Miracle-dz/UniFAR-FTUs huggingface dataset:
Control of Battery Storage Systems for the Simultaneous Provision of Multiple Services

In this paper, we propose a control framework for a battery energy storage system (BESS) to provide simultaneously multiple services to the electrical grid. The objective is to maximize the battery exploitation from these services in the presence of uncertainty (load, stochastic distributed generation, and grid frequency). The framework is structured in two phases. In a period-ahead phase, we solve an optimization problem that allocates the battery power and energy budgets to the different services. In the subsequent real-time phase the control set-points for the deployment of such services are calculated separately and superimposed. The control framework is first formulated in a general way and then casted in the problem of providing dispatchability of a medium voltage feeder in conjunction to primary frequency control. The performance of the proposed framework are validated by simulations and real-scale experiments, performed with a grid-connected 560 kWh/720 kVA Li-ion BESS.
Optimal, centralized dynamic curbside parking space zoning

In this paper we formulate a dynamic mixed integer program for optimally zoning curbside parking spaces subject to transportation policy-inspired constraints and regularization terms. First, we illustrate how given some objective of curb zoning valuation as a function of zone type (e.g., paid parking or bus stop), dynamically rezoning involves unrolling this optimization program over a fixed time horizon. Second, we implement two different solution methods that optimize for a given curb zoning value function. In the first method, we solve long horizon dynamic zoning problems via approximate dynamic programming. In the second method, we employ Dantzig-Wolfe decomposition to break-up the mixed-integer program into a master problem and several sub-problems that we solve in parallel; this decomposition accelerates the MIP solver considerably. We present simulation results and comparisons of the different employed techniques on vehicle arrival-rate data obtained for a neighborhood in downtown Seattle, Washington, USA.
Generalizable Patch-Based Neural Rendering

Neural rendering has received tremendous attention since the advent of Neural Radiance Fields (NeRF), and has pushed the state-of-the-art on novel-view synthesis considerably. The recent focus has been on models that overfit to a single scene, and the few attempts to learn models that can synthesize novel views of unseen scenes mostly consist of combining deep convolutional features with a NeRF-like model. We propose a different paradigm, where no deep features and no NeRF-like volume rendering are needed. Our method is capable of predicting the color of a target ray in a novel scene directly, just from a collection of patches sampled from the scene. We first leverage epipolar geometry to extract patches along the epipolar lines of each reference view. Each patch is linearly projected into a 1D feature vector and a sequence of transformers process the collection. For positional encoding, we parameterize rays as in a light field representation, with the crucial difference that the coordinates are canonicalized with respect to the target ray, which makes our method independent of the reference frame and improves generalization. We show that our approach outperforms the state-of-the-art on novel view synthesis of unseen scenes even when being trained with considerably less data than prior work.
Crystal Structure and Theoretical Analysis of Cs 2 Ca 3 (SO 4 ) 4

: Using the homovalent cation substitution strategy, a new sulfate, Cs 2 Ca 3 (SO 4 ) 4 , was successfully prepared using the spontaneous crystallization technique. A single-crystal structure mea-surement suggested that it crystallizes in space group P 2 1 / c , with lattice parameters and molecules per unit cell of a = 9.9153(8), b = 9.3760(6), c = 9.8044(9), β = 118.365(3) ◦ , V = 802.04(11), and Z = 2. In the structure of Cs 2 Ca 3 (SO 4 ) 4 , CaO 6 octahedra and SO 4 tetrahedra are interconnected via a corner-sharing mode to form a three-dimensional framework comprising large cavities ﬁlled with Cs + cations. First-principles calculations and diffuse reﬂectance spectra indicated that Cs 2 Ca 3 (SO 4 ) 4 has a large energy band gap. Moreover, structural comparisons with similar compounds were conducted to explain the role of cations in tuning structural symmetry and birefringence. This paper helps to explain the size effect of cations on structural evolution.
Careful What You Wish For: on the Extraction of Adversarially Trained Models

