Miracle-dz/UniFAR-FTUs huggingface dataset:
E2C: A Visual Simulator for Heterogeneous Computing Systems

Heterogeneity has been an indispensable aspect of distributed computing throughout the history of these systems. In particular, with the increasing prevalence of accelerator technologies (e.g., GPUs and TPUs) and the emergence of domain-specific computing via ASICs and FPGA, the matter of heterogeneity and harnessing it has become a more critical challenge than ever before. Harnessing system heterogeneity has been a longstanding challenge in distributed systems and has been investigated extensively in the past. Making use of real infrastructure (such as those offered by the public cloud providers) for benchmarking the performance of heterogeneous machines, for different applications, with respect to different objectives, and under various workload intensities is cost- and time-prohibitive. To mitigate this burden, we develop an open-source simulation tool, called E2C, that can help researchers and practitioners study any type of heterogeneous computing system and measure its performance under various system configurations. E2C has an intuitive graphical user interface (GUI) that enables its users to easily examine system-level solutions (scheduling, load balancing, scalability, etc.) in a controlled environment within a short time and at no cost. In particular, E2C offers the following features: (i) simulating a heterogeneous computing system; (ii) implementing a newly developed scheduling method and plugging it into the system, (iii) measuring energy consumption and other output-related metrics; and (iv) powerful visual aspects to ease the learning curve for students. Potential users of E2C can be undergraduate and graduate students in computer science/engineering, researchers, and practitioners.
Perfect State Transfer in Arbitrary Distance

Quantum Perfect State Transfer (PST) is a fundamental tool of quantum communication in a network. It is considered a rare phenomenon. The original idea of PST depends on the fundamentals of the continuous-time quantum walk. A path graph with at most three vertices allows PST. Based on the Markovian quantum walk, we introduce a signiﬁcantly powerful method for PST in this article. We establish PST between the extreme vertices of a path graph of arbitrary length. Moreover, any pair of vertices j and n − j − 1 in a path graph with n vertices allow PST for 0 ≤ j < n − 1 2 . Also, no cycle graph with more than 4 vertices does not allow PST based on the continuous-time quantum walk. In contrast, we establish PSTs based on Markovian quantum walk between the pair of vertices j and j + m for j = 0 , 1 , . ( m − 1) in a cycle graph with 2 m vertices.
Pauli transfer matrix direct reconstruction: channel characterization without full process tomography

We present a tomographic protocol for the characterization of multiqubit quantum channels. We discuss a specific class of input states, for which the set of Pauli measurements at the output of the channel directly relates to its Pauli transfer matrix components. We compare our results to those of standard quantum process tomography, showing an exponential reduction in the number of different experimental configurations required by a single matrix element extraction, while keeping the same number of shots. This paves the way for more efficient experimental implementations, whenever a selective knowledge of the Pauli transfer matrix is needed. We provide several examples and simulations.
Microscopic calculation of fission product yields for odd-mass nuclei

Fission data are essential inputs to reaction networks involved in nucleosynthesis simulations and nuclear forensics. In such applications as well as in the description of multi-chance fission, the characteristics of fission for odd-mass nuclei are just as important as those for even-even nuclei. The fission theories that aim at explicitly describing fission dynamics are typically based on some variant of the nuclear mean-field theory. In such cases, the treatment of systems with an odd number of particles is markedly more involved, both formally and computationally. In this article, we use the blocking prescription of the Hartree-Fock-Bogoliubov theory with Skyrme energy functionals to compute the deformation properties of odd-mass uranium isotopes. We show that the resulting fission fragment distributions depend quite significantly on the spin of the odd neutron. By direct calculation of the spin distribution of the fissioning nucleus, we propose a methodology to rigorously predict the charge and mass distributions in odd-mass nuclei.
Word Embedding Neural Networks to Advance Knee Osteoarthritis Research

Osteoarthritis (OA) is the most prevalent chronic joint disease worldwide, where knee OA takes more than 80% of commonly affected joints. Knee OA is not a curable disease yet, and it affects large columns of patients, making it costly to patients and healthcare systems. Etiology, diagnosis, and treatment of knee OA might be argued by variability in its clinical and physical manifestations. Although knee OA carries a list of well-known terminology aiming to standardize the nomenclature of the diagnosis, prognosis, treatment, and clinical outcomes of the chronic joint disease, in practice there is a wide range of terminology associated with knee OA across different data sources, including but not limited to biomedical literature, clinical notes, healthcare literacy, and health-related social media. Among these data sources, the scientific articles published in the biomedical literature usually make a principled pipeline to study disease. Rapid yet, accurate text mining on large-scale scientific literature may discover novel knowledge and terminology to better understand knee OA and to improve the quality of knee OA diagnosis, prevention, and treatment. The present works aim to utilize artificial neural network strategies to automatically extract vocabularies associated with knee OA diseases. Our finding indicates the feasibility of developing word embedding neural networks for autonomous keyword extraction and abstraction of knee OA.
A literature review on different types of empirically evaluated bug localization approaches

