Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental study of H∞ control for a flexible plate

This paper presents theoretical and experimental studies of H∞ control for a flexible plate. Firstly, the dynamic equation of the plate with time delay is established with consideration of external disturbance. Then the standard state equation without explicit time delay is deduced by using a particular integral transformation to the time-delay equation. Finally, a H∞ time-delay controller is designed based on the standard state equation using the method of linear matrix inequality. In the controller designed, it contains not only the current step of state feedback but also the linear combination of some former steps of control. In the experiment, piezoelectric patches are used as actuators and sensors. The experiment system is structured based on the DSP TMS320F2812. Single time delay and double time delay are both considered in the experiment. The robustness of H∞ controller against the structural parameter of the plate and time delay is experimentally investigated. Simulation and experimental results indicate that the control system may suffer from instability if time delay is not treated in control design. The time-delay controller presented in this paper can effectively deal with time delay in the system, and is available for small-time delay and large-time delay as well. Furthermore, this delay controller is robust to the variance of structural parameter of the plate and time delay.
Enhancement of x-ray yields from heteronuclear cluster plasmas irradiated by intense laser light

We report a new method to enhance the x-ray emission from nano-cluster plasmas formed upon irradiation by intense femtosecond-duration laser pulses. Our experiments demonstrate that when Ar clusters are doped with H2O the time-integrated yield of Ar K x-ray emission is enhanced by approximately 12-fold in comparison to that obtained from pure Ar clusters under otherwise identical experimental conditions. A significant alteration in the time-dependent electron density is achieved by the presence of an H2O dopant, and this could be the possible reason for the enhancement that is observed.
INTERNATIONAL CENTRE FOR THEORETICAL PHYSICS ON THE RENORMALIZATION AND SHORT DISTANCE PROPERTIES OF HADRONIC OPERATORS IN QCD

We discuss the renonnalization of the contour dependent gauge invariant composite bilocal or string operator q(2) exp g I A dt 1 '1 in QCD, which one would naturally associate with the hadronic bound states. We then discuss the short distance expansion of this operator as the end points merge. We argue that some functional average over all possible paths between x^ and x« may be the appropriate operator to describe the mesonic modes in QCD and that the short distance expansion may provide as a valuable insight/to the nature of this functional average. Most of our considerations are for smooth contours however we propose a simple vay of treating on the Bane footing the additional divergences pointed out by Polyakov due to sharp beads in the contour. The latter confirms the conclusions reached for smooth contours.
Optimal Fault-Tolerant Data Fusion in Sensor Networks: Fundamental Limits and Efficient Algorithms

Distributed estimation in the context of sensor networks is considered, where distributed agents are given a set of sensor measurements, and are tasked with estimating a target variable. A subset of sensors are assumed to be faulty. The objective is to minimize i) the mean squared estimation error at each node (accuracy objective), and ii) the mean squared distance between the estimates at each pair of nodes (consensus objective). It is shown that there is an inherent tradeoff between the former and latter objectives. Assuming a general stochastic model, the sensor fusion algorithm optimizing this tradeoff is characterized through a computable optimization problem, and a Cramér-Rao type lower bound for the achievable accuracy-consensus loss is obtained. Finding the optimal sensor fusion algorithm is computationally complex. To address this, a general class of low-complexity Brooks-Iyengar Algorithms are introduced, and their performance, in terms of accuracy and consensus objectives, is compared to that of optimal linear estimators through case study simulations of various scenarios.
CiteBench: A benchmark for Scientific Citation Text Generation

Science progresses by incrementally building upon the prior body of knowledge documented in scientific publications. The acceleration of research across many fields makes it hard to stay up-to-date with the recent developments and to summarize the ever-growing body of prior work. To target this issue, the task of citation text generation aims to produce accurate textual summaries given a set of papers-to-cite and the citing paper context. Existing studies in citation text generation are based upon widely diverging task definitions, which makes it hard to study this task systematically. To address this challenge, we propose CiteBench: a benchmark for citation text generation that unifies multiple diverse datasets and enables standardized evaluation of citation text generation models across task designs and domains. Using the new benchmark, we investigate the performance of multiple strong baselines, test their transferability between the datasets, and deliver new insights into the task definition and evaluation to guide future research in citation text generation. We make the code for CiteBench publicly available at https://github.com/UKPLab/citebench.
MANER: Mask Augmented Named Entity Recognition for Extreme Low-Resource Languages

