Miracle-dz/UniFAR-FTUs huggingface dataset:
Relieving the Need for Bi-Level Decision-Making for Optimal Retail Pricing via Online Meta-Prediction of Data-Driven Demand Response of HVAC Systems

Price-based demand response (DR) of heating, ventilating, and air-conditioning (HVAC) systems is a challenging task, requiring comprehensive models to represent the building thermal dynamics and game theoretic interactions among participants. This paper proposes an online learning-based strategy for a distribution system operator (DSO) to determine optimal electricity prices, considering the optimal DR of HVAC systems in commercial buildings. An artificial neural network (ANN) is trained with building energy data and represented using an explicit set of linear and nonlinear equations, without physics-based model parameters. An optimization problem for price-based DR is then formulated using this equation set and repeatedly solved offline, producing data on optimal DR schedules for various conditions of electricity prices and building thermal environments. Another ANN is then trained online to directly predict DR schedules for day-ahead electricity prices, which is referred to as meta-prediction (MP). By replacing the DR optimization problem with the MP-enabled ANN, an optimal electricity pricing strategy can be implemented using a single-level decision-making structure, which is simpler and more practical than a bi-level one. In simulation case studies, the proposed single-level strategy is verified to successfully reflect the game theoretic relations between the DSO and commercial building operators, so that they effectively exploit the operational flexibility of the HVAC systems to make the DR application profitable, while ensuring the grid voltage stability and occupants thermal comfort.
Unravelling the physics of multiphase AGN winds through emission line tracers

Observations of emission lines in active galactic nuclei (AGNs) often find fast (∼1000 km s−1) outflows extending to kiloparsec scales, seen in ionized, neutral atomic and molecular gas. In this work we present radiative transfer calculations of emission lines in hydrodynamic simulations of AGN outflows driven by a hot wind bubble, including non-equilibrium chemistry, to explore how these lines trace the physical properties of the multiphase outflow. We find that the hot bubble compresses the line-emitting gas, resulting in higher pressures than in the ambient interstellar medium or that would be produced by the AGN radiation pressure. This implies that observed emission line ratios such as [O iv]$_{25 \, \rm {\mu m}}$ / [Ne ii]$_{12 \, \rm {\mu m}}$, [Ne v]$_{14 \, \rm {\mu m}}$ / [Ne ii]$_{12 \, \rm {\mu m}}$, and [N iii]$_{57 \, \rm {\mu m}}$ / [N ii]$_{122 \, \rm {\mu m}}$ constrain the presence of the bubble and hence the outflow driving mechanism. However, the line-emitting gas is under-pressurized compared to the hot bubble itself, and much of the line emission arises from gas that is out of pressure, thermal and/or chemical equilibrium. Our results thus suggest that assuming equilibrium conditions, as commonly done in AGN line emission models, is not justified if a hot wind bubble is present. We also find that ≳50 per cent of the mass outflow rate, momentum flux, and kinetic energy flux of the outflow are traced by lines such as [N ii]$_{122 \, \rm {\mu m}}$ and [Ne iii]$_{15 \, \rm {\mu m}}$ (produced in the 10$^{4} \, \rm {K}$ phase) and [C ii]$_{158 \, \rm {\mu m}}$ (produced in the transition from 10$^{4} \, \rm {K}$ to 100 K).
One-Shot Learning with Triplet Loss for Vegetation Classification Tasks

Triplet loss function is one of the options that can significantly improve the accuracy of the One-shot Learning tasks. Starting from 2015, many projects use Siamese networks and this kind of loss for face recognition and object classification. In our research, we focused on two tasks related to vegetation. The first one is plant disease detection on 25 classes of five crops (grape, cotton, wheat, cucumbers, and corn). This task is motivated because harvest losses due to diseases is a serious problem for both large farming structures and rural families. The second task is the identification of moss species (5 classes). Mosses are natural bioaccumulators of pollutants; therefore, they are used in environmental monitoring programs. The identification of moss species is an important step in the sample preprocessing. In both tasks, we used self-collected image databases. We tried several deep learning architectures and approaches. Our Siamese network architecture with a triplet loss function and MobileNetV2 as a base network showed the most impressive results in both above-mentioned tasks. The average accuracy for plant disease detection amounted to over 97.8% and 97.6% for moss species classification.
The Martini Model in Materials Science