Recent attacks on Machine Learning (ML) models such as evasion attacks with adversarial examples and models stealing through extraction attacks pose several security and privacy threats. Prior work proposes to use adversarial training to secure models from adversarial examples that can evade the classification of a model and deteriorate its performance. However, this protection technique affects the model’s decision boundary and its prediction probabilities, hence it might raise model privacy risks. In fact, a malicious user using only a query access to the prediction output of a model can extract it and obtain a high-accuracy and high-fidelity surrogate model. To have a greater extraction, these attacks leverage the prediction probabilities of the victim model. Indeed, all previous work on extraction attacks do not take into consideration the changes in the training process for security purposes. In this paper, we propose a framework to assess extraction attacks on adversarially trained models with vision datasets. To the best of our knowledge, our work is the first to perform such evaluation. Through an extensive empirical study, we demonstrate that adversarially trained models are more vulnerable to extraction attacks than models obtained under natural training circumstances. They can achieve up to ×1.2 higher accuracy and agreement with a fraction lower than ×0.75 of the queries. We additionally find that the adversarial robustness capability is transferable through extraction attacks, i.e., extracted Deep Neural Networks (DNNs) from robust models show an enhanced accuracy to adversarial examples compared to extracted DNNs from naturally trained (i.e. standard) models.
Complete versus incomplete definitions of the deformed logarithmic and exponential functions

The recent generalizations of Boltzmann-Gibbs statistics mathematically relies on the deformed logarithmic and exponential functions defined through some deformation parameters. In the present work, we investigate whether a deformed logarithmic/exponential map is a bijection from $\mathbb{R}^+/\mathbb{R}$ (set of positive real numbers/all real numbers) to $\mathbb{R}/\mathbb{R}^+$, as their undeformed counterparts. We show that their inverse map exists only in some subsets of the aforementioned (co)domains. Furthermore, we present conditions which a generalized deformed function has to satisfy, so that the most important properties of the ordinary functions are preserved. The fulfillment of these conditions permits us to determine the validity interval of the deformation parameters. We finally apply our analysis to Tsallis, Kaniadakis, Abe and Borges-Roditi deformed functions.
Digraphwave: Scalable Extraction of Structural Node Embeddings via Diffusion on Directed Graphs

Structural node embeddings, vectors capturing local connectivity information for each node in a graph, have many applications in data mining and machine learning, e.g., network alignment and node classification, clustering and anomaly detection. For the analysis of directed graphs, e.g., transactions graphs, communication networks and social networks, the capability to capture directional information in the structural node embeddings is highly desirable, as is scalability of the embedding extraction method. Most existing methods are nevertheless only designed for undirected graph. Therefore, we present Digraphwave – a scalable algorithm for extracting structural node embeddings on directed graphs. The Digraphwave embeddings consist of compressed diffusion pattern signatures, which are twice enhanced to increase their discriminate capacity. By proving a lower bound on the heat contained in the local vicinity of a diffusion initialization node, theoretically justified diffusion timescale values are established, and Digraphwave is left with only two easy-to-interpret hyperparameters: the embedding dimension and a neighbourhood resolution specifier. In our experiments, the two embedding enhancements, named transposition and aggregation, are shown to lead to a significant increase in macro F1 score for classifying automorphic identities, with Digraphwave outperforming all other structural embedding baselines. Moreover, Digraphwave either outperforms or matches the performance of all baselines on real graph datasets, displaying a particularly large performance gain in a network alignment task, while also being scalable to graphs with millions of nodes and edges, running up to 30x faster than a previous diffusion pattern based method and with a fraction of the memory consumption.
Bayesian Recurrent Units and the Forward-Backward Algorithm

Using Bayes’s theorem, we derive a unit-wise recurrence as well as a backward recursion similar to the forward-backward algorithm. The resulting Bayesian recurrent units can be integrated as recurrent neural networks within deep learning frameworks, while retaining a probabilistic interpretation from the direct correspondence with hidden Markov models. Whilst the contribution is mainly theoretical, experiments on speech recognition indicate that adding the derived units at the end of state-of-the-art recurrent architectures can improve the performance at a very low cost in terms of trainable parameters.
A Wavelet Transform and self-supervised learning-based framework for bearing fault diagnosis with limited labeled data

