected network, which leads to reduced overestimation bias as well as the variance. We provide empirical evidence on the advantages of our method by evaluating on some benchmark environment, the experimental results demonstrate significant improvement of performance in reducing the overestimation bias and stabilizing the training, further leading to better derived policies.
Studying explicit 
U(1)A
 symmetry breaking in a hot and magnetized two flavor nonlocal NJL model constrained using lattice results

We study the two flavour non-local Nambu--Jona-Lasinio (NJL) model in the presence of a magnetic field and explored the chiral crossover in presence of a non-local form of the 't Hooft determinant term. Its coupling is governed by a dimensionless parameter $c$. This term is responsible for the explicit breaking of $U(1)_A$ symmetry. We have attempted a systematic analysis of the model parameters by fitting to self-consistent lattice QCD calculations. Three parameters of the model are fixed by $eB=0$ results from published lattice QCD on the chiral condensate, the pion decay constant ($F_\pi$), and the pion mass. The difference of the $u$ and $d$ quark condensates in the presence of a magnetic field ($eB$) is quite sensitive to $c$ and we fix $c$ using published lattice QCD results for this observable. We see no evidence that $c$ depends on $eB$. The crossover temperature decreases with increasing $eB$ only for condensate values at the lower end of the allowed values (as already seen in \cite{Pagura:2016pwr}) and $F_\pi$ at the upper end of the allowed values. We further check our model predictions by calculating the topological susceptibility with the fitted $c$ values and compare it with lattice results. Since the topological susceptibility is related to the extent of the $U(1)_A$ symmetry breaking, we find that it is sensitive to the value of $c$.
DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue

A long-standing goal of task-oriented dialogue research is the ability to flexibly adapt dialogue models to new domains. To progress research in this direction, we introduce DialoGLUE (Dialogue Language Understanding Evaluation), a public benchmark consisting of 7 task-oriented dialogue datasets covering 4 distinct natural language understanding tasks, designed to encourage dialogue research in representation-based transfer, domain adaptation, and sample-efficient task learning. We release several strong baseline models, demonstrating performance improvements over a vanilla BERT architecture and state-of-the-art results on 5 out of 7 tasks, by pre-training on a large open-domain dialogue corpus and task-adaptive self-supervised training. Through the DialoGLUE benchmark, the baseline methods, and our evaluation scripts, we hope to facilitate progress towards the goal of developing more general task-oriented dialogue models.
Robust monotonic convergent iterative learning control design: An LMI‐based method

This work investigates robust monotonic convergent iterative learning control (ILC) for uncertain linear systems in both time and frequency domains, and the ILC algorithm optimizing the convergence speed in terms of l2 norm of error signals is derived. First, it is shown that the robust monotonic convergence of the ILC system can be established equivalently by the positive definiteness of a matrix polynomial over some set. Then, a necessary and sufficient condition in the form of sum of squares for the positive definiteness is proposed, which is amendable to the feasibility of linear matrix inequalities. Based on such a condition, the optimal ILC algorithm that maximizes the convergence speed is obtained by solving a set of convex optimization problems. Moreover, the order of the learning function can be chosen arbitrarily so that the designers have the flexibility to decide the complexity of the learning algorithm.
Gamma-ray bursts: not so much deadlier than we thought

We analyse the additional effect on planetary atmospheres of recently detected gamma-ray burst afterglow photons in the range up to 1 TeV. For an Earth-like atmosphere, we find that there is a small additional depletion in ozone versus that modeled for only prompt emission. We also find a small enhancement of muon flux at the planet surface. Overall, we conclude that the additional afterglow emission, even with TeV photons, does not result in a significantly larger impact over that found in past studies.
Channel Modeling and Performance Analysis of Airplane-Satellite Terahertz Band Communications

Wireless connectivity in airplanes is becoming more important, demanded, and common. One of the largest bottlenecks with the in-flight Internet is that the airplane is far away from both the satellites and the ground base stations during most of the flight time. Maintaining a reliable and high-rate wireless connection with the airplane over such a long-range link thus becomes a challenge. Microwave frequencies allow for long link distances but lack the data rate to serve up to several hundreds of potential onboard customers. Higher bands in the millimeter-wave spectrum (30 GHz–300 GHz) have, therefore, been utilized to overcome the bandwidth limitations. Still, the per-user throughput with state-of-the-art millimeter-wave systems is an order of magnitude lower than the one available with terrestrial wireless networks. In this paper, we take a step further and study the channel characteristics for the terahertz band (THz, 0.3 THz–10 THz) in order to map the feasibility of this band for aviation. We first propose a detailed channel model for aerial THz communications taking into account both the non-flat Earth geometry and the main features of the frequency-selective THz channel. We then apply this model to estimate the characteristics of aerial THz links in different conditions. We finally determine the altitudes where the use of airplane-to-satellite THz connection becomes preferable over the airplane-to-ground THz link. Our results reveal that the capacity of the airborne THz link may reach speeds ranging from 50–150 Gbps, thus enabling cellular-equivalent data rates to the passengers and staff during the entire flight.
TOI 540 b: A Planet Smaller than Earth Orbiting a Nearby Rapidly Rotating Low-mass Star