Today, software systems have a significant role in various domains among which are healthcare, entertainment, transport and logistics, and many more. It is only natural that with this increasing dependency on software, the number of software systems increases. Additionally, these systems become more and more complex. All this leads to a rise in the number of software faults also known as bugs. As a result, the ability to locate the source of a bug (e.g. a file or a commit) is vital for the development and maintenance of efficient software solutions. Bug localization refers to the automated process of discovering files that contain bugs, based on a bug report. This research project aims to make a literature review on different techniques for bug localization. This study distinguishes itself from other surveys and literature reviews [1] in one significant way. The focus of the work is on identifying, categorizing and analyzing existing bug localization methods and tools which were evaluated in an industrial setting. To the best of my knowledge, there are no other works that prioritise this aspect. Unfortunately, such literature is scarce, therefore, bug localization techniques evaluated on open source software are also included.
Over-the-Air Federated Learning with Enhanced Privacy

Federated learning (FL) has emerged as a promising learning paradigm in which only local model parameters (gradients) are shared. Private user data never leaves the local devices thus preserving data privacy. However, recent research has shown that even when local data is never shared by a user, exchanging model parameters without protection can also leak private information. Moreover, in wireless systems, the frequent transmission of model parameters can cause tremendous bandwidth consumption and network congestion when the model is large. To address this problem, we propose a new FL framework with efficient over-the-air parameter aggregation and strong privacy protection of both user data and models. We achieve this by introducing pairwise cancellable random artificial noises (PCR-ANs) on end devices. As compared to existing over-the-air computation (AirComp) based FL schemes, our design provides stronger privacy protection. We analytically show the secrecy capacity and the convergence rate of the proposed wireless FL aggregation algorithm.
Adaptive and Dynamic Multi-Resolution Hashing for Pairwise Summations

In this paper, we propose Adam-Hash: an adaptive and dynamic multi-resolution hashing data-structure for fast pairwise summation estimation. Given a data-set X ⊂ ℝ<sup>d</sup>, a binary function f : ℝ<sup>d</sup> × ℝ<sup>d</sup> → ℝ, and a point y ∈ ℝ<sup>d</sup>, the Pairwise Summation Estimate $PS{E_X}(y): = \frac{1}{{\left| X \right|}}\sum\nolimits_{x \in X} {f(x,y)} $. For any given data-set X, we need to design a data-structure such that given any query point y ∈ ℝ<sup>d</sup>, the data-structure approximately estimates PSE<inf>X</inf>(y) in time that is sub-linear in |X|. Prior works on this problem have focused exclusively on the case where the data-set is static, and the queries are independent. In this paper, we design a hashing-based PSE data-structure which works for the more practical dynamic setting in which insertions, deletions, and replacements of points are allowed. Moreover, our proposed Adam-Hash is also robust to adaptive PSE queries, where an adversary can choose query q<inf>j</inf> ∈ ℝ<sup>d</sup> depending on the output from previous queries q<inf>1</inf>, q<inf>2</inf>, …, q<inf>j–1</inf>.
Particle velocities and long-range transport in classically forbidden regions

We study the motion of massive particles in a system of two coupled waveguide potentials, where the population transfer between the waveguides eﬀectively acts as a clock and allows particle velocities to be determined. Application of this scheme to tunneling phenomena at a reﬂective step potential reveals an energy-velocity relationship for classically forbidden motion and predicts the existence of classically forbidden long-range particle transport. Our work suggests that density gradients in wave functions are indicative of a genuine quantum mechanical type of motion.
Stability Analysis and Performance Evaluation of Mixed-Precision Runge-Kutta Methods

Additive Runge-Kutta methods designed for preserving highly accurate solutions in mixed-precision computation were proposed and analyzed in 4. These specially designed methods use reduced precision for the implicit computations and full precision for the explicit computations. In this work we analyze the stability properties of these methods and their sensitivity to the low precision rounding errors, and demonstrate their performance in terms of accuracy and efficiency. We develop codes in FORTRAN and Julia to solve nonlinear systems of ODEs and PDEs using the mixed precision additive Runge-Kutta (MP-ARK) methods. The convergence, accuracy, runtime, and energy consumption of these methods is explored. We show that for a given level of accuracy, suitably chosen MP-ARK methods may provide significant reductions in runtime.
Decision-making and control with diffractive optical networks

