Miracle-dz/UniFAR-FTUs huggingface dataset:
Parameter and
 
 q
 
 asymptotics of Lq$$ {\mathfrak{L}}_q $$‐norms of hypergeometric orthogonal polynomials

The three canonical families of the hypergeometric orthogonal polynomials (Hermite, Laguerre and Jacobi) control the physical wavefunctions of the bound stationary states of a great deal of quantum systems. The algebraic L q -norms of these polynomials describe many physical, chemical and information-theoretical properties of these systems, such as e.g. the kinetic and Weizs¨acker energies, the position and momentum expectation values, the R´enyi and Shannon entropies and the Cram´er-Rao, the Fisher-Shannon and LMC measures of complexity. In this work we examine, partially review and solve the q -asymptotics and the parameter asymptotics (i.e., when the weight function’s parameter tends towards inﬁnity) of the unweighted and weighted L q -norms for these orthogonal polynomials. This study has been motivated by the application of these algebraic norms to the energetic, entropic and complexity-like properties of the highly-excited
Investigating Black-Box Function Recognition Using Hardware Performance Counters

This paper presents new methods and results for recognising black-box program functions using hardware performance counters (HPC), where an investigator can invoke and measure function calls. Important use cases include analysing compiled libraries, e.g., static and dynamic link libraries, and trusted execution environment (TEE) applications. We develop a generic approach to classify a comprehensive set of hardware events, e.g., branch mis-predictions and instruction retirements, to recognise standard benchmarking and cryptographic library functions. This includes various signing, verification and hash functions, and ciphers in numerous modes of operation. Three architectures are evaluated using off-the-shelf Intel/X86-64, ARM, and RISC-V CPUs. Next, we show that several known CVE-numbered OpenSSL vulnerabilities can be detected using HPC differences between patched and unpatched library versions. Further, we demonstrate that standardised cryptographic functions within ARM TrustZone TEE applications can be recognised using non-secure world HPC measurements, applying to platforms that insecurely perturb the performance monitoring unit (PMU) during TEE execution. High accuracy was achieved in all cases (86.22–99.83%) depending on the application, architectural, and compilation assumptions. Lastly, we discuss mitigations, outstanding challenges, and directions for future research.
Sparsity-Exploiting Distributed Projections onto a Simplex

Projecting a vector onto a simplex is a well-studied problem that arises in a wide range of optimization problems. Numerous algorithms have been proposed for determining the projection; however, the primary focus of the literature is on serial algorithms. We present a parallel method that decomposes the input vector and distributes it across multiple processors for local projection. Our method is especially effective when the resulting projection is highly sparse, which is the case, for instance, in large-scale problems with independent and identically distributed (i.i.d.) entries. Moreover, the method can be adapted to parallelize a broad range of serial algorithms from the literature. We fill in theoretical gaps in serial algorithm analysis and develop similar results for our parallel analogues. Numerical experiments conducted on a wide range of large-scale instances, both real world and simulated, demonstrate the practical effectiveness of the method. History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms—Continuous. Funding: This work was supported by the Office of Naval Research [Grant N00014-23-1-2632]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0328 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0328 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github. io/ .
Real-Time Neural Character Rendering with Pose-Guided Multiplane Images

We propose pose-guided multiplane image (MPI) synthesis which can render an animatable character in real scenes with photorealistic quality. We use a portable camera rig to capture the multi-view images along with the driving signal for the moving subject. Our method generalizes the image-to-image translation paradigm, which translates the human pose to a 3D scene representation -- MPIs that can be rendered in free viewpoints, using the multi-views captures as supervision. To fully cultivate the potential of MPI, we propose depth-adaptive MPI which can be learned using variable exposure images while being robust to inaccurate camera registration. Our method demonstrates advantageous novel-view synthesis quality over the state-of-the-art approaches for characters with challenging motions. Moreover, the proposed method is generalizable to novel combinations of training poses and can be explicitly controlled. Our method achieves such expressive and animatable character rendering all in real time, serving as a promising solution for practical applications.
A Review of Research on Civic Technology: Definitions, Theories, History and Insights

There have been initiatives that take advantage of information and communication technologies to serve civic purposes, referred to as civic technologies (Civic Tech). In this paper, we present a review of 224 papers from the ACM Digital Library focusing on Computer Supported Cooperative Work and Human-Computer Interaction, the key fields supporting the building of Civic Tech. Through this review, we discuss the concepts, theories and history of civic tech research and provide insights on the technological tools, social processes and participation mechanisms involved. Our work seeks to direct future civic tech efforts to the phase of “by the citizens”.
Antiferromagnetic Spin Orientation and Magnetic Domain Structure in Epitaxially Grown 
MnN
 Studied Using Optical Second-Harmonic Generation

