Miracle-dz/UniFAR-FTUs huggingface dataset:
PAQMAN: A Principled Approach to Active Queue Management

—Active Queue Management (AQM) aims to prevent bufferbloat and serial drops in router and switch FIFO packet buffers that usually employ drop-tail queueing. AQM describes methods to send proactive feedback to TCP ﬂow sources to regulate their rate using selective packet drops or markings. Traditionally, AQM policies relied on heuristics to approximately provide Quality of Service (QoS) such as a target delay for a given ﬂow. These heuristics are usually based on simple network and TCP control models together with the monitored buffer ﬁlling. A primary drawback of these heuristics is that their way of accounting ﬂow characteristics into the feedback mechanism and the corresponding effect on the state of congestion are not well understood. In this work, we show that taking a probabilistic model for the ﬂow rates and the dequeueing pattern, a Semi-Markov Decision Process (SMDP) can be formulated to obtain an optimal packet dropping policy. This policy-based AQM, denoted PAQMAN , takes into account a steady-state model of TCP and a target delay for the ﬂows. Additionally, we present an inference algorithm that builds on TCP congestion control in order to calibrate the model parameters governing underlying network conditions. Finally, we evaluate the performance of our approach using simulation compared to state-of-the-art AQM algorithms.
On the $\mu$ equals zero conjecture for fine Selmer groups in Iwasawa theory

We study the Iwasawa theory of the fine Selmer group associated to certain Galois representations. The vanishing of the $\mu$-invariant is shown to follow in some cases from a natural property satisfied by Galois deformation rings. We outline conditions under which the $\mu=0$ conjecture is shown to hold for various Galois representations of interest.
Maximally Chaotic Dynamical Systems of Anosov-Kolmogorov and Fundamental Interactions

We give a general review on the application of Ergodic theory to the investigation of dynamics of the Yang-Mills gauge fields and of the gravitational systems, as well as its application in the Monte Carlo method and fluid dynamics. In ergodic theory the maximally chaotic dynamical systems (MCDS) can be defined as dynamical systems that have nonzero Kolmogorov entropy. The hyperbolic dynamical systems that fulfil the Anosov C-condition belong to the MCDS insofar as they have exponential instability of their phase trajectories and positive Kolmogorov entropy. It follows that the C-condition defines a rich class of MCDS that span over an open set in the space of all dynamical systems. The large class of Anosov-Kolmogorov MCDS is realised on Riemannian manifolds of negative sectional curvatures and on high-dimensional tori. The interest in MCDS is rooted in the attempts to understand the relaxation phenomena, the foundations of the statistical mechanics, the appearance of turbulence in fluid dynamics, the non-linear dynamics of Yang-Mills field and gravitating N-body systems as well as black hole thermodynamics. Our aim is to investigate classicaland quantum-mechanical properties of MCDS and their role in the theory of fundamental interactions. 1Based on lectures at the International Bogolyubov Conference ”Problems of Theoretical and Mathematical Physics” at the Steklov Mathematical Institute, Moscow-Dubna, September 9-13, 2019 and seminars at the Niels Bohr Institute, at the CERN Theory Department and A. Alikhanian National Laboratory in Yerevan. http://www.mathnet. ru/php/presentation.phtml? option_lang=eng&presentid=24992 ar X iv :2 20 2. 09 84 6v 1 [ he pth ] 2 0 Fe b 20 22
STAR-RIS-NOMA Networks: An Error Performance Perspective

This letter investigates the bit error rate (BER) performance of simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) in non-orthogonal multiple access (NOMA) networks. In the investigated network, a STAR-RIS serves multiple non-orthogonal users located on either side of the surface by utilizing the mode switching protocol. We derive the closed-form BER expressions in perfect and imperfect successive interference cancellation cases. Furthermore, asymptotic analyses are also conducted to provide further insights into the BER behavior in the high signal-to-noise ratio region. Finally, the accuracy of our theoretical analysis is validated through Monte Carlo simulations. The obtained results reveal that the BER performance of STAR-RIS-NOMA outperforms that of the classical NOMA system, and STAR-RIS might be a promising NOMA 2.0 solution.
The effects of a new technology on the design, organization, and architectures of computing systems