The Martini model, a coarse‐grained force field initially developed with biomolecular simulations in mind, has found an increasing number of applications in the field of soft materials science. The model's underlying building block principle does not pose restrictions on its application beyond biomolecular systems. Here, the main applications to date of the Martini model in materials science are highlighted, and a perspective for the future developments in this field is given, particularly in light of recent developments such as the new version of the model, Martini 3.
Neural-network-based parameter estimation for quantum detection

Artificial neural networks (NNs) bridge input data into output results by approximately encoding the function that relates them. This is achieved after training the network with a collection of known inputs and results leading to an adjustment of the neuron connections and biases. In the context of quantum detection schemes, NNs find a natural playground. In particular, in the presence of a target (e.g. an electromagnetic field), a quantum sensor delivers a response, i.e. the input data, which can be subsequently processed by a NN that outputs the target features. In this work we demonstrate that adequately trained NNs enable to characterize a target with (i) minimal knowledge of the underlying physical model (ii) in regimes where the quantum sensor presents complex responses and (iii) under a significant shot noise due to a reduced number of measurements. We exemplify the method with a development for 171Yb+ atomic sensors. However, our protocol is general, thus applicable to arbitrary quantum detection scenarios.
Effective multi-view registration of point sets based on student's t mixture model

Recently, Expectation-maximization (EM) algorithm has been introduced as an effective means to solve multi-view registration problem. Most of the previous methods assume that each data point is drawn from the Gaussian Mixture Model (GMM), which is difficult to deal with the noise with heavy-tail or outliers. Accordingly, this paper proposed an effective registration method based on Student's t Mixture Model (StMM). More specially, we assume that each data point is drawn from one unique StMM, where its nearest neighbors (NNs) in other point sets are regarded as the t-distribution centroids with equal covariances, membership probabilities, and fixed degrees of freedom. Based on this assumption, the multi-view registration problem is formulated into the maximization of the likelihood function including all rigid transformations. Subsequently, the EM algorithm is utilized to optimize rigid transformations as well as the only t-distribution covariance for multi-view registration. Since only a few model parameters require to be optimized, the proposed method is more likely to obtain the desired registration results. Besides, all t-distribution centroids can be obtained by the NN search method, it is very efficient to achieve multi-view registration. What's more, the t-distribution takes the noise with heavy-tail into consideration, which makes the proposed method be inherently robust to noises and outliers. Experimental results tested on benchmark data sets illustrate its superior performance on robustness and accuracy over state-of-the-art methods.
Chromate in food samples: an artefact of wrongly applied analytical methodology?

Recently several papers were published in highly ranked journals on the presence of Cr(VI) in tea infusions, bread samples and plants. These statements were made on the basis of determination of total Cr concentrations in alkaline and aqueous sample extracts by ETAAS, without applying any speciation analysis. If Cr(VI) really exists in bread samples and tea infusions, consumption of bread and tea would represent long-term chronic exposure to Cr(VI) and a health threat for the majority of the human population. It is well accepted that due to the presence of organic matter foodstuffs of plant and animal origin cannot contain Cr(VI). Therefore, to show that the reported data on the presence of Cr(VI) in foodstuffs are an artefact of inappropriately applied analytical methodology, investigation of speciation was carried out by high performance liquid chromatography-inductively coupled plasma mass spectrometry (HPLC-ICP-MS). 50Cr(VI) and 53Cr(III) stable isotopes were used to follow species interconversions during the extraction procedures. Separated Cr species eluted from the column were followed at m/z 50, 52 and 53. The high sensitivity of the HPLC-ICP-MS method (LOD for m/z 52, 0.03 μg Cr(VI) L−1) enabled reliable determination of Cr(VI) at concentration levels that were much lower than the total Cr content present in sample extracts. The speciation analysis data demonstrated that in all samples analysed Cr(VI) concentrations were below the LOD. In tea infusions 50Cr(VI) was almost completely reduced due to the presence of antioxidants, while the high content of organic matter in bread appreciably reduced 50Cr(VI) even in highly alkaline (pH 12) bread extracts. The data confirmed that Cr(VI) does not exist in foodstuffs of plant origin and provided some conclusive evidence that the same can be expected for foods of animal origin.
Successive Projection for Solving Systems of Nonlinear Equations/Inequalities