MnN is a centrosymmetric collinear antiferromagnet belonging to the transition metal nitride family with a high Neel temperature ( ∼ 660 K), a low anisotropy ﬁeld, and a large magnetic moment ( ∼ 3.3 µ B ) per Mn atom. Despite several recent experimental and theoretical studies, the spin symmetry (magnetic point group) and magnetic domain structure of the material remain unknown. In this work, we use optical second harmonic generation (SHG) to study the magnetic structure of thin epitaxially-grown single-crystal (001) MnN ﬁlms. Our work shows that spin moments in MnN are tilted away from the [001] direction and the components of the spin moments in the (001) plane are aligned along one of the the two possible in-plane symmetry axes ([100] or [110]) resulting in a magnetic point group symmetry of 2/m1’. Our work rules out magnetic point group symmetries 4/mmm1’ and mmm1’ that have been previously discussed in the literature. Four different spin domains consistent with the 2/m1’ magnetic point group symmetry are possible in MnN. A statistical model based on the observed variations in the polarization-dependent intensity of the second harmonic signal collected over large sample areas puts an upper bound of ∼ 0.65 µ m on the mean domain size. Our results show that SHG can be used to probe the magnetic order in metallic antiferromagnets. This work is expected to contribute to the recent efforts in using antiferromagnets for spintronic applications.
La Ingeniería de Control y su Rol Actual en la Medicina

En la actualidad, a pesar del gran avance tecnológico al que continuamente estamos expuestos, todavía existe un gran desconocimiento acerca de la importancia y participación de la ingeniería de control en todas las ramas de la industria y de la tecnología moderna. En este sentido, es aún más desconocido el rol que tiene esta ingeniería en una gran cantidad de procedimientos clínicos y en general como sustento teórico para dar explicación a la gran cantidad de procesos fisiológicos que posee el cuerpo humano.
RealNet: Combining Optimized Object Detection with Information Fusion Depth Estimation Co-Design Method on IoT

– Depth Estimation and Object Detection Recognition play an important role in autonomous driving technology under the guidance of deep learning artiﬁcial intelligence [25]. We propose a hybrid structure called RealNet: a co-design method combining the model-streamlined recognition algorithm, the depth estimation algorithm with information fusion, and deploying them on the Jetson-Nano for unmanned vehicles with monocular vision sensors. We use ROS for experiment. The method proposed in this paper is suitable for mobile platforms with high real-time request. Innovation of our method is using information fusion to compensate the problem of insufﬁcient frame rate of output image, and improve the robustness of target detection and depth estimation under monocular vision.Object Detection is based on YOLOv5( https://github.com/ultralytics/yolov5) [28]. We have simpliﬁed the network structure of its DarkNet53 [7] and realized a prediction speed up to 0.01s. Depth Estimation is based on the VNL Depth Estimation, which considers multiple geometric constraints in 3D global space. [48] It calculates the loss function by calculating the deviation of the virtual normal vector VN and the label, which can obtain deeper depth information. We use PnP fusion algorithm to solve the problem of insufﬁcient frame rate of depth map output. It solves the motion estimation depth from three-dimensional target to two-dimensional point based on corner feature matching, which is faster than VNL calculation. We interpolate VNL output and PnP [33] output to achieve information fusion. Experiments show that this can effectively eliminate the jitter of depth information and improve robustness. At the control end, this method combines the results of target detection and depth estimation to calculate the target position, and uses a pure tracking control algorithm to track it.
Learning by Erasing: Conditional Entropy based Transferable Out-Of-Distribution Detection

Detecting OOD inputs is crucial to deploy machine learning models to the real world safely. However, existing OOD detection methods require an in-distribution (ID) dataset to retrain the models. In this paper, we propose a Deep Generative Models (DGMs) based transferable OOD detection that does not require retraining on the new ID dataset. We first establish and substantiate two hypotheses on DGMs: DGMs exhibit a predisposition towards acquiring low-level features, in preference to semantic information; the lower bound of DGM's log-likelihoods is tied to the conditional entropy between the model input and target output. Drawing on the aforementioned hypotheses, we present an innovative image-erasing strategy, which is designed to create distinct conditional entropy distributions for each individual ID dataset. By training a DGM on a complex dataset with the proposed image-erasing strategy, the DGM could capture the discrepancy of conditional entropy distribution for varying ID datasets, without re-training. We validate the proposed method on the five datasets and show that, without retraining, our method achieves comparable performance to the state-of-the-art group-based OOD detection methods. The project codes will be open-sourced on our project website.
Smart App Attack: Hacking Deep Learning Models in Android Apps