We present the discovery of TOI 540 b, a hot planet slightly smaller than Earth orbiting the low-mass star 2MASS J05051443-4756154. The planet has an orbital period of P = 1.239149 days (±170 ms) and a radius of , and is likely terrestrial based on the observed mass–radius distribution of small exoplanets at similar insolations. The star is 14.008 pc away and we estimate its mass and radius to be and , respectively. The star is distinctive in its very short rotational period of hr and correspondingly small Rossby number of 0.007 as well as its high X-ray-to-bolometric luminosity ratio of based on a serendipitous XMM-Newton detection during a slew operation. This is consistent with the X-ray emission being observed at a maximum value of as predicted for the most rapidly rotating M dwarfs. TOI 540 b may be an alluring target to study atmospheric erosion due to the strong stellar X-ray emission. It is also among the most accessible targets for transmission and emission spectroscopy and eclipse photometry with the James Webb Space Telescope, and may permit Doppler tomography with high-resolution spectroscopy during transit. This discovery is based on precise photometric data from the Transiting Exoplanet Survey Satellite and ground-based follow-up observations by the MEarth team.
Nonlinear Monte Carlo methods with polynomial runtime for high-dimensional iterated nested expectations

The approximative calculation of iterated nested expectations is a recurring challenging problem in applications. Nested expectations appear, for example, in the numerical approximation of solutions of backward stochastic differential equations (BSDEs), in the numerical approximation of solutions of semilinear parabolic partial differential equations (PDEs), in statistical physics, in optimal stopping problems such as the approximative pricing of American or Bermudan options, in risk measure estimation in mathematical finance, or in decision-making under uncertainty. Nested expectations which arise in the above named applications often consist of a large number of nestings. However, the computational effort of standard nested Monte Carlo approximations for iterated nested expectations grows exponentially in the number of nestings and it remained an open question whether it is possible to approximately calculate multiply iterated high-dimensional nested expectations in polynomial time. In this article we tackle this problem by proposing and studying a new class of full-history recursive multilevel Picard (MLP) approximation schemes for iterated nested expectations. In particular, we prove under suitable assumptions that these MLP approximation schemes can approximately calculate multiply iterated nested expectations with a computational effort growing at most polynomially in the number of nestings $ K \in \mathbb{N} = \{1, 2, 3, \ldots \} $, in the problem dimension $ d \in \mathbb{N} $, and in the reciprocal $\frac{1}{\varepsilon}$ of the desired approximation accuracy $ \varepsilon \in (0, \infty) $.
A Comprehensive Multi-Period Optimal Power Flow Framework for Smart LV Networks

This paper presents an extensive multi-period optimal power flow framework, with new modelling elements, for smart LV distribution systems that rely on residential flexibility for combating operational issues. A detailed performance assessment of different setups is performed, including: ZIP flexible loads (FLs), varying degrees of controllability of conventional residential devices, such as electric vehicles (EVs) or photovoltaics (PVs), by the distribution system operator (DSO) (adhering to customer-dependent restrictions) and full exploitation of the capabilities offered by state-of-the-art inverter technologies. A comprehensive model-dependent impact assessment is performed, including phase imbalances, neutral and ground wires and load dependencies. The de-congestion potential of common residential devices is highlighted, analyzing capabilities such as active power redistribution, reactive power support and phase balancing. Said potential is explored on setups where the DSO can make only partial adjustments on customer profiles, rather than (as is common) deciding on the full profiles. The extensive analysis can be used by DSOs and researchers alike to make informed decisions on the required levels of modelling detail, the connected devices and the degrees of controlability. The formulation is computationally efficient, scaling well to medium-size systems, and can serve as an excellent basis for building more tractable or more targeted approaches.
Learned Fine-Tuner for Incongruous Few-Shot Learning

Model-agnostic meta-learning (MAML) effectively meta-learns an initialization of model parameters for few-shot learning where all learning problems share the same format of model parameters -- congruous meta-learning. We extend MAML to incongruous meta-learning where different yet related few-shot learning problems may not share any model parameters. A Learned Fine Tuner (LFT) is used to replace hand-designed optimizers such as SGD for the task-specific fine-tuning. Here, MAML instead meta-learns the parameters of this LFT across incongruous tasks leveraging the learning-to-optimize (L2O) framework such that models fine-tuned with LFT (even from random initializations) adapt quickly to new tasks. As novel contributions, we show that the use of LFT within MAML (i) offers the capability to tackle few-shot learning tasks by meta-learning across incongruous yet related problems (e.g., classification over images of different sizes and model architectures), and (ii) can efficiently work with first-order and derivative-free few-shot learning problems. Theoretically, we quantify the difference between LFT (for MAML) and L2O. Empirically, we demonstrate the effectiveness of LFT through both synthetic and real problems and a novel application of generating universal adversarial attacks across different image sources in the few-shot learning regime.
Automatically Tailoring Static Analysis to Custom Usage Scenarios