Solving large-scale systems of nonlinear equations/inequalities is a fundamental problem in computing and optimization. In this paper, we propose a generic successive projection (SP) framework for this problem. The SP sequentially projects the current iterate onto the constraint set corresponding to each nonlinear (in)equality. It extends von Neumann's alternating projection for finding a point in the intersection of two linear subspaces, Bregman's method for finding a common point of convex sets and the Kaczmarz method for solving systems of linear equations to the more general case of multiple nonlinear and nonconvex sets. The existing convergence analyses on randomized Kaczmarz are merely applicable to linear case. There are no theoretical convergence results of the SP for solving nonlinear equations. This paper presents the first proof that the SP locally converges to a solution of nonlinear equations/inequalities at a linear rate. Our work establishes the convergence theory of the SP for the case of multiple nonlinear and nonconvex sets. Besides cyclic and randomized projections, we devise two new greedy projection approaches that significantly accelerate the convergence. Furthermore, the theoretical bounds of the convergence rates are derived. We reveal that the convergence rates are related to the Hoffman constants of the Jacobian matrix of the nonlinear functions at the solution. Applying the SP to solve the graph realization problem, which attracts much attention in theoretical computer science, is discussed.
Event Data Quality: A Survey

Event data are prevalent in diverse domains such as financial trading, business workflows and industrial IoT nowadays. An event is often characterized by several attributes denoting the meaning associated with the corresponding occurrence time/duration. From traditional operational systems in enterprises to online systems for Web services, event data is generated from physical world uninterruptedly. However, due to the variety and veracity features of Big data, event data generated from heterogeneous and dirty sources could have very different event representations and data quality issues. In this work, we summarize several typical works on studying data quality issues of event data, including: (1) event matching, (2) event error detection, (3) event data repair, and (4) approximate pattern matching.
Under an iron sky: On the entropy at the start of the Universe

Abstract Curiously, our Universe was born in a low entropy state, with abundant free energy to power stars and life. The form that this free energy takes is usually thought to be gravitational: the Universe is almost perfectly smooth, and so can produce sources of energy as matter collapses under gravity. It has recently been argued that a more important source of low-entropy energy is nuclear: the Universe expands too fast to remain in nuclear statistical equilibrium, effectively shutting off nucleosynthesis in the first few minutes, providing leftover hydrogen as fuel for stars. Here, we fill in the astrophysical details of this scenario and seek the conditions under which a Universe will emerge from early nucleosynthesis as almost-purely iron. In so doing, we identify a hitherto-overlooked character in the story of the origin of the second law: matter–antimatter asymmetry.
Application of the Efros Theorem to the Function Represented by the Inverse Laplace Transform of s-μ exp(-sν)

Using a special case of the Efros theorem which was derived by Wlodarski, and operational calculus, it was possible to derive many infinite integrals, finite integrals and integral identities for the function represented by the inverse Laplace transform. The integral identities are mainly in terms of convolution integrals with the Mittag–Leffler and Volterra functions. The integrands of determined integrals include elementary functions (power, exponential, logarithmic, trigonometric and hyperbolic functions) and the error functions, the Mittag–Leffler functions and the Volterra functions. Some properties of the inverse Laplace transform of s−μexp(−sν) with μ≥0 and 0
A Dual-Store Structure for Knowledge Graphs