This paper investigates the problem of Named Entity Recognition (NER) for extreme low-resource languages with only a few hundred tagged data samples. NER is a fundamental task in Natural Language Processing (NLP). A critical driver accelerating NER systems' progress is the existence of large-scale language corpora that enable NER systems to achieve outstanding performance in languages such as English and French with abundant training data. However, NER for low-resource languages remains relatively unexplored. In this paper, we introduce Mask Augmented Named Entity Recognition (MANER), a new methodology that leverages the distributional hypothesis of pre-trained masked language models (MLMs) for NER. Thetoken in pre-trained MLMs encodes valuable semantic contextual information. MANER re-purposes thetoken for NER prediction. Specifically, we prepend thetoken to every word in a sentence for which we would like to predict the named entity tag. During training, we jointly fine-tune the MLM and a new NER prediction head attached to eachtoken. We demonstrate that MANER is well-suited for NER in low-resource languages; our experiments show that for 100 languages with as few as 100 training examples, it improves on state-of-the-art methods by up to 48% and by 12% on average on F1 score. We also perform detailed analyses and ablation studies to understand the scenarios that are best-suited to MANER.
Speaking Style Conversion in the Waveform Domain Using Discrete Self-Supervised Units

We introduce DISSC, a novel, lightweight method that converts the rhythm, pitch contour and timbre of a recording to a target speaker in a textless manner. Unlike DISSC, most voice conversion (VC) methods focus primarily on timbre, and ignore people's unique speaking style (prosody). The proposed approach uses a pretrained, self-supervised model for encoding speech to discrete units, which makes it simple, effective, and fast to train. All conversion modules are only trained on reconstruction like tasks, thus suitable for any-to-many VC with no paired data. We introduce a suite of quantitative and qualitative evaluation metrics for this setup, and empirically demonstrate that DISSC significantly outperforms the evaluated baselines. Code and samples are available at https://pages.cs. huji.ac. il/adiyoss-lab/dissc/.
Infinite horizon generalized predictive control

Generalized predictive control (GPC) is an attractive predictive control formulation, the major weakness of which has been the lack of general stability guarantees. As a result, different variants of the method have been proposed which yield stabilizing control. Most of these however rely on the addition to the control objective of a set of equality constraints which sub-optimally restricts the class of inputs considered in the optimization of performance. On the other hand, the use of an infinite costing horizon is known to lead naturally to stability but has not been popular due to implementation difficulties. It has indeed not been clear how to minimize GPC-type costs, in the constrained case, when an infinite costing horizon is used. This paper presents a practicalmeans ofimplementing GPC withan infinite costing horizon with and without inequality constraints.
Synthesis and Evaluation of a Domain-specific Large Data Set for Dungeons & Dragons

This paper introduces the Forgotten Realms Wiki (FRW) data set and domain specific natural language generation using FRW along with related analyses. Forgotten Realms is the de-facto default setting of the popular open ended tabletop fantasy role playing game, Dungeons&Dragons. The data set was extracted from the Forgotten Realms Fandom wiki consisting of more than over 45,200 articles. The FRW data set is constituted of 11 sub-data sets in a number of formats: raw plain text, plain text annotated by article title, directed link graphs, wiki info-boxes annotated by the wiki article title, Poincar\'e embedding of first link graph, multiple Word2Vec and Doc2Vec models of the corpus. This is the first data set of this size for the Dungeons&Dragons domain. We then present a pairwise similarity comparison benchmark which utilizes similarity measures. In addition, we perform D&D domain specific natural language generation using the corpus and evaluate the named entity classification with respect to the lore of Forgotten Realms.
From NEA and NIA to NESAS and SCAS: Demystifying the 5G Security Ecosystem

Despite the numerous pompous statements regarding 5G, it is indisputable that 5G creates a radical shift in telecommunications. The main reason is that 5G is an enabler of numerous applications we have long envisioned and either simulated or implemented in test environments, partially or on a smaller scale. 5G will soon unlock the potential of smart cities, industry 4.0, and IoT, to name a few. However, a crucial question is how much we can trust this technology. Since this technology will soon become the core infrastructure for all of the above, it is critical to understand the fundamental security mechanisms that comprise this technology and the guarantees they provide to assess the potential risks we are exposed to. This work follows a non-technical yet bottom-up approach to introduce the reader to the core security mechanisms and establish a baseline for the security of 5G, to demystify the principal notions and processes. Based on the above, we streamline future directions and highlight possible threats.
Electronic transport and thermoelectric properties of phosphorene nanodisk under an electric field

The Seebeck coefficient is an important quantity in determining the thermoelectric efficiency of a material. Phosphorene is a two-dimensional material with a puckered structure, which makes its properties anisotropic. In this work, a phosphorene nanodisk (PDisk) with a radius of 3.1 nm connected to two zigzag phosphorene nanoribbons is studied, numerically, by the tight-binding and non-equilibrium Green’s function (NEGF) methods in the presence of transverse and perpendicular electric fields. Our results show that the change in structure from a zigzag ribbon to a disk form creates an energy gap in the structure, such that for a typical nanodisk with a radius of 3.1 nm, the size of the energy gap is 3.88 eV. Besides, with this change, the maximum Seebeck coefficient increases from 1.54 to 2.03 mV/K. Furthermore, we can control the electron transmission and Seebeck coefficients with the help of the electric fields. The numerical results show that with the increase of the electric field, the transmission coefficient decreases and the Seebeck coefficient changes. The effect of a perpendicular electric field on the Seebeck coefficient is weaker than a transverse electric field. For an applied transverse electric field of 0.3 V/nm, the maximum Seebeck coefficient enhances to 2.09 mV/K.
Quasi-parametric rates for Sparse Multivariate Functional Principal Components Analysis