Traditional supervised bearing fault diagnosis methods rely on massive labelled data, yet annotations may be very time-consuming or infeasible. The fault diagnosis approach that utilizes limited labelled data is becoming increasingly popular. In this paper, a Wavelet Transform (WT) and self-supervised learning-based bearing fault diagnosis framework is proposed to address the lack of supervised samples issue. Adopting the WT and cubic spline interpolation technique, original measured vibration signals are converted to the time-frequency maps (TFMs) with a fixed scale as inputs. The Vision Transformer (ViT) is employed as the encoder for feature extraction, and the self-distillation with no labels (DINO) algorithm is introduced in the proposed framework for self-supervised learning with limited labelled data and sufficient unlabeled data. Two rolling bearing fault datasets are used for validations. In the case of both datasets only containing 1% labelled samples, utilizing the feature vectors extracted by the trained encoder without fine-tuning, over 90\% average diagnosis accuracy can be obtained based on the simple K-Nearest Neighbor (KNN) classifier. Furthermore, the superiority of the proposed method is demonstrated in comparison with other self-supervised fault diagnosis methods.
ProteinKG65: A Knowledge Graph for Protein Science

Existing data-centric methods for protein science generally cannot suﬃciently capture and leverage biology knowledge, which may be crucial for many protein tasks. To facilitate research in this ﬁeld, we create ProteinKG65, a knowledge graph for protein science. Using gene ontology and Uniprot knowledge base as a basis, we transform and integrate various kinds of knowledge with aligned descriptions and protein sequences respectively to GO terms and protein entities. ProteinKG65 is mainly dedicated to providing a specialized protein knowledge graph, bringing the knowledge of Gene Ontology to protein function and structure prediction. The current version contains about 614,099 entities, 5,620,437 triples (including 5,510,437 protein-go triplets and 110,000 GO-GO triplets). We also illustrate the potential applications of Pro-teinKG65 with a prototype. We hope our released knowledge graph can help promote studies in AI for science.
Session-based Cyberbullying Detection in Social Media: A Survey

Cyberbullying is a pervasive problem in online social media, where a bully abuses a victim through a social media session. By investigating cyberbullying perpetrated through social media sessions, recent research has looked into mining patterns and features for modeling and understanding the two defining characteristics of cyberbullying: repetitive behavior and power imbalance. In this survey paper, we define the Session-based Cyberbullying Detection framework that encapsulates the different steps and challenges of the problem. Based on this framework, we provide a comprehensive overview of session-based cyberbullying detection in social media, delving into existing efforts from a data and methodological perspective. Our review leads us to propose evidence-based criteria for a set of best practices to create session-based cyberbullying datasets. In addition, we perform benchmark experiments comparing the performance of state-of-the-art session-based cyberbullying detection models as well as large pre-trained language models across two different datasets. Through our review, we also put forth a set of open challenges as future research directions.
Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning

Optimizing noisy functions online, when evaluating the objective requires experiments on a deployed system, is a crucial task arising in manufacturing, robotics and many others. Often, constraints on safe inputs are unknown ahead of time, and we only obtain noisy information, indicating how close we are to violating the constraints. Yet, safety must be guaranteed at all times, not only for the final output of the algorithm. We introduce a general approach for seeking a stationary point in high dimensional non-linear stochastic optimization problems in which maintaining safety during learning is crucial. Our approach called LB-SGD is based on applying stochastic gradient descent (SGD) with a carefully chosen adaptive step size to a logarithmic barrier approximation of the original problem. We provide a complete convergence analysis of non-convex, convex, and strongly-convex smooth constrained problems, with first-order and zeroth-order feedback. Our approach yields efficient updates and scales better with dimensionality compared to existing approaches. We empirically compare the sample complexity and the computational cost of our method with existing safe learning approaches. Beyond synthetic benchmarks, we demonstrate the effectiveness of our approach on minimizing constraint violation in policy search tasks in safe reinforcement learning (RL).
Optimum design of the variable-volume gas pycnometer for determining the volume of solid particles