To effectively manage increasing knowledge graphs in various domains, a hot research topic, knowledge graph storage management, has emerged. Existing methods are classified to relational stores and native graph stores. Relational stores are able to store large-scale knowledge graphs and convenient in updating knowledge, but the query performance weakens obviously when the selectivity of a knowledge graph query is large. Native graph stores are efficient in processing complex knowledge graph queries due to its index-free adjacent property, but they are inapplicable to manage a large-scale knowledge graph due to limited storage budgets or inflexible updating process. Motivated by this, we propose a dual-store structure which leverages a graph store to accelerate the complex query process in the relational store. However, it is challenging to determine <italic>what</italic> data to transfer from relational store to graph store at <italic>what</italic> time. To address this problem, we formulate it as a Markov Decision Process and derive a physical design tuner <inline-formula><tex-math notation="LaTeX">${{\sf DOTIL}}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">DOTIL</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq1-3093200.gif"/></alternatives></inline-formula> based on reinforcement learning. With <inline-formula><tex-math notation="LaTeX">${{\sf DOTIL}}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">DOTIL</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq2-3093200.gif"/></alternatives></inline-formula>, the dual-store structure is adaptive to dynamic changing workloads. Experimental results on real knowledge graphs demonstrate that our proposed dual-store structure improves query performance up to average 50.11 percent compared with the most commonly used relational stores.
Semantic Layout Manipulation With High-Resolution Sparse Attention

We tackle the problem of semantic image layout manipulation, which aims to manipulate an input image by editing its semantic label map. A core problem of this task is how to transfer visual details from the input images to the new semantic layout while making the resulting image visually realistic. Recent work on learning cross-domain correspondence has shown promising results for global layout transfer with dense attention-based warping. However, this method tends to lose texture details due to the resolution limitation and the lack of smoothness constraint on correspondence. To adapt this paradigm for the layout manipulation task, we propose a high-resolution sparse attention module that effectively transfers visual details to new layouts at a resolution up to 512x512. To further improve visual quality, we introduce a novel generator architecture consisting of a semantic encoder and a two-stage decoder for coarse-to-fine synthesis. Experiments on the ADE20k and Places365 datasets demonstrate that our proposed approach achieves substantial improvements over the existing inpainting and layout manipulation methods.
Contrastive Learning with Adversarial Perturbations for Conditional Text Generation

Recently, sequence-to-sequence (seq2seq) models with the Transformer architecture have achieved remarkable performance on various conditional text generation tasks, such as machine translation. However, most of them are trained with teacher forcing with the ground truth label given at each time step, without being exposed to incorrectly generated tokens during training, which hurts its generalization to unseen inputs, that is known as the ``exposure bias" problem. In this work, we propose to mitigate the conditional text generation problem by contrasting positive pairs with negative pairs, such that the model is exposed to various valid or incorrect perturbations of the inputs, for improved generalization. However, training the model with naive contrastive learning framework using random non-target sequences as negative examples is suboptimal, since they are easily distinguishable from the correct output, especially so with models pretrained with large text corpora. Also, generating positive examples requires domain-specific augmentation heuristics which may not generalize over diverse domains. To tackle this problem, we propose a principled method to generate positive and negative samples for contrastive learning of seq2seq models. Specifically, we generate negative examples by adding small perturbations to the input sequence to minimize its conditional likelihood, and positive examples by adding large perturbations while enforcing it to have a high conditional likelihood. Such ``hard'' positive and negative pairs generated using our method guides the model to better distinguish correct outputs from incorrect ones. We empirically show that our proposed method significantly improves the generalization of the seq2seq on three text generation tasks - machine translation, text summarization, and question generation.
LiveChess2FEN: a Framework for Classifying Chess Pieces based on CNNs