In recent years, there has been significant progress in the development and industrial adoption of static analyzers. Such analyzers typically provide a large, if not huge, number of configurable options controlling the precision and performance of the analysis. A major hurdle in integrating static analyzers in the software-development life cycle is tuning their options to custom usage scenarios, such as a particular code base or certain resource constraints. In this paper, we propose a technique that automatically tailors a static analyzer, specifically an abstract interpreter, to the code under analysis and any given resource constraints. We implement this technique in a framework called TAILOR, which we use to perform an extensive evaluation on real-world benchmarks. Our experiments show that the configurations generated by TAILOR are vastly better than the default analysis options, vary significantly depending on the code under analysis, and most remain tailored to several subsequent code versions.
ChemoVerse: Manifold Traversal of Latent Spaces for Novel Molecule Discovery

In order to design a more potent and effective chemical entity, it is essential to identify molecular structures with the desired chemical properties. Recent advances in generative models using neural networks and machine learning are being widely used by many emerging startups and researchers in this domain to design virtual libraries of drug-like compounds. Although these models can help a scientist to produce novel molecular structures rapidly, the challenge still exists in the intelligent exploration of the latent spaces of generative models, thereby reducing the randomness in the generative procedure. In this work we present a manifold traversal with heuristic search to explore the latent chemical space. Different heuristics and scores such as the Tanimoto coefficient, synthetic accessibility, binding activity, and QED drug-likeness can be incorporated to increase the validity and proximity for desired molecular properties of the generated molecules. For evaluating the manifold traversal exploration, we produce the latent chemical space using various generative models such as grammar variational autoencoders (with and without attention) as they deal with the randomized generation and validity of compounds. With this novel traversal method, we are able to find more unseen compounds and more specific regions to mine in the latent space. Finally, these components are brought together in a simple platform allowing users to perform search, visualization and selection of novel generated compounds.
Residual acoustic echo suppression based on efficient multi-task convolutional neural network

Acoustic echo degrades the user experience in voice communication systems thus needs to be suppressed completely. We propose a real-time residual acoustic echo suppression (RAES) method using an efficient convolutional neural network. The double talk detector is used as an auxiliary task to improve the performance of RAES in the context of multi-task learning. The training criterion is based on a novel loss function, which we call as the suppression loss, to balance the suppression of residual echo and the distortion of near-end signals. The experimental results show that the proposed method can efficiently suppress the residual echo under different circumstances.
Reality-Assisted Evolution of Soft Robots through Large-Scale Physical Experimentation: A Review

Abstract We introduce the framework of reality-assisted evolution to summarize a growing trend towards combining model-based and model-free approaches to improve the design of physically embodied soft robots. In silico, data-driven models build, adapt, and improve representations of the target system using real-world experimental data. By simulating huge numbers of virtual robots using these data-driven models, optimization algorithms can illuminate multiple design candidates for transference to the real world. In reality, large-scale physical experimentation facilitates the fabrication, testing, and analysis of multiple candidate designs. Automated assembly and reconfigurable modular systems enable significantly higher numbers of real-world design evaluations than previously possible. Large volumes of ground-truth data gathered via physical experimentation can be returned to the virtual environment to improve data-driven models and guide optimization. Grounding the design process in physical experimentation ensures that the complexity of virtual robot designs does not outpace the model limitations or available fabrication technologies. We outline key developments in the design of physically embodied soft robots in the framework of reality-assisted evolution.
Projection-Free Adaptive Gradients for Large-Scale Optimization

The complexity in large-scale optimization can lie in both handling the objective function and handling the constraint set. In this respect, stochastic Frank-Wolfe algorithms occupy a unique position as they alleviate both computational burdens, by querying only approximate first-order information from the objective and by maintaining feasibility of the iterates without using projections. In this paper, we improve the quality of their first-order information by blending in adaptive gradients. Starting from the design of adaptive gradient algorithms, we propose to solve the occurring constrained optimization subproblems \emph{very} incompletely via a fixed and small number of iterations of the Frank-Wolfe algorithm (often times only $2$ iterations), in order to preserve the low per-iteration complexity. We derive convergence rates and demonstrate the computational advantage of our method over the state-of-the-art stochastic Frank-Wolfe algorithms on both convex and nonconvex objectives.