Little is known about optimum design of gas pycnometers, so that they can determine the volume of solid particles with the greatest accuracy. The purpose of this study was to investigate the optimum design of the ‘variable-volume’ gas pycnometer, because its ease of handling makes it a good candidate for widespread use. The law of propagation of uncertainty was used to derive a theoretical formula that relates the pycnometer's accuracy to the main sources of random uncertainty (gas-pressure measurements, pycnometer temperature, sample-chamber and piston-chamber volumes). The consequences of this formula in terms of optimizing the geometry and working conditions of the pycnometer are discussed. It was found that some gas pycnometers described in the literature may not have been used under the best conditions. As for the ‘constant-volume’ gas pycnometer, which was considered in a previous study, it seems possible to use commercially available components for constructing a variable-volume gas pycnometer that can determine the volume of solid particles with a relative standard uncertainty smaller than 0.25%. However, an accurately calibrated pressure transducer is required for the variable-volume gas pycnometer (instead, the only requirement for the constant-volume gas pycnometer is that the transducer's response varies linearly with pressure).
Search for Z-primes and Randall-Sundrum gravitons with dimuons in CMS

This paper presents results from studies on observability of particles and Randall-Sundrum gravitons in the dimuon channel in the CMS detector. Full detector simulation and reconstruction were carried out to derive both the discovery potential and results for model discrimination. Effects of systematic uncertainties on the expected reach of the CMS experiment are also presented.
A radiant heating method for performing high-temperature high-strain-rate tests

A radiant heating system is described which is capable of raising the temperature of a test specimen to C in approximately 10 s, while the apparatus remains at room temperature. The system uses two infrared lamps which are controlled with an electronic timer to within a resolution of 0.1 s. The radiant heaters reach 90% of their maximum power in approximately 3 s. The radiant heating system has been used to heat tensile specimens prior to impact loading using the tensile split-Hopkinson pressure bar. Impact tensile tests at a nominal strain rate of and initial temperatures of 20, 200, 400 and C were performed on the titanium alloy 90% Ti-6% Al-4% V. A sharp reduction in yield stress and an increase in elongation at fracture were noted in the high-temperature tests. The implicit finite element code ABAQUS has been used to model the radiant heating and thermal diffusion in the tensile specimen and loading bars. The modelling has been used to determine the thermal distribution in the test specimen prior to loading and the validity of the calibration technique.
Design optimization of a piezoresistive pressure sensor considering the output signal-to-noise ratio

Piezoresistive sensors often suffer from poor signal-to-noise ratios, limiting their use for measuring small pressure differentials. As the pressure range is reduced, and the sensitivity of the sensor increased, the effect of noise on the output signal becomes the limiting factor in the sensor design. In this paper, the optimization of the design to enhance the signal-to-noise ratio of a piezoresistive-type pressure sensor is performed considering different noise components commonly present with these types of sensors. The optimal design for the cases of constant voltage and current is achieved by maximizing the performance index, which is defined as the output signal versus noise. The output voltage and noise are separately analyzed and experimentally tested with respect to the geometric parameters of the piezoresistor and the applied voltage and current. Brownian, Johnson and flicker (1/f) noises are modeled, the latter two of which are dominant for piezoresistive sensors operated at low frequencies to dc. The experimental results show that the optimal design with respect to the resistor length, and number of turns is significantly different when noise is considered. For the special case of the piezoresistive sensor tested, the flicker noise required more turns and longer active elements than if only the effective and non-effective resistances were considered. The optimal Vout/Vnoise was over twice that of the sensor designed maximizing Vout alone.
In situ measurement of residual stress in micromachined thin films using a specimen with composite-layered cantilevers