Automatic digitization of chess games using computer vision is a significant technological challenge. This problem is of much interest for tournament organizers and amateur or professional players to broadcast their over-the-board (OTB) games online or analyze them using chess engines. Previous work has shown promising results, but the recognition accuracy and the latency of state-of-the-art techniques still need further enhancements to allow their practical and affordable deployment. We have investigated how to implement them on an Nvidia Jetson Nano single-board computer effectively. Our first contribution has been accelerating the chessboard's detection algorithm. Subsequently, we have analyzed different Convolutional Neural Networks for chess piece classification and how to map them efficiently on our embedded platform. Notably, we have implemented a functional framework that automatically digitizes a chess position from an image in less than 1 second, with 92% accuracy when classifying the pieces and 95% when detecting the board.
Clustered Sparse Channel Estimation for Massive MIMO Systems by Expectation Maximization-Propagation (EM-EP)

We study the problem of downlink channel estimation in multi-user massive multiple input multiple output (MIMO) systems. To this end, we consider the use of Bayesian compressive sensing approach in which the clustered sparse structure of the channel in the angular domain is employed to reduce the pilot overhead. To capture the clustered structure, we employ a conditionally independent identically distributed Bernoulli-Gaussian prior on the sparse vector representing the channel, and a Markov prior on its support vector. An expectation propagation (EP) algorithm is developed to approximate the intractable joint distribution of the sparse vector and its support with a distribution from an exponential family. The approximate distribution is then used for direct estimation of the channel. The EP algorithm assumes that the model parameters are known a priori. Since these parameters are unknown, we estimate these parameters using the expectation maximization (EM) algorithm. The combination of EM and EP referred to as EM-EP algorithm is reminiscent of the variational EM approach. Simulation results show that the proposed EM-EP algorithm outperforms several recently-proposed algorithms in the literature.
A network analysis on cloud gaming: Stadia, GeForce Now and PSNow

Cloud gaming is a class of services that promises to revolutionize the videogame market. It allows the user to play a videogame with essential equipment while using a remote server for the actual execution. The multimedia content is streamed through the network from the server to the user. Hence, this service requires low latency and a large bandwidth to work properly with low response time and high-definition video. Three of the leading tech companies (Google, Sony, and NVIDIA) entered this market with their products, and others, like Microsoft and Amazon, are also launching their platforms. However, these companies have released little information about their cloud gaming operation and how they utilize the network. In this work, we study cloud gaming services from the network point of view. We collect more than 200 packet traces under different application settings and network conditions from a broadband network to poor mobile network conditions, for 3 cloud gaming services, namely Stadia from Google, GeForce Now from NVIDIA and PS Now from Sony. We analyze the employed protocols and the workload that they impose on the network. We find that GeForce Now and Stadia use the RTP protocol to stream the multimedia content, with the latter relying on the standard WebRTC APIs. Depending on the network and video quality, they result in bandwidth-hungry services consuming up to 45 Mbit/s. PS Now instead uses only undocumented protocols and never exceeds 13 Mbit/s. 4G mobile networks can often sustain these loads, while traditional 3G connections struggle. The systems quickly react to deteriorated network conditions, and packet losses up to 5% do not cause a reduction in resolution.
L’usage de l’exécution symbolique pour la déobfuscation binaire en milieu industriel

Cette these a ete faite dans un cadre industriel ou les activites principales sont la retro-ingenierie pour la recherche de vulnerabilites et la verification de certaines proprietes de securite sur des programmes deja compiles. La premiere partie de cette these porte sur la collecte et le partage des problematiques industrielles lors de l’analyse de programmes binaires. Base sur ces problematiques, un cadre de travail d’analyse dynamique binaire a ete developpe et formalise. Des exemples reels d’utilisation y sont ensuite presentes tels que la detection de predicats opaques dans les conditions de branchement. Enfin, une nouvelle approche automatique permettant la devirtualisation de code binaire y est presentee en combinant les fonctionnalites du cadre de travail ainsi que celles d’autres outils.