This dissertation will bring to life a roadmap for studying the effects of a new technology on the design, organization, and architecture of digital computer systems. Until only recently, most research conducted with any nano-scale device has been limited to the realm of device physics, or at best, the simplest circuits. But now, with some specific nano-scale devices showing significant promise and progress, some researchers are beginning to study what computational systems of such devices might look like. My work takes this idea one step further. Its focus is to use research in architecting and designing complex systems of nano-scale devices to actually enhance device development and accelerate potential device implementations. This research was done in the context of the quantum-dot cellular automata (QCA). It considers the design of a completely QCA microprocessor which formed a foundation of circuit design techniques, candidate architectures, and logical design rules for the technology. This foundation has had a positive impact on the most promising implementation for QCA (molecular cells) in the context of FPGAs. It also explores and defines a set of Mead/Conway-esq physical design rules that should promote further interdisciplinary work between device physicists and computer system designers and how these methodologies should be beneficial to other emergent devices.
Phase-field fracture irreversibility using the slack variable approach

In this manuscript, the phase-field fracture irreversibility constraint is transformed into an equality-based constraint using the slack variable approach. The equality-based fracture irreversibility constraint is then introduced in the phase-field fracture energy functional using the Lagrange Multiplier Method and the Penalty method. Both methods are variationally consistent with the conventional variational inequality phase-field fracture problem, unlike the history-variable approach. Thereafter, numerical experiments are carried out on benchmark problems in brittle and quasi-brittle fracture to demonstrate the efficacy of the proposed method.
Gradient Tracking: A Unified Approach to Smooth Distributed Optimization

In this work, we study the classical distributed optimization problem over digraphs, where the objective function is a sum of smooth local functions. Inspired by the implicit tracking mechanism proposed in our earlier work, we develop a unified algorithmic framework from a pure primal perspective, i.e., UGT, which is essentially a generalized gradient tracking method and can unify most existing distributed optimization algorithms with constant step-sizes. It is proved that two variants of UGT can both achieve linear convergence if the global objective function is strongly convex. Finally, the performance of UGT is evaluated by numerical experiments.
Global well-posedness for three-dimensional compressible viscous micropolar and heat-conducting fluids with vacuum at infinity and large oscillations

We investigate global well-posedness to the Cauchy problem of three-dimensional compressible viscous and heat-conducting micropolar fluid equations with zero density at infinity. By delicate energy estimates, we establish global existence and uniqueness of strong solutions under some smallness condition depending only on the parameters appeared in the system and the initial mass. In particular, the initial mass can be arbitrarily large. This improves our previous work [23]. Moreover, we also generalize the result [13] to the case that vacuum is allowed at infinity.
End-to-End High Accuracy License Plate Recognition Based on Depthwise Separable Convolution Networks

Automatic license plate recognition plays a crucial role in modern transportation systems such as for traffic monitoring and vehicle violation detection. In real-world scenarios, license plate recognition still faces many challenges and is impaired by unpredictable interference such as weather or lighting conditions. Many machine learning based ALPR solutions have been proposed to solve such challenges in recent years. However, most are not convincing, either because their results are evaluated on small or simple datasets that lack diverse surroundings, or because they require powerful hardware to achieve a reasonable frames-per-second in real-world applications. In this paper, we propose a novel segmentation-free framework for license plate recognition and introduce NP-ALPR, a diverse and challenging dataset which resembles real-world scenarios. The proposed network model consists of the latest deep learning methods and state-of-the-art ideas, and benefits from a novel network architecture. It achieves higher accuracy with lower computational requirements than previous works. We evaluate the effectiveness of the proposed method on three different datasets and show a recognition accuracy of over 99% and over 70 fps, demonstrating that our method is not only robust but also computationally efficient.
MACRONYM: A Large-Scale Dataset for Multilingual and Multi-Domain Acronym Extraction