This work aims to give non-asymptotic results for estimating the ﬁrst principal component of a multivariate random process. We ﬁrst deﬁne the covariance function and the covariance operator in the multivariate case. We then deﬁne a projection operator. This operator can be seen as a reconstruction step from the raw data in the functional data analysis context. Next, we show that the eigenelements can be expressed as the solution to an optimization problem, and we introduce the LASSO variant of this optimization problem and the associated plugin estimator. Finally, we assess the estimator’s accuracy. We establish a minimax lower bound on the mean square reconstruction error of the eigenelement, which proves that the procedure has an optimal variance in the minimax sense.
Difformer: Empowering Diffusion Model on Embedding Space for Text Generation

Diffusion models have achieved state-of-the-art synthesis quality on visual and audio tasks, and recent works adapt them to textual data by diffusing on the embedding space. But the difference between the continuous data space and the embedding space raises challenges to the diffusion model, which have not been carefully explored. In this paper, we conduct systematic studies and analyze the challenges threefold. Firstly, the data distribution is learnable for embeddings, which may lead to the collapse of the loss function. Secondly, as the norm of embedding varies between popular and rare words, adding the same noise scale will lead to sub-optimal results. In addition, we ﬁnd that noises sampled from a standard Gaussian distribution may distract the diffusion process. To solve the above challenges, we propose Difformer, a denoising diffusion probabilistic model based on Transformer, which consists of three techniques including utilizing an anchor loss function, a layer normalization module for embeddings, and a norm factor to the Gaussian noise. All techniques are complementary to each other and critical to boosting the model performance together. Experiments are conducted on benchmark datasets over two seminal text generation tasks including machine translation and text summarization. The results show that Difformer significantly outperforms the embedding diffusion baselines, while achieving competitive results with strong autoregressive baselines.
A Conversation on ER = EPR

Ten years ago this week, the two authors had an email conversation about black holes, ER bridges, and EPR entanglement. This brief note contains a verbatim translation of these emails. While the ideas expressed in this email dialogue linking [1] and [2] are more mainstream now than they were back then, there are still many unresolved puzzles, some of which are discussed in this old correspondence.
Don’t Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments

A key missing capacity of current language models (LMs) is grounding to real-world environments. Most existing work for grounded language understanding uses LMs to directly generate plans that can be executed in the environment to achieve the desired effects. It thereby casts the burden of ensuring grammaticality, faithfulness, and controllability all on the LMs. We propose Pangu, a generic framework for grounded language understanding that capitalizes on the discriminative ability of LMs instead of their generative ability. Pangu consists of a symbolic agent and a neural LM working in a concerted fashion: The agent explores the environment to incrementally construct valid plans, and the LM evaluates the plausibility of the candidate plans to guide the search process. A case study on the challenging problem of knowledge base question answering (KBQA), which features a massive environment, demonstrates the remarkable effectiveness and flexibility of Pangu: A BERT-base LM is sufficient for setting a new record on standard KBQA datasets, and larger LMs further bring substantial gains.Pangu also enables, for the first time, effective few-shot in-context learning for KBQA with large LMs such as Codex.
A hierarchical approach for pulmonary nodules identification from CT images using YOLO v5s nodule detection and 3D neural network classifier

Purpose: Lung cancer is one of the leading causes of death worldwide. Early lung cancer detection would enable physicians to treat patients effectively and decrease mortality rates significantly. This work proposes a hierarchical framework for lung nodule detection and classification from computed tomography (CT) images. Method: In the first step, a pre-trained model (YOLO) was used to detect all suspicious nodules. The YOLO model was re-trained using 397 CT images to detect the entire nodule in CT images. To maxim-ize the sensitivity of the model, a confidence level (the probability threshold for object detection) of 0.3 was set for nodule detection in the first phase (ensuring the entire suspicious nodules are detected from the input CT images). The aim of the hierarchy model is to detect and classify the entire lung nodules (from CT images) with a low false-negative rate. Given the outcome of the first step, we proposed a 3D CNN classifier to analyze and classify the suspicious nodules detected by the YOLO model to achieve a nodule detection framework with a very low false-negative rate. This framework was evaluated using the LUNA 16 dataset, which consists of 888 CT images containing the location of 1186 nodules and 400000 non-nodules in the lung. Results:
Spectral Regularized Kernel Two-Sample Tests

Over the last decade, an approach that has gained a lot of popularity to tackle non-parametric testing problems on general (i.e., non-Euclidean) domains is based on the notion of reproducing kernel Hilbert space (RKHS) embedding of probability distributions. The main goal of our work is to understand the optimality of two-sample tests constructed based on this approach. First, we show that the popular MMD (maximum mean discrepancy) two-sample test is not optimal in terms of the separation boundary measured in Hellinger distance. Second, we propose a modification to the MMD test based on spectral regularization by taking into account the covariance information (which is not captured by the MMD test) and prove the proposed test to be minimax optimal with a smaller separation boundary than that achieved by the MMD test. Third, we propose an adaptive version of the above test which involves a data-driven strategy to choose the regularization parameter and show the adaptive test to be almost minimax optimal up to a logarithmic factor. Moreover, our results hold for the permutation variant of the test where the test threshold is chosen elegantly through the permutation of the samples. Through numerical experiments on synthetic and real-world data, we demonstrate the superior performance of the proposed test in comparison to the MMD test.
Explainable Fuzzer Evaluation

While the aim of fuzzer evaluation is to establish fuzzer performance in general, an evaluation is always conducted on a specific benchmark. In this paper, we investigate the degree to which the benchmarking result depends on the properties of the benchmark and propose a methodology to quantify the impact of benchmark properties on the benchmarking result in relation to the impact of the choice of fuzzer. We found that the measured performance and ranking of a fuzzer substantially depends on properties of the programs and the seed corpora used during evaluation. For instance, if the benchmark contained larger programs or seed corpora with a higher initial coverage, AFL's ranking would improve while LibFuzzer's ranking would worsen. We describe our methodology as explainable fuzzer evaluation because it explains why the specific evaluation setup yields the observed superiority or ranking of the fuzzers and how it might change for different benchmarks. We envision that our analysis can be used to assess the degree to which evaluation results are overfitted to the benchmark and to identify the specific conditions under which different fuzzers performs better than others.
Human-in-the-loop Evaluation for Early Misinformation Detection: A Case Study of COVID-19 Treatments

We present a human-in-the-loop evaluation framework for fact-checking novel misinformation claims and identifying social media messages that support them. Our approach extracts check-worthy claims, which are aggregated and ranked for review. Stance classifiers are then used to identify tweets supporting novel misinformation claims, which are further reviewed to determine whether they violate relevant policies. To demonstrate the feasibility of our approach, we develop a baseline system based on modern NLP methods for human-in-the-loop fact-checking in the domain of COVID-19 treatments. We make our data and detailed annotation guidelines available to support the evaluation of human-in-the-loop systems that identify novel misinformation directly from raw user-generated content.
Evaluation of diffusion lengths and surface recombination velocities from electron beam induced current scans

A new procedure is described for the determination of the minority‐carrier diffusion length and surface recombination velocity from electron beam induced current (EBIC) scans on a semiconductor containing a barrier perpendicular to the surface. The analysis relies on the evaluation of the first moment of two EBIC profiles at different beam energies. An application of the method to literature data is reported.
Gradient Descent-Type Methods: Background and Simple Unified Convergence Analysis

In this book chapter, we briefly describe the main components that constitute the gradient descent method and its accelerated and stochastic variants. We aim at explaining these components from a mathematical point of view, including theoretical and practical aspects, but at an elementary level. We will focus on basic variants of the gradient descent method and then extend our view to recent variants, especially variance-reduced stochastic gradient schemes (SGD). Our approach relies on revealing the structures presented inside the problem and the assumptions imposed on the objective function. Our convergence analysis unifies several known results and relies on a general, but elementary recursive expression. We have illustrated this analysis on several common schemes.
Words as Gatekeepers: Measuring Discipline-specific Terms and Meanings in Scholarly Publications

Scholarly text is often laden with jargon, or specialized language that can facilitate efficient in-group communication within fields but hinder understanding for out-groups. In this work, we develop and validate an interpretable approach for measuring scholarly jargon from text. Expanding the scope of prior work which focuses on word types, we use word sense induction to also identify words that are widespread but overloaded with different meanings across fields. We then estimate the prevalence of these discipline-specific words and senses across hundreds of subfields, and show that word senses provide a complementary, yet unique view of jargon alongside word types. We demonstrate the utility of our metrics for science of science and computational sociolinguistics by highlighting two key social implications. First, though most fields reduce their use of jargon when writing for general-purpose venues, and some fields (e.g., biological sciences) do so less than others. Second, the direction of correlation between jargon and citation rates varies among fields, but jargon is nearly always negatively correlated with interdisciplinary impact. Broadly, our findings suggest that though multidisciplinary venues intend to cater to more general audiences, some fields' writing norms may act as barriers rather than bridges, and thus impede the dispersion of scholarly ideas.