On-device deep learning is rapidly gaining popularity in mobile applications. Compared to offloading deep learning from smartphones to the cloud, on-device deep learning enables offline model inference while preserving user privacy. However, such mechanisms inevitably store models on users’ smartphones and may invite adversarial attacks as they are accessible to attackers. Due to the characteristic of the on-device model, most existing adversarial attacks cannot be directly applied for on-device models. In this paper, we introduce a grey-box adversarial attack framework to hack on-device models by crafting highly similar binary classification models based on identified transfer learning approaches and pre-trained models from TensorFlow Hub. We evaluate the attack effectiveness and generality in terms of four different settings including pre-trained models, datasets, transfer learning approaches and adversarial attack algorithms. The results demonstrate that the proposed attacks remain effective regardless of different settings, and significantly outperform state-of-the-art baselines. We further conduct an empirical study on real-world deep learning mobile apps collected from Google Play. Among 53 apps adopting transfer learning, we find that 71.7% of them can be successfully attacked, which includes popular ones in medicine, automation, and finance categories with critical usage scenarios. The results call for the awareness and actions of deep learning mobile app developers to secure the on-device models. The code of this work is available at https://github.com/Jinxhy/SmartAppAttack.
Suppressing Chemical Corrosions of Lithium Metal Anodes

The lithium metal anode is essential for next‐generation high‐energy‐density rechargeable Li‐metal batteries. Although extensive studies have been performed to prolong the cycle life of Li‐metal batteries, the calendar life, which associates with the chemical corrosion of Li metal in liquid electrolytes, has not been quantitatively understood. Here, by combing the titration gas chromatography method and cryogenic focused ion beam, a quantitative relationship between the chemical corrosion rate and electrochemically deposited Li morphology in various liquid electrolyte systems is established. It has been identified that the corrosion rate is dominated by the porosity of the deposited Li. The larger the porosity of deposited Li has, the faster the corrosion rate will be. Strategies to mitigate the chemical corrosion on Li thus to extend the calendar life of Li‐metal batteries are further proposed. By strictly controlling the stacking pressure during Li plating, Li deposits with ultra‐low porosity can be achieved, suppressing the corrosion rate to 0.08 ± 0.16%/day compared with 1.71 ± 0.19%/day of the high‐porosity Li.
DIMIX: Diminishing Mixing for Sloppy Agents

We study non-convex distributed optimization problems where a set of agents collaboratively solve a separable optimization problem that is distributed over a time-varying network. The existing methods to solve these problems rely on (at most) one time-scale algorithms, where each agent performs a diminishing or constant step-size gradient descent at the average estimate of the agents in the network. However, if possible at all, exchanging exact information, that is required to evaluate these average estimates, potentially introduces a massive communication overhead. Therefore, a reasonable practical assumption to be made is that agents only receive a rough approximation of the neighboring agents' information. To address this, we introduce and study a \textit{two time-scale} decentralized algorithm with a broad class of \textit{lossy} information sharing methods (that includes noisy, quantized, and/or compressed information sharing) over \textit{time-varying} networks. In our method, one time-scale suppresses the (imperfect) incoming information from the neighboring agents, and one time-scale operates on local cost functions' gradients. We show that with a proper choices for the step-sizes' parameters, the algorithm achieves a convergence rate of $\mathcal{O}({T}^{-1/3 + \epsilon})$ for non-convex distributed optimization problems over time-varying networks, for any $\epsilon>0$. Our simulation results support the theoretical results of the paper.
Revisiting multi-breathers in the discrete Klein–Gordon equation: a spatial dynamics approach

We consider the existence and spectral stability of multi-breather structures in the discrete Klein–Gordon equation, both for soft and hard symmetric potentials. To obtain analytical results, we project the system onto a finite-dimensional Hilbert space consisting of the first M Fourier modes, for arbitrary M. On this approximate system, we then take a spatial dynamics approach and use Lin’s method to construct multi-breathers from a sequence of well-separated copies of the primary, single-site breather. We then locate the eigenmodes in the Floquet spectrum associated with the interaction between the individual breathers of such multi-breather states by reducing the spectral problem to a matrix equation. Expressions for these eigenmodes for the approximate, finite-dimensional system are obtained in terms of the primary breather and its kernel eigenfunctions, and these are found to be in very good agreement with the numerical Floquet spectrum results. This is supplemented with results from numerical timestepping experiments, which are interpreted using the spectral computations.
A Time-Triggered Dimension Reduction Algorithm for the Task Assignment Problem

The task assignment problem is fundamental in combinatorial optimisation, aiming at allocating one or more tasks to a number of agents while minimizing the total cost or maximizing the overall assignment beneﬁt. This problem is known to be computationally hard since it is usually formulated as a mixed-integer programming problem. In this paper, we consider a novel time-triggered dimension reduction algorithm (TTDRA). We propose convexiﬁcation approaches to convexify both the constraints and the cost function for the general non-convex assignment problem. The computational speed is accelerated via our time-triggered dimension reduction scheme, where the triggering condition is designed based on the optimality tolerance and the convexity of the cost function. Optimality and computational eﬃciency are veriﬁed via numerical simulations on benchmark examples.
Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach

Recently, Deep Neural Networks (DNNs) have been widely introduced into Collaborative Filtering (CF) to produce more accurate recommendation results due to their capability of capturing the complex nonlinear relationships between items and users.However, the DNNs-based models usually suffer from high computational complexity, i.e., consuming very long training time and storing huge amount of trainable parameters. To address these problems, we propose a new broad recommender system called Broad Collaborative Filtering (BroadCF), which is an efficient nonlinear collaborative filtering approach. Instead of DNNs, Broad Learning System (BLS) is used as a mapping function to learn the complex nonlinear relationships between users and items, which can avoid the above issues while achieving very satisfactory recommendation performance. However, it is not feasible to directly feed the original rating data into BLS. To this end, we propose a user-item rating collaborative vector preprocessing procedure to generate low-dimensional user-item input data, which is able to harness quality judgments of the most similar users/items. Extensive experiments conducted on seven benchmark datasets have confirmed the effectiveness of the proposed BroadCF algorithm
Hate Me Not: Detecting Hate Inducing Memes in Code Switched Languages

The rise in the number of social media users has led to an increase in the hateful content posted online. In countries like India, where multiple languages are spoken, these abhorrent posts are from an unusual blend of code-switched languages. This hate speech is depicted with the help of images to form"Memes"which create a long-lasting impact on the human mind. In this paper, we take up the task of hate and offense detection from multimodal data, i.e. images (Memes) that contain text in code-switched languages. We firstly present a novel triply annotated Indian political Memes (IPM) dataset, which comprises memes from various Indian political events that have taken place post-independence and are classified into three distinct categories. We also propose a binary-channelled CNN cum LSTM based model to process the images using the CNN model and text using the LSTM model to get state-of-the-art results for this task.
Low-Complexity Suboptimal ML Detection for OFDM-IM Systems

Orthogonal frequency division multiplexing with index modulation (OFDM-IM) is a novel multicarrier scheme, which uses <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> out of <inline-formula> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula> subcarriers as active subcarriers to transmit data. For detecting the subcarrier activation pattern (SAP) at the receiver, maximum likelihood (ML) detection cannot be used because of its high computational complexity. Instead, the detector selecting the most likely active <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> subcarriers is used, which is called a <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> largest values (<inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>lv) detector. However, this method degrades the detection performance especially if the ratio of illegal SAPs to SAPs is high. In this letter, the suboptimal ML detector is proposed, which is a simple modification of the <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>lv detector, but very efficient. The proposed detector has a similar detection performance compared to the ML detection, which is suitable for flexible implementation of OFDM-IM systems.
A Survey of Traversability Estimation for Mobile Robots

Traversability illustrates the difficulty of driving through a specific region and encompasses the suitability of the terrain for traverse based on its physical properties, such as slope and roughness, surface condition, etc. In this survey we highlight the merits and limitations of all the major steps in the evolution of traversability estimation techniques, covering both non-trainable and machine-learning methods, leading up to the recent proliferation of deep learning literature. We discuss how the nascence of Deep Learning has created an opportunity for radical improvement in traversability estimation. Finally, we discuss how self-supervised learning can help satisfy deep methods’ increased need for (challenging to acquire and label) large-scale datasets.
Maximizing Non-Monotone Submodular Functions over Small Subsets: Beyond 1/2-Approximation

In this work we give two new algorithms that use similar techniques for (non-monotone) submodular function maximization subject to a cardinality constraint. The first is an offline fixed-parameter tractable algorithm that guarantees a 0 . 539-approximation for all non-negative submodular functions. The second algorithm works in the random-order streaming model. It guarantees a (1 / 2 + c )-approximation for symmetric functions, and we complement it by showing that no space-efficient algorithm can beat 1 / 2 for asymmetric functions. To the best of our knowledge this is the first provable separation between symmetric and asymmetric submodular function maximization.
MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction

This paper presents MuCGEC, a multi-reference multi-source evaluation dataset for Chinese Grammatical Error Correction (CGEC), consisting of 7,063 sentences collected from three Chinese-as-a-Second-Language (CSL) learner sources. Each sentence is corrected by three annotators, and their corrections are carefully reviewed by a senior annotator, resulting in 2.3 references per sentence. We conduct experiments with two mainstream CGEC models, i.e., the sequence-to-sequence model and the sequence-to-edit model, both enhanced with large pretrained language models, achieving competitive benchmark performance on previous and our datasets. We also discuss CGEC evaluation methodologies, including the effect of multiple references and using a char-based metric. Our annotation guidelines, data, and code are available at https://github.com/HillZhang1999/MuCGEC.