The ultimate goal of artificial intelligence is to mimic the human brain to perform decision-making and control directly from high-dimensional sensory input. Diffractive optical networks provide a promising solution for implementing artificial intelligence with high-speed and low-power consumption. Most of the reported diffractive optical networks focus on single or multiple tasks that do not involve environmental interaction, such as object recognition and image classification. In contrast, the networks capable of performing decision-making and control have not yet been developed to our knowledge. Here, we propose using deep reinforcement learning to implement diffractive optical networks that imitate human-level decision-making and control capability. Such networks taking advantage of a residual architecture, allow for finding optimal control policies through interaction with the environment and can be readily implemented with existing optical devices. The superior performance of these networks is verified by engaging three types of classic games, Tic-Tac-Toe, Super Mario Bros. , and Car Racing. Finally, we present an experimental demonstration of playing Tic-Tac-Toe by leveraging diffractive optical networks based on a spatial light modulator. Our work represents a solid step forward in advancing diffractive optical networks, which promises a fundamental shift from the target-driven control of a pre-designed state for simple recognition or classification tasks to the high-level sensory capability of artificial intelligence. It may find exciting applications in autonomous driving, intelligent robots, and intelligent manufacturing.
Fabrication of multiple Si nanohole thin films from bulk wafer by controlling metal-assisted etching direction

Crystalline Si photovoltaic modules still have high production cost due to significant consumption of the Si wafer. Reducing the large amount of Si material consumption is thus a critical issue. Here we develop a two-step metal-assisted etching technique for forming vertically-aligned Si nanohole thin films from bulk Si wafers. The formation of Si nanohole thin films includes a series of solution processes: deposition of Ag nanoparticles in an AgNO3/ HF aqueous solution, formation of Si nanohole arrays at the first-step metal-assisted etching, and side etching of the roots of the nanohole structure at the second-step metal-assisted etching. All the processes can proceed at around room temperature. A Si nanohole thin film with an average hole-size of 100 nm and a thickness of 5ìm-20ìm was hence formed at the top of the wafer. Afterwards, the Si nanohole thin film was transferred onto alien substrates. The Si nanohole thin film has the crystal quality similar to the bulk Si wafer. The above bulk Si substrate can be reused. With similar processes, other Si nanohole thin films can be formed from the above recycled Si wafer. The hole size and thickness are similar. The Si wafers recycled will significantly reduce the material consumption of Si. Thus, such technique is promising for lowering the cost of Si solar cells.m.
Heisenberg Spin Chain And Supersymmetric Gauge Theory

We show how a Heisenberg spin chain emerges from the two-dimensional N=(2,2) gauge theory at an intermediate scale, which relies on the renormalization group flow guided by the global symmetries and the dynamics of domain walls. The discussion of higher-dimensional gauge theories with four supercharges is similar by compactifying them into two dimensions. Instead of utilizing the bosonic fields solely in the literature, adopting the fermionic degrees of freedom of gauge theory is a crucial step in our construction. From the perspective of the spin chain, we observe that the Seiberg(-like) duality between two gauge theories is manifest since it is a finite symmetry in the closed spin chain. Based on this, we further conjecture gauge theories with the same global symmetries could be unified into a single system: the Heisenberg spin chain. At least, we prove this conjecture at the intermediate scale. Finally, we also comment on other formulations of an integrable system centered on the Heisenberg spin chain. To brave people ar X iv :2 21 2. 11 28 8v 1 [ he pth ] 2 1 D ec 2 02 2
Interaction-Aware Motion Planning for Autonomous Vehicles with Multi-Modal Obstacle Uncertainties Using Model Predictive Control

—This paper proposes an interaction-aware motion-planning method for an autonomous vehicle in uncertain multi-vehicle trafﬁc environments. An interaction-aware motion-prediction model is used to predict the behaviors of surrounding vehicles. The multi-modal prediction uncertainties, containing both the maneuver and trajectory uncertainties of surrounding vehicles, are considered in the method for resilient motion planning of the ego vehicle. Based on the prediction of the surrounding vehicles, an optimal reference trajectory of the ego vehicle is computed by model predictive control (MPC) to follow the time-varying reference targets and avoid collisions with obstacles. A trade-off between the performance and robustness of the method can be achieved by tuning a safety-awareness parameter in the MPC. The efﬁciency of the method is illustrated in challenging highway-driving simulation scenarios and a driving scenario from a recorded trafﬁc dataset.
The Inverse of Exact Renormalization Group Flows as Statistical Inference

We build on the view of the Exact Renormalization Group (ERG) as an instantiation of Optimal Transport described by a functional convection-diffusion equation. We provide a new information theoretic perspective for understanding the ERG through the intermediary of Bayesian Statistical Inference. This connection is facilitated by the Dynamical Bayesian Inference scheme, which encodes Bayesian inference in the form of a one parameter family of probability distributions solving an integro-differential equation derived from Bayes' law. In this note, we demonstrate how the Dynamical Bayesian Inference equation is, itself, equivalent to a diffusion equation which we dub Bayesian Diffusion. Identifying the features that define Bayesian Diffusion, and mapping them onto the features that define the ERG, we obtain a dictionary outlining how renormalization can be understood as the inverse of statistical inference.
Unscrambling Quantum Information with Clifford Decoders.