A new concept of measuring residual stress in micromachined thin films using a specimen with composite-layered cantilevers is proposed. The reliable fabrication process of a composite-layered cantilever and its corresponding modeling are introduced and tested. A few errors that may exist in the previous measurements can be removed by using the proposed method. The residual stress of an additional thin film is obtained by comparing the deflections of the states before and after the deposition of the additional film. Since the structure and fabrication processes described in this paper are widely used in surface micromachining, in situ measurement can be undertaken easily and simply by using the proposed method and a specimen for measurement.
The spatial resolution of hot-wire arrays for the measurement of small-scale turbulence

The measurement of small-scale turbulence quantities with hot-wire arrays has proven difficult due to the difficulty of measuring spatial derivatives of velocity and temperature fluctuations reliably. This paper reviews recent developments in accounting for spatial resolution effects on these arrays. In particular, the effects of wire length, separations between wires and effective wire inclinations are considered for an X-probe, parallel-wire arrays and vorticity arrays. Expressions for correcting spectra and variances of velocity (or temperature) derivatives and vorticity components are presented. Sufficient evidence is now available to validate these corrections, at least when local isotropy is a reasonable approximation. Data from direct numerical simulations (DNS) have played an important role in the context of this validation.
Error analysis and calibration of a spectroscopic Mueller matrix polarimeter using a short-pulse laser source

This paper deals with an error analysis together with the calibration of a spectroscopic Mueller polarimeter with dual rotating retarders; both of them take into consideration the elliptical birefringence characteristics of each rotating device. In doing so, the precision currently given for the Mueller matrix elements is drastically improved. Simulations enabled us, first, to determine the measurement error on each element of the Mueller matrix without a sample and, second, to adopt a method of calibration. Experimental results and corrections highlight the interest of taking elliptical birefringence and dichroism into account.
The NAC Transcription Factor SlNAP2 Regulates Leaf Senescence and Fruit Yield in 33 Tomato 34

80 Leaf senescence is an essential physiological process in plants that supports the recycling of 81 nitrogen and other nutrients to support the growth of developing organs, including young 82 leaves, seeds and fruits. Thus, the regulation of senescence is crucial for evolutionary success 83 in wild populations and for increasing yield in crops. Here we describe the influence of a 84 NAC transcription factor, SlNAP2 ( Solanum lycopersicum NAC-like, activated by 85 apetala3/pistillata) , that controls both leaf senescence and fruit yield in tomato ( Solanum 86 lycopersicum ). SlNAP2 expression increases during age-dependent and dark-induced leaf 87 senescence. We demonstrate that SlNAP2 activates SlSAG113 ( Solanum lycopersicum 88 SENESCENCE-ASSOCIATED GENE 113 ), a homolog of Arabidopsis thaliana SAG113 , 89 chlorophyll degradation genes such as SlSGR1 ( Solanum lycopersicum senescence-inducible 90 chloroplast stay-green protein 1) and SlPAO ( Solanum lycopersicum pheide a oxygenase), 91 and other downstream targets by directly binding to their promoters, thereby promoting leaf 92 senescence. Furthermore, SlNAP2 directly controls the expression of genes important for 93 abscisic acid (ABA) biosynthesis Solanum lycopersicum 9- cis -epoxycarotenoid dioxygenase 94 1 ( SlNCED1 ), transport Solanum lycopersicum ABC transporter G family member 40 95 ( SlABCG40 ) and degradation Solanum lycopersicum ABA 8′-hydroxylase ( SlCYP707A2 ), 96 indicating that SlNAP2 has a complex role in establishing ABA homeostasis during leaf 97 senescence. Inhibiting SlNAP2 expression in transgenic tomato plants impedes leaf 98 senescence but enhances fruit yield and sugar content likely due to prolonged leaf 99 photosynthesis in aging tomato plants. Our data indicate that SlNAP2 has a central role in 100 controlling leaf senescence and fruit yield in tomato. fruit Arrow-ending lines, positive regulation; T-ending regulation dashed line indicates a possible, but not experimentally leaves and fruits. Comparative analysis reveals significant differences in gene expression signalling pathways senescence