Acronym extraction is the task of identifying acronyms and their expanded forms in texts that is necessary for various NLP applications. Despite major progress for this task in recent years, one limitation of existing AE research is that they are limited to the English language and certain domains (i.e., scientific and biomedical). Challenges of AE in other languages and domains are mainly unexplored. As such, lacking annotated datasets in multiple languages and domains has been a major issue to prevent research in this direction. To address this limitation, we propose a new dataset for multilingual and multi-domain AE. Specifically, 27,200 sentences in 6 different languages and 2 new domains, i.e., legal and scientific, are manually annotated for AE. Our experiments on the dataset show that AE in different languages and learning settings has unique challenges, emphasizing the necessity of further research on multilingual and multi-domain AE.
Synthetic Disinformation Attacks on Automated Fact Verification Systems

Automated fact-checking is a needed technology to curtail the spread of online misinformation. One current framework for such solutions proposes to verify claims by retrieving supporting or refuting evidence from related textual sources. However, the realistic use cases for fact-checkers will require verifying claims against evidence sources that could be affected by the same misinformation. Furthermore, the development of modern NLP tools that can produce coherent, fabricated content would allow malicious actors to systematically generate adversarial disinformation for fact-checkers. In this work, we explore the sensitivity of automated fact-checkers to synthetic adversarial evidence in two simulated settings: ADVERSARIAL ADDITION, where we fabricate documents and add them to the evidence repository available to the fact-checking system, and ADVERSARIAL MODIFICATION, where existing evidence source documents in the repository are automatically altered. Our study across multiple models on three benchmarks demonstrates that these systems suffer significant performance drops against these attacks. Finally, we discuss the growing threat of modern NLG systems as generators of disinformation in the context of the challenges they pose to automated fact-checkers.
Global-to-Local Modeling and Experiment Investigation of HFCBGA Package Board-Level Solder Joint Reliability

Due to the high speed and high I/O count requirements for semiconductor packages, thousands of soldered interconnections are indispensable, and this situation renders traditional finite element method (FEM) analysis a formidable challenge. This paper presents a 3D-equivalent global model and local submodeling technique to investigate board-level solder joint reliability under cyclic temperature loading. The equivalent global model is capable of addressing critical solder failure locations. An individual local solder ball is used to predict the number of cycles to failure. The high performance flip-chip ball grid array (HFCBGA) package case was studied with the provided experimental data. According to FEM results, the predicted solder ball life is close to that observed experimentally. Therefore, the global-to-local modeling technique can be concluded to provide an efficient methodology for evaluating very high pin count HFCBGA package reliability.
Unified approach for computing sum of sources over CQ-MAC

We consider the task of communicating a generic bivariate function of two classical sources over a Classical-Quantum Multiple Access Channel (CQ-MAC). The two sources are observed at the encoders of the CQ-MAC, and the decoder aims at reconstructing a bivariate function from the received quantum state. Inspired by the techniques developed for the classical setting, and employing the technique of simultaneous (joint) decoding developed for the CQ setup, we propose and analyze a coding scheme based on a classical superposition of algebraic structured codes and unstructured codes, and the idea of embedding functions on a prime field. We derive a new set of sufficient conditions that strictly enlarge the largest known set of sources (capable of communicating the bivariate function) for any given CQ-MAC. We provide these conditions in terms of single-letter quantum information-theoretic quantities.
Analysis of photoinjector transverse phase space in action and phase coordinates

Photoinjectors are the main high brightness electron sources for X-ray free electron lasers (XFEL). Photoinjector emittance reduction is one of the key knobs for improving XFEL lasing, so precise emittance measurement is critical. It's well known that rms emittance is very sensitive to low intensity tails of particle distributions in the phase space, whose measurement depend on the signal to noise ratio (SNR) and image processing procedures. Such sensitivities make the interpretations of beam transverse brightness challenging, leading to different emittance definitions to reduce the impact of tail particles. In this paper, transverse phase space is analyzed in action and phase coordinates for both analytical models and experiments, which give a more intuitive way to calculate the beam core brightness.
Communication-Efficient Actor-Critic Methods for Homogeneous Markov Games

Recent success in cooperative multi-agent reinforcement learning (MARL) relies on centralized training and policy sharing. Centralized training eliminates the issue of non-stationarity MARL yet induces large communication costs, and policy sharing is empirically crucial to efficient learning in certain tasks yet lacks theoretical justification. In this paper, we formally characterize a subclass of cooperative Markov games where agents exhibit a certain form of homogeneity such that policy sharing provably incurs no suboptimality. This enables us to develop the first consensus-based decentralized actor-critic method where the consensus update is applied to both the actors and the critics while ensuring convergence. We also develop practical algorithms based on our decentralized actor-critic method to reduce the communication cost during training, while still yielding policies comparable with centralized training.
Collusion Resistant Federated Learning with Oblivious Distributed Differential Privacy

Federated learning enables a population of distributed clients to jointly train a shared machine learning model with the assistance of a central server. The finance community has shown interest in its potential to allow inter-firm and cross-silo collaborative models for problems of common interest (e.g. fraud detection), even when customer data use is heavily regulated. Prior works on federated learning have employed cryptographic techniques to keep individual client model parameters private even when the central server is not trusted. However, there is an important gap in the literature: efficient protection against attacks in which other parties collude to expose an honest client’s model parameters, and therefore potentially protected customer data. We aim to close this collusion gap by presenting an efficient mechanism based on oblivious distributed differential privacy that is the first to protect against such client collusion, including the “Sybil” attack in which a server generates or selects compromised client devices to gain additional information. We leverage this novel privacy mechanism to construct an improved secure federated learning protocol and prove the security of that protocol. We conclude with empirical analysis of the protocol’s execution speed, learning accuracy, and privacy performance on two data sets within a realistic simulation of 5,000 distributed network clients.
Utilizing Rayon Fiber Residues from Fiber Manufacturing Industry for Preparation of Cellulose/CMC Hydrogels

The cellulose-based hydrogel was successfully prepared from rayon fiber residue obtained from the fiber manufacturing industry. By chemical means, the hydrogel was simply prepared at an ambient temperature by mixing rayon with carboxymethyl cellulose (CMC) in NaOH/urea solution with epichlorohydrin (ECH) as a crosslinking agent. Rayon cellulose was used for stabilizing of hydrogel structure, providing a dimensional stability to the hydrogel whereas CMC acted as a porogen, widening the pore size within the hydrogel structure while swelling in water. With increasing CMC content, the percent water uptake of the hydrogel was increased but the structural stability was impaired. The prepared rayon cellulose/CMC hydrogel could take up more than 200% water within 60 minutes with an appropriate rayon cellulose-to-CMC ratio of 1:1 providing an ultimate balance between percent water uptake and the structural stability of the hydrogel. Its percent water uptake was as high as 285% to its initial dry weight.
Rotating Spirals in segregated reaction-diffusion systems

We give a complete characterization of the boundary traces φi (i = 1, . . . , K) supporting spiraling waves, rotating with a given angular speed ω, which appear as singular limits of competition-diffusion systems of the type  ∂tui − ∆ui = μui − βui ∑j 6=i aijuj in Ω×R+ ui = φi on ∂Ω×R+ ui(x, 0) = ui,0(x) for x ∈ Ω as β → +∞. Here Ω is a rotationally invariant planar set and aij > 0 for every i and j. We tackle also the homogeneous Dirichlet and Neumann boundary conditions, as well as entire solutions in the plane. As a byproduct of our analysis we detect explicit families of eternal, entire solutions of the pure heat equation, parameterized by ω ∈ R, which reduce to homogeneous harmonic polynomials for ω = 0.
A Less Uncertain Sampling-Based Method of Batch Bayesian Optimization

This paper presents a method called sampling-computation-optimization (SCO) to design batch Bayesian optimization. SCO does not construct new high-dimensional acquisition functions but samples from the existing one-site acquisition function to obtain several candidate samples. To reduce the uncertainty of the sampling, the general discrepancy is computed to compare these samples. Finally, the genetic algorithm and switch algorithm are used to optimize the design. Several strategies are used to reduce the computational burden in the SCO. From the numerical results, the SCO designs were less uncertain than those of other sampling-based methods. As for application in batch Bayesian optimization, SCO can find a better solution when compared with other batch methods in the same dimension and batch size. In addition, it is also flexible and can be adapted to different one-site methods. Finally, a complex experimental case is given to illustrate the application value and scenario of SCO method.
Integrated Fault Diagnosis and Control Design for DER Inverters using Machine Learning Methods

This paper employs a supervised machine learning (ML) algorithm to propose an integrated fault detection and diagnosis (FDD) and fault-tolerant control (FTC) strategy to detect, diagnose, and classify the grid faults and correct the input voltage before affecting the grid-connected distributed energy resources (DER) inverters. This controller can mitigate the impact of grid faults on inverters by predicting and modifying the time series of their input voltage. Simulation results show the effectiveness of the proposed controller and evaluate its operating performance.
Automated Reasoning in Non-classical Logics in the TPTP World

Non-classical logics are used in a wide spectrum of disciplines, including artificial intelligence, computer science, mathematics, and philosophy. The de-facto standard infrastructure for automated theorem proving, the TPTP World, currently supports only classical logics. Similar standards for non-classical logic reasoning do not exist (yet). This hampers practical development of reasoning systems, and limits their interoperability and application. This paper describes the latest extension of the TPTP World, which provides languages and infrastructure for reasoning in non-classical logics. The extensions integrate seamlessly with the existing TPTP World.
MixKG: Mixing for harder negative samples in knowledge graph

Knowledge graph embedding~(KGE) aims to represent entities and relations into low-dimensional vectors for many real-world applications. The representations of entities and relations are learned via contrasting the positive and negative triplets. Thus, high-quality negative samples are extremely important in KGE. However, the present KGE models either rely on simple negative sampling methods, which makes it difficult to obtain informative negative triplets; or employ complex adversarial methods, which requires more training data and strategies. In addition, these methods can only construct negative triplets using the existing entities, which limits the potential to explore harder negative triplets. To address these issues, we adopt mixing operation in generating harder negative samples for knowledge graphs and introduce an inexpensive but effective method called MixKG. Technically, MixKG first proposes two kinds of criteria to filter hard negative triplets among the sampled negatives: based on scoring function and based on correct entity similarity. Then, MixKG synthesizes harder negative samples via the convex combinations of the paired selected hard negatives. Experiments on two public datasets and four classical KGE methods show MixKG is superior to previous negative sampling algorithms.
Dynamic and Efficient Gray-Box Hyperparameter Optimization for Deep Learning

Gray-box hyperparameter optimization techniques have recently emerged as a promising direction for tuning Deep Learning methods. In this work, we introduce DyHPO, a method that learns to dynamically decide which configuration to try next, and for what budget. Our technique is a modification to the classical Bayesian optimization for a gray-box setup. Con-cretely, we propose a new surrogate for Gaussian Processes that embeds the learning curve dynamics and a new acquisition function that incorporates multi-budget information. We demonstrate the significant superiority of DyHPO against state-of-the-art hyperparameter optimization baselines through large-scale experiments comprising 50 datasets (Tabular, Image, NLP) and diverse neural networks (MLP, CNN/NAS, RNN).