Quantum information scrambling is a unitary process that destroys local correlations and spreads information throughout the system, effectively hiding it in nonlocal degrees of freedom. In principle, unscrambling this information is possible with perfect knowledge of the unitary dynamics [B. Yoshida and A. Kitaev, arXiv:1710.03363. ]. However, this Letter demonstrates that even without previous knowledge of the internal dynamics, information can be efficiently decoded from an unknown scrambler by monitoring the outgoing information of a local subsystem. We show that rapidly mixing but not fully chaotic scramblers can be decoded using Clifford decoders. The essential properties of a scrambling unitary can be efficiently recovered, even if the process is exponentially complex. Specifically, we establish that a unitary operator composed of t non-Clifford gates admits a Clifford decoder up to t≤n.
Trajectory Generation and Tracking Control for Aggressive Tail-Sitter Flights

We address the theoretical and practical problems related to the trajectory generation and tracking control of tail-sitter UAVs. Theoretically, we focus on the differential flatness property with full exploitation of actual UAV aerodynamic models, which lays a foundation for generating dynamically feasible trajectory and achieving high-performance tracking control. We have found that a tail-sitter is differentially flat with accurate (not simplified) aerodynamic models within the entire flight envelope, by specifying coordinate flight condition and choosing the vehicle position as the flat output. This fundamental property allows us to fully exploit the high-fidelity aerodynamic models in the trajectory planning and tracking control to achieve accurate tail-sitter flights. Particularly, an optimization-based trajectory planner for tail-sitters is proposed to design high-quality, smooth trajectories with consideration of kinodynamic constraints, singularity-free constraints, and actuator saturation. The planned trajectory of flat output is transformed into state trajectory in real time with optional consideration of wind in environments. To track the state trajectory, a global, singularity-free, and minimally parameterized on-manifold MPC is developed, which fully leverages the accurate aerodynamic model to achieve high-accuracy trajectory tracking within the whole flight envelope. The proposed algorithms are implemented on our quadrotor tail-sitter prototype, “Hong Hu,” and their effectiveness are demonstrated through extensive real-world experiments in both indoor and outdoor field tests, including agile SE(3) flight through consecutive narrow windows requiring specific attitude and with speed up to 10 m/s, typical tail-sitter maneuvers (transition, level flight, and loiter) with speed up to 20 m/s, and extremely aggressive aerobatic maneuvers (Wingover, Loop, Vertical Eight, and Cuban Eight) with acceleration up to 2.5 g. The video demonstration is available at https://youtu.be/2x_bLbVuyrk .
Aliasing is a Driver of Adversarial Attacks

Aliasing is a highly important concept in signal processing, as careful consideration of resolution changes is essential in ensuring transmission and processing quality of au-dio, image, and video. Despite this, up until recently aliasing has received very little consideration in Deep Learning, with all common architectures carelessly sub-sampling without considering aliasing effects. In this work, we investigate the hypothesis that the existence of adversarial perturbations is due in part to aliasing in neural networks. Our ultimate goal is to increase robustness against adversarial attacks using explainable, non-trained, structural changes only, derived from aliasing ﬁrst principles. Our contribu-tions are the following. First, we establish a sufﬁcient con-dition for no aliasing for general image transformations. Next, we study sources of aliasing in common neural network layers, and derive simple modiﬁcations from ﬁrst principles to eliminate or reduce it. Lastly, our experimental results show a solid link between anti-aliasing and adversarial attacks. Simply reducing aliasing already results in more robust classiﬁers, and combining anti-aliasing with robust training out-performs solo robust training on L 2 attacks with none or minimal losses in performance on L ∞ attacks.
Reusable Options through Gradient-based Meta Learning

Hierarchical methods in reinforcement learning have the potential to reduce the amount of decisions that the agent needs to perform when learning new tasks. However, finding reusable useful temporal abstractions that facilitate fast learning remains a challenging problem. Recently, several deep learning approaches were proposed to learn such temporal abstractions in the form of options in an end-to-end manner. In this work, we point out several shortcomings of these methods and discuss their potential negative consequences. Subsequently, we formulate the desiderata for reusable options and use these to frame the problem of learning options as a gradient-based meta-learning problem. This allows us to formulate an objective that explicitly incentivizes options which allow a higher-level decision maker to adjust in few steps to different tasks. Experimentally, we show that our method is able to learn transferable components which accelerate learning and performs better than existing prior methods developed for this setting. Additionally, we perform ablations to quantify the impact of using gradient-based meta-learning as well as other proposed changes.