Miracle-dz/UniFAR-FTUs huggingface dataset:
MG3MConv: Multi-Grained Matrix-Multiplication-Mapping Convolution Algorithm toward the SW26010 Processor

As the core of artificial intelligence applications, the research of convolution has become a hot topic in high performance computing. With the rapid development of the emerging SW26010 processor in artificial intelligence, there is an urgent need for high-performance convolution algorithms on the processor. However, the current support of convolution on SW26010 is still rudimentary. The only studies provide sufficient runtime peak performance but lack the adaptability to various convolution scenes. To perfect convolution algorithms on SW26010, we propose a multi-grained matrix-multiplication-mapping convolution algorithm called MG3MConv, which targets the architectural features of SW26010. MG3MConv supports diversified mapping schemes of convolution tasks based on the concept of the thread block proposed in this paper. All the architecture-oriented optimization methods are elaborately designed from four levels to fully exploit the hardware efficiency of SW26010. The experiments show that the hardware efficiency of MG3MConv can reach 84.78% in max, which is 1.75 times compared with that of cuDNN based on NVIDIA K80m GPU. Moreover, MG3MConv can overperform cuDNN in most convolution scenes. We also use six representative CNNs as real-world cases, and the hardware efficiency of MG3MConv reaches up to 67.04% on the VGG network model, which is 1.37 times and 1.96 times that of cuDNN and swDNN, respectively.
Graphene is neither Relativistic nor Non-Relativistic case: Thermodynamics Aspects

Discovery of electron hydrodynamics in graphene system has opened a new scope of analytic calculations in condensed matter physics, which was traditionally well cultivated in science and engineering as a non-relativistic hydrodynamics and in high energy nuclear and astro physics as relativistic hydrodynamics. Electrons in graphene follow neither non-relativistic nor relativistic hydrodynamics and thermodynamics. Present article has gone through systematic microscopic calculations of thermodynamical quantities like pressure, energy density, etc. of electron-fluid in graphene and compared with corresponding estimations for non-relativistic and ultra-relativistic cases. Identifying the Dirac fluid and Fermi liquid domains, we have sketched the transition of temperature and Fermi energy dependency of electron thermodynamics for graphene and other cases. An equivalent transition for quark matter is also discussed. The most exciting part is the general expression of specific heat, whose Fermi to Dirac fluid domain transition can be realized as a transition from a solid-based to a fluid-based picture. This understanding may be connected to the experimentally observed Wiedemann-Franz Law violation in the Dirac fluid domain of graphene system.
A Survey From Distributed Machine Learning to Distributed Deep Learning

Artificial intelligence has made remarkable progress in handling complex tasks, thanks to advances in hardware acceleration and machine learning algorithms. However, to acquire more accurate outcomes and solve more complex issues, algorithms should be trained with more data. Processing this huge amount of data could be time-consuming and require a great deal of computation. To address these issues, distributed machine learning has been proposed, which involves distributing the data and algorithm across several machines. There has been considerable effort put into developing distributed machine learning algorithms, and different methods have been proposed so far. We divide these algorithms in classification and clustering (traditional machine learning), deep learning and deep reinforcement learning groups. Distributed deep learning has gained more attention in recent years and most of the studies have focused on this approach. Therefore, we mostly concentrate on this category. Based on the investigation of the mentioned algorithms, we highlighted the limitations that should be addressed in future research.
ProgGP: From GuitarPro Tablature Neural Generation To Progressive Metal Production

Recent work in the field of symbolic music generation has shown value in using a tokenization based on the GuitarPro format, a symbolic representation supporting guitar expressive attributes, as an input and output representation. We extend this work by fine-tuning a pre-trained Transformer model on ProgGP, a custom dataset of 173 progressive metal songs, for the purposes of creating compositions from that genre through a human-AI partnership. Our model is able to generate multiple guitar, bass guitar, drums, piano and orchestral parts. We examine the validity of the generated music using a mixed methods approach by combining quantitative analyses following a computational musicology paradigm and qualitative analyses following a practice-based research paradigm. Finally, we demonstrate the value of the model by using it as a tool to create a progressive metal song, fully produced and mixed by a human metal producer based on AI-generated music.
Encoder Complexity Control in SVT-AV1 by Speed-Adaptive Preset Switching

Current developments in video encoding technology lead to continuously improving compression performance but at the expense of increasingly higher computational demands. Regarding the online video traffic increases during the last years and the concomitant need for video encoding, encoder complexity control mechanisms are required to restrict the processing time to a sufficient extent in order to find a reasonable trade-off between performance and complexity. We present a complexity control mechanism in SVT-AV1 by using speed-adaptive preset switching to comply with the remaining time budget. This method enables encoding with a user-defined time constraint within the complete preset range with an average precision of 8.9 % without introducing any additional latencies.
FairLay-ML: Intuitive Remedies for Unfairness in Data-Driven Social-Critical Algorithms

This thesis explores open-sourced machine learning (ML) model explanation tools to understand whether these tools can allow a layman to visualize, understand, and suggest intuitive remedies to unfairness in ML-based decision-support systems. Machine learning models trained on datasets biased against minority groups are increasingly used to guide life-altering social decisions, prompting the urgent need to study their logic for unfairness. Due to this problem's impact on vast populations of the general public, it is critical for the layperson -- not just subject matter experts in social justice or machine learning experts -- to understand the nature of unfairness within these algorithms and the potential trade-offs. Existing research on fairness in machine learning focuses mostly on the mathematical definitions and tools to understand and remedy unfair models, with some directly citing user-interactive tools as necessary for future work. This thesis presents FairLay-ML, a proof-of-concept GUI integrating some of the most promising tools to provide intuitive explanations for unfair logic in ML models by integrating existing research tools (e.g. Local Interpretable Model-Agnostic Explanations) with existing ML-focused GUI (e.g. Python Streamlit). We test FairLay-ML using models of various accuracy and fairness generated by an unfairness detector tool, Parfait-ML, and validate our results using Themis. Our study finds that the technology stack used for FairLay-ML makes it easy to install and provides real-time black-box explanations of pre-trained models to users. Furthermore, the explanations provided translate to actionable remedies.
Long memory in the Hybrid Time Series

In this paper, consideration is given to the assessment of the availability of long-term memory in a time series with variable coefficients that depend on the Markov chain or the continuous Markov process. In the work we succeeded in establishing sufficient conditions for the present of a long-term memory based on a multifractal detrended fluctuation analysis and a Geweke – Porter-Hudak method. A real example is analysed of the Erste Group in the period 07.01. 2000 – 23.10. 2017, as a result of which it was possible to prove that this company.
$\mathcal{A}$-theory: A brane world-volume theory with manifest U-duality

In this paper, the ${\cal A}$-theory, an extension of F-theory, is described as a fully U-duality covariant brane theory. This theory has some distinguishing features not known from world-sheet models. In particular, seen as a sigma model, both world-volume and target space coordinates are specific representations of the same group (the U-duality group). The U-duality group in question is an exceptional group (a split form of the $E_d$ series). The structure of this group allows it to encompass both the T-duality group of string theory as well as the general linear symmetry group of ${\cal M}$-theory. ${\cal A}$-theory is defined by the current algebras in Hamiltonian formalism, or by world-volume actions in Lagrangian formalism. The spacetime coordinates are selfdual gauge fields on the world-volume, requiring the Gauss law constraints tying the world-volume to spacetime. Solving the Gauss law constraints/the Virasoro constraints gives the world-volume/spacetime sectioning from ${\cal A}$-theory to ${\cal T}$-theory/ ${\cal M}$-theory respectively. The ${\cal A}$-theory Lagrangian admits extended symmetry which has not been observed previously in the literature, where the background fields include both the spacetime and the world-volume gravitational fields. We also constructed the four-point amplitude of ${\cal A}$-theory in the low energy limit. The amplitude is written in a way that the U-duality symmetry is manifest, but after solving the section condition, it reduces to the usual four-graviton amplitude. In the previous papers, we have referred to this model as F-theory, however, F-theory initiated by Vafa is now a big branch of string theory as the study of elliptic fibrations, so we refer to these constructions as generalized models of theory for all dimensions with all duality symmetries as ${\cal A}$-theory.
Activation of hidden nonlocality using local filtering operations based on CGLMP inequality

Entanglement is necessary but not sufficient to demonstrate nonlocality as there exist local entangled states which do not violate any Bell inequality. In recent years, the activation of nonlocality (known as hidden nonlocality) by using local filtering operations has gained considerable interest. In the original proposal of Popescu [Phys. Rev. Lett. 74, 2619 (1995)] the hidden nonlocality was demonstrated for the Werner class of states in $d \geq 5$. In this paper, we demonstrate the hidden nonlocality for a class of mixed entangled states (convex mixture of a pure state and color noise) in an arbitrary $d$-dimensional system using suitable local filtering operations. For our demonstration, we consider the quantum violation of Collins-Linden-Gisin-Masser-Popescu (CGLMP) inequality which has hitherto not been considered for this purpose. We show that when the pure state in the aforementioned mixed entangled state is a maximally entangled state, the range of the mixing parameter for revealing hidden nonlocality increases with increasing the dimension of the system. Importantly, we find that for $d \geq 8$, hidden non-locality can be revealed for the whole range of mixing parameter. Further, by considering another pure state, the maximally CGLMP-violating state, we demonstrate the activation of nonlocality by using the same local filtering operation.
Continuity of Theory Structure: A Conceptual Spaces Approach

ABSTRACT By understanding laws of nature as geometrical rather than linguistic entities, this paper addresses how to describe theory structures and how to evaluate their continuity. Relying on conceptual spaces as a modelling tool, we focus on the conceptual framework an empirical theory presupposes, thus obtain a geometrical representation of a theory’s structure. We stress the relevance of measurement procedures in separating conceptual from empirical structures. This lets our understanding of scientific laws come closer to scientific practice, and avoids a widely recognised deficit in current philosophy of science accounts, namely to risk a collapse of the physical into the mathematical.
Anomalous Andreev Spectrum and Transport in Non-Hermitian Josephson Junctions

We propose a phase-biased non-Hermitian Josephson junction (NHJJ) composed of two superconductors mediated by a short non-Hermitian link. We find that its Andreev spectrum as a function of phase difference exhibits Josephson gaps, i.e. finite phase windows, in which Andreev quasi-bound states do not exist. Remarkably, exceptional points emerge in the complex Andreev spectrum, constituting a particular spectral feature of NHJJs. Moreover, we propose complex supercurrents due to inelastic Cooper pair tunneling. At the exceptional points, the corresponding phase-biased supercurrent divergences. Our results illustrate that the Josephson effect is strongly affected by non-Hermitian physics.
Blockchain‐enhanced trust management for mobile edge computing‐enabled intelligent vehicular collaboration in the 6G era

The rapid development and increasing popularity of mobile devices have facilitated the development of computation‐intensive applications and services, but their constrained compute, storage and battery capabilities have also created the requirements for optimized resource provision and service orchestration. Emerging mobile edge computing (MEC) and next‐generation networks (6G) can meet these requirements of mobile devices by migrating complex computing tasks and large volumes of data to edge servers with acceptable latency, thus effectively enhancing the quality of experience (QoE) of mobile users. Intelligent Vehicular Collaboration, as an intelligent mobile application or service in intelligent connected vehicle (ICV) applications, enables precise and effective driving decisions based on a large amount of real‐time traffic information statistics and intelligent analysis of vehicle status to improve the user driving experience and the efficiency of intelligent transportation systems(ITS). However, the openness and dynamics of wireless communications and the high frequency of information interaction of MEC‐enabled intelligent vehicular collaboration services in the 6G era make them highly vulnerable to cyber attacks. How to achieve dynamically trustworthy, secure, and intelligent vehicular collaboration services in the presence of malicious node access and cyber attacks in the 6G MEC‐enabled vehicular network is a significant challenge. In this paper, we propose a novel blockchain‐enabled trust management mechanism to address this challenge. First, we design a spatiotemporal‐correlated message credibility assessment method and a dynamic mutual trust evaluation method to obtain accurate ratings for messages and mobile nodes respectively. Based on the above methods, we propose an improved blockchain‐based intelligent trust management mechanism that enables transparent, tamper‐evident, and trustworthy secure intelligent vehicular collaboration services while maintaining stable and efficient system performance. Simulation results show that our suggested solutions can efficiently evaluate the trust level of ICVs and achieve efficient and secure intelligent vehicular collaboration with acceptable communication and computation overheads.
SecFlow: Adaptive Security-Aware Workflow Management System in Multi-Cloud Environments

In this paper, we propose an architecture for a security-aware workflow management system (WfMS) we call SecFlow in answer to the recent developments of combining workflow management systems with Cloud environments and the still lacking abilities of such systems to ensure the security and privacy of cloud-based workflows. The SecFlow architecture focuses on full workflow life cycle coverage as, in addition to the existing approaches to design security-aware processes, there is a need to fill in the gap of maintaining security properties of workflows during their execution phase. To address this gap, we derive the requirements for such a security-aware WfMS and design a system architecture that meets these requirements. SecFlow integrates key functional components such as secure model construction, security-aware service selection, security violation detection, and adaptive response mechanisms while considering all potential malicious parties in multi-tenant and cloud-based WfMS.
Primordial magnetic fields and the Hubble tension

Magnetic fields appear to be present in essentially all astrophysical environments, including galaxies, clusters of galaxies and voids. There are both observational and theoretical motives for considering the possibility of their origin tracing back to the events in the very early universe, such as the electroweak phase transition or Inflation. Such a primordial magnetic field (PMF) would remain embedded in the plasma and evolve to persist through the radiation and matter eras, and to the present day. As described in this Chapter, a PMF present in the primordial plasma prior to recombination could help relieve the Hubble tension. A stochastic magnetic field would induce inhomogeneities, pushing the baryons into regions of lower magnetic energy density and speeding up the recombination process. As a consequence, the sound horizon at last scattering would be smaller, which is a necessary ingredient for relieving the Hubble tension. Intriguingly, the strