Miracle-dz/UniFAR-FTUs huggingface dataset:
Supervised-Learning-Aided Communication Framework for Massive MIMO Systems With Low-Resolution ADCs

This paper considers a massive multiple-input-multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs). In this system, inspired by supervised learning, we propose a novel communication framework that consists of channel training and data detection. The underlying idea of the proposed framework is to use the input-output relations of a nonlinear system, formed by a channel and a quantization at the ADCs, for data detection. Specifically, for the channel training, we develop implicit and explicit training methods that empirically learn the conditional probability mass functions (PMFs) of the nonlinear system. For the data detection, we propose three detection methods that map a received signal vector to one of the indexes of possible symbol vectors, according to the empirical conditional PMFs learned from the channel training. We also present a low-complexity version of the proposed framework that reduces a detection complexity by using a successive-interference-cancellation (SIC) approach. In this low-complexity version, a symbol vector is divided into two subvectors and then these two subvectors are successively detected using SIC. When employing the proposed framework with one-bit ADCs, we derive an analytical expression for the symbol-vector-error probability. One major observation is that the symbol-vector-error probability decreases exponentially with the inverse of the number of transmit antennas, the operating signal-to-noise ratio, and the minimum distance that can increase with the number of receive antennas. Simulations demonstrate the detection error reduction of the proposed framework compared to existing detection techniques.
Scalable Planning for Energy Storage in Energy and Reserve Markets

Energy storage can facilitate the integration of renewable energy resources by providing arbitrage and ancillary services. Jointly optimizing energy and ancillary services in a centralized electricity market reduces the system's operating cost and enhances the profitability of energy storage systems. However, achieving these objectives requires that storage be located and sized properly. We use a bilevel formulation to optimize the location and size of energy storage systems, which perform energy arbitrage and provide regulation services. Our model also ensures the profitability of investments in energy storage by enforcing a rate of return constraint. Computational tractability is achieved through the implementation of a primal decomposition and a subgradient-based cutting-plane method. We test the proposed approach on a 240-bus model of the Western Electricity Coordinating Council system and analyze the effects of different storage technologies, rate of return requirements, and regulation market policies on energy storage participation on the optimal storage investment decisions. We also demonstrate that the proposed approach outperforms exact methods in terms of solution quality and computational performance.
Convergence of Entropy-Regularized Natural Policy Gradient Methods with Linear Function Approximation

Natural policy gradient (NPG) methods with entropy regularization achieve impressive empirical success in reinforcement learning problems with large state-action spaces. However, their convergence properties and the impact of entropy regularization remain elusive in the function approximation regime. In this paper, we establish ﬁnite-time convergence analyses of entropy-regularized NPG with linear function approximation under softmax parameterization. In particular, we prove that entropy-regularized NPG with averaging satisﬁes the persistence of excitation condition, and achieves a fast convergence rate of ˜ O (1 /T ) up to a function approximation error in regularized Markov decision processes. This convergence result does not require any a priori assumptions on the policies. Furthermore, under mild regularity conditions on the concentrability coefﬁcient and basis vectors, we prove that entropy-regularized NPG exhibits linear convergence up to a function approximation error.
A causal framework for explaining the predictions of black-box sequence-to-sequence models : Supplementary Material

The bipartite version of the graph partitioning problem with edge uncertainty considered by Fan et al. (2012) has the following form. Assume we want to partition U and V into K subsets each, say {Ui} and {Vj}, with each Ui having cardinality in [cmin, c u max] and each Vj in [c v min, c v max]. Let xik be the binary indicator of ui ∈ Uk, and analogously for xjk and vj . In addition, we let yij be a binary variable which takes value 1 when ui, vj are in different corresponding subsets (i.e. ui ∈ Uk, vj ∈ Vk′ and k 6= k′). We can express the constraints of the problem as:
Deep Supervision with Shape Concepts for Occlusion-Aware 3D Object Parsing

In this supplementary material, Section 2 details the 3D annotation for CAD models and real images as well as our approach to compute the car yaw angle given a 3D skeleton. Next, in Section 3, we present the instance segmentation algorithm in detail for our experiments on PASCAL3D+. Finally, we provide more quantitative results on KITTI-3D in Section 4 and demonstrate more qualitative results on KITTI-3D, PASCAL VOC and IKEA datasets in Section 5.
ER@CEBAF: A test of 5-pass energy recovery at CEBAF

Jefferson Lab personnel have broad expertise in the design, commissioning, and operation of multiple energy recovery linacs (ERLs): the CEBAF Front-End Test (early 1990s) [1], CEBAF-ER (2003) [2, 3], the IR Free Electron Laser (FEL) Demo, the IR FEL Upgrade, and the UV FEL Driver (1997-2014). Continued development of this core competency has led to this collaborative proposal to explore the forefronts of ERL technology at high energy in a unique expansion of CEBAF capability to a 5-pass ERL with negligible switchover time and programmatic impact to the CEBAF physics program. Such a capability would enable world-class studies of open issues in high-energy ERL beam dynamics that are relevant to future facilities such as electron-ion colliders (EICs) [4, 5, 6, 7]. This proposal requests support from the CEBAF Program Advisory Committee to seek funding for hardware installation, and a prospective 12 days of beam time circa Fall 2018 for commissioning this high-energy multi-pass ERL experiment in CEBAF.
Cooperative Games in MultiAgent Systems

In both human and artificial societies some activities are only allowed to coalitions that satisfy certain criteria, e.g., to sufficiently large coalitions or coalitions which involve players of sufficient seniority. Simmons (1988) formalised this idea in the context of secret sharing schemes by defining the concept of a (disjunctive) hierarchical access structure. The mathematical concept which describe access structures of secret sharing schemes is that of a simple game. In this paper we aim to start a systematic study of hierarchical games, both disjunctive and conjunctive, and our results show that they deserve such a treatment. We prove the duality between disjunctive and conjunctive hierarchical games. We introduce a canonical representation theorem for both and characterise disjunctive hierarchical games as complete games with a unique shiftmaximal losing coalition. We give a short combinatorial proof of the Beimel-Tassa-Weinreb characterisation theorem of weighted disjunctive hierarchical games. By duality we get similar theorems for conjunctive hierarchical games.
Audio Segmentation Grapheme-to-Phoneme Text Phonemes Audio Synthesis Durations Duration Prediction

We present Deep Voice, a production-quality text-to-speech system constructed entirely from deep neural networks. Deep Voice lays the groundwork for truly end-to-end neural speech synthesis. The system comprises five major building blocks: a segmentation model for locating phoneme boundaries, a grapheme-tophoneme conversion model, a phoneme duration prediction model, a fundamental frequency prediction model, and an audio synthesis model. For the segmentation model, we propose a novel way of performing phoneme boundary detection with deep neural networks using connectionist temporal classification (CTC) loss. For the audio synthesis model, we implement a variant of WaveNet that requires fewer parameters and trains faster than the original. By using a neural network for each component, our system is simpler and more flexible than traditional text-tospeech systems, where each component requires laborious feature engineering and extensive domain expertise. Finally, we show that inference with our system can be performed faster than real time and describe optimized WaveNet inference kernels on both CPU and GPU that achieve up to 400x speedups over existing implementations. †Authors are listed alphabetically by last name. Submitted February 24, 2017 for review for the International Conference on Machine Learning (ICML) 2017. Copyright 2017 by the author(s).
Exploring the antimicrobial potential of biologically synthesized zero valent iron nanoparticles

Abstract The widespread use of antibiotics has resulted in the emergence of multidrug-resistant bacteria. Therefore, it is essential to explore alternative strategies to effectively combat medically significant resistant pathogens. In recent years, nanoparticles (NPs) have emerged as a promising alternative source of antimicrobial agents. While nanoscale particles were traditionally synthesized using chemical techniques, the development of metallic NPs using biological methods has garnered attention. This current study focuses on the synthesis of iron NPs (Fe NPs) using metal-tolerant fungal strains, as numerous microorganisms serve as environmentally safe and durable precursors to produce persistent and bi-functional NPs. The study involved the isolation and evaluation of ten fungal strains that are resistant to heavy metals to determine their ability to produce Fe NPs. The biologically synthesized Fe NPs were characterized using X-ray diffraction (XRD), energy dispersive X-ray spectroscopy (EDX), and scanning electron microscopy techniques. The XRD results indicated the presence of Fe in nanopowder form, displaying a series of reflection angles (2θ) at 65° and 75° indicating the existence of cubic planes. EDX analysis revealed the presence of ferrous and ferric elements, along with zero-valent Fe NPs. Micrographs of the surface topology displayed spherical aggregation of the synthesized NPs. Furthermore, the Fe NPs exhibited promising antibacterial potential against selected bacterial strains, including Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, Cronobacter sakazakii, Listeria innocua, and Enterococcus fecalis. This study demonstrates that the biological synthesis of metallic NPs is environmentally safe, and Fe NPs produced through mycological means could be utilized to combat antibiotic-resistant pathogenic strains.
Commonsense Knowledge Graph towards Super APP and Its Applications in Alipay

The recently explosive growth of Super Apps brings great convenience to people's daily life by providing a wide variety of services through mini-programs, including online shopping, travel, finance, and so on. Due to the considerable gap between various scenarios, the restriction of effective information transfer and sharing severely blocks the efficient delivery of online services, potentially affecting the user's app experience. To deeply understand users' needs, we propose SupKG, a commonsense knowledge graph towards Super APP to help comprehensively characterize user behaviors across different business scenarios. In particular, our SupKG is carefully established from multiplex and heterogeneous data source in Alipay (a well-known Super App in China), which also emphasize abundant spatiotemporal relations and intent-related entities to answer the fundamental question in life service ''which service do users need at what time and where''. On the hand, the successful application of SupKG hinges on the effective form of network representation ie Knowledge Graph Embedding (KGE). However, a series of unsatisfying issues still need to be carefully considered in the industrial environment: i) bridging language representations with knowledge structure in a unified manner, ii) alleviating the skewed data distribution in SupKG, and iii) effectively characterizing hierarchical structures in SupKG. With these motivations, we develop a novel knowledge graph representation learning framework for SupKG, enabling various downstream applications to benefit from learned representations of entities and relations. Extensive experiments on the standard knowledge graph completion task demonstrate the consistent and significant performance improvement of our representation learning framework, which also greatly benefits the supplementation of potential knowledge of SupKG. Towards real-world applications in Alipay, our SupKG and learned representations show the potential superiority of integrating global behaviors in cold-start scenarios and providing high-quality knowledge for warming up the graph-based ranking.
Investigando a produção de mésons vetoriais e fótons diretos em colisões envolvendo íons pesados no Large Hadron Collider

The exclusive production of vector mesons as well the prompt photon production have been studied recently, both experimentally and theoretically. In this work, we investigated the exclusive photoproduction of vector mesons and the real photon radiation processes in proton-proton (pp), proton-nucleus (pA) and nucleus-nucleus (AA) collisions at the Large Hadron Collider (LHC) energies, considering the color dipole approach. The masses of heavy mesons provide a perturbative scale for the problem allowing to test the perturbative Quantum Chromodynamics (QCD). On the other hand, the light mesons do not have a perturbative scale associated to the process, so they allow to test the non-perturbative regime of QCD. The transition between the hard perturbative and the soft regimes can be addressed by the parton saturation models within the color dipole formalism. In addition, we investigated the direct photon production, where we estimate the corresponding cross section in pp and pA collisions at the LHC. The prompt photon are an important probe of QCD dynamics, scanning the small distances region and, consequently, providing access to the hadronic structure of the proton. For both reactions treated in this thesis, the main objective is to analyze the theoretical uncertainties related to the phenomenological models in the description of observables in hadronics collisions at high energy regime. Lista de artigos relacionados • G. Sampaio dos Santos, M. V. T. Machado, Exclusive photoproduction of quarkonium in proton-nucleus collisions at energies available at the CERN Large Hadron Collider, Phys. Rev. C 89, 025201 (2014). • G. Sampaio dos Santos, M. V. T. Machado, Light vector meson photoproduction in hadron-hadron and nucleus-nucleus collisions at energies available at the CERN Large Hadron Collider, Phys. Rev. C 91, 025203 (2015). • G. Sampaio dos Santos, M. V. T. Machado, On theoretical uncertainty of color dipole phenomenology in the J/ψ and Υ photoproduction in pA and AA collisions at the CERN Large Hadron Collider, J. Phys. G 42, 105001 (2015). • G. Sampaio dos Santos, M. V. T. Machado, Exclusive photoproduction of light vector meson in coherent collisions at the LHC energies, J. Phys. Conf. Ser. 706, 052017 (2016). • (Artigo não relacionado aos temas da Tese) G. Sampaio dos Santos, M. V. T. Machado, Diffractive dissociation in proton-nucleus collisions at collider energies, Eur. Phys. J. A 50, 166 (2014).
Learning Multivariate Hawkes Process via Graph Recurrent Neural Network

This paper presents a novel approach for modeling and predicting patterns of events in time-series learning, named graph recurrent temporal point process (GRTPP). Prior research has focused on using deep learning techniques, such as recurrent neural networks (RNNs) or attention-based sequential data embedding, on modeling the time-varying intensity of events. However, these models were typically limited to modeling a single intensity function capturing the event occurrence of all event types simultaneously. GRTPP addresses this issue by encoding multivariate event sequences into a sequence of graphs, where each node contains information about the event occurrence and time. The sequence of graphs is then embedded into node embeddings for each event type, taking into account the relationships between the event types. By integrating the estimated intensity functions, GRTPP predicts the event type and the timing of the next event. The proposed GRTPP model offers improved effectiveness and explainability compared to previous models, as demonstrated through empirical evaluations on five real-world datasets and the actual credit card transaction dataset. The code is available at https://github.com/im0j/GRTPP https://github.com/im0j/GRTPP.
Personalized Federated Learning with Parameter Propagation

With decentralized data collected from diverse clients, a personalized federated learning paradigm has been proposed for training machine learning models without exchanging raw data from local clients. We dive into personalized federated learning from the perspective of privacy-preserving transfer learning, and identify the limitations of previous personalized federated learning algorithms. First, previous works suffer from negative knowledge transferability for some clients, when focusing more on the overall performance of all clients. Second, high communication costs are required to explicitly learn statistical task relatedness among clients. Third, it is computationally expensive to generalize the learned knowledge from experienced clients to new clients. To solve these problems, in this paper, we propose a novel federated parameter propagation (FEDORA) framework for personalized federated learning. Specifically, we reformulate the standard personalized federated learning as a privacy-preserving transfer learning problem, with the goal of improving the generalization performance for every client. The crucial idea behind FEDORA is to learn how to transfer and whether to transfer simultaneously, including (1) adaptive parameter propagation: one client is enforced to adaptively propagate its parameters to others based on their task relatedness (e.g., explicitly measured by distribution similarity), and (2) selective regularization: each client would regularize its local personalized model with received parameters, only when those parameters are positively correlated with the generalization performance of its local model. The experiments on a variety of federated learning benchmarks demonstrate the effectiveness of the proposed FEDORA framework over state-of-the-art personalized federated learning baselines.
Contrastive Learning of Stress-specific Word Embedding for Social Media based Stress Detection

Detecting stress via user's social media posts has attracted increasing research interests in recent years. The majority of the methods leverage word embeddings to represent each of the posted words as a vector, and then perform classification on a sequence of word vectors. To enhance the performance of distinguishing words/phrases related to stressors and stressful emotions from others, in this study, we present a stress-specific word embedding learning framework upon the pre-trained language model BERT. Specifically, we formulate three self-supervised contrastive learning tasks with a joint learning objective. (1) The stressor discrimination task, which is designed to allow the framework to be sensitive to words/phrases about stressors. (2) The stressor cluster discrimination task, which is designed to allow the framework to distinguish stressors into different categories. (3) The stressful emotion discrimination task, which is designed to allow the framework to grasp words/phrases about stressful emotions. Our performance study shows that the learned stress-specific word embedding can significantly benefit social media based stress detection tasks, especially in the more practical scenarios with insufficient labeled data. Besides, we build two user-level social media based stress detection datasets that can help train machine learning models to facilitate human well-being.
JMLKelinci+: Detecting Semantic Bugs and Covering Branches with Valid Inputs using Coverage-Guided Fuzzing and Runtime Assertion Checking

Testing to detect semantic bugs is essential, especially for critical systems. Coverage-guided fuzzing (CGF) and runtime assertion checking (RAC) are two well-known approaches for detecting semantic bugs. CGF aims to generate test inputs with high code coverage. However, while CGF tools can be equipped with sanitizers to detect a fixed set of semantic bugs, they can otherwise only detect bugs that lead to a crash. Thus, the first problem we address is how to help fuzzers detect previously unknown semantic bugs that do not lead to a crash. Moreover, a CGF tool may not necessarily cover all branches with valid inputs, although invalid inputs are useless for detecting semantic bugs. So, the second problem is how to guide a fuzzer to maximize coverage using only valid inputs. On the other hand, RAC monitors the expected behavior of a program dynamically and can only detect a semantic bug when a valid test input shows that the program does not satisfy its specification. Thus, the third problem is how to provide high-quality test inputs for a RAC that can trigger potential bugs. The combination of a CGF tool and RAC solves these problems and can cover branches with valid inputs and detect semantic bugs effectively. Our study uses RAC to guarantee that only valid inputs reach the program under test using the program’s specified preconditions and it also uses RAC to detect semantic bugs using specified postconditions. A prototype tool was developed for this study, named JMLKelinci+. Our results show that combining a CGF tool with RAC will lead to executing the program under test only with valid inputs and that this technique can effectively detect semantic bugs. Also, this idea improves the feedback given to a CGF tool, enabling it to cover all branches faster in programs with non-trivial preconditions.
Numerical Techniques in Electromagnetics

Numerical Techniques in Electromagnetics is designed to show the reader how to pose, numerically analyze, and solve electromagnetic (EM) problems. It gives them the ability to expand their problem-solving skills using a variety of available numerical methods. Topics covered include fundamental concepts in EM; numerical methods; finite difference methods; variational methods, including moment methods and finite element methods; transmission-line matrix or modeling (TLM); and Monte Carlo methods. The simplicity of presentation of topics throughout the book makes this an ideal text for teaching or self-study by senior undergraduates, graduate students, and practicing engineers.
Comparison of attitude and heading reference systems using foot mounted MIMU sensor data: basic, Madgwick, and Mahony

A magnetic and inertial measurement unit (MIMU) usually measures acceleration, rotation rate, and earth's magnetic field in order to determine a body's attitude. In order to find the orientation information using all sensors a fusion algorithm is used. This paper compares two approaches used for a Attitude and Heading Reference System (AHRS), namely Madgwick and Mahony with a basic fusion approach. Foot mounted MIMU data is used to estimate the Euler angles as well as the position. The results show that Madgwick obtains better heading orientation than Mahony and the basic AHRS approach in terms of the error (RMSE) of the Euler angles when compared to the ground truth. However, the execution time of Mahony is less than Madgwick with the basic AHRS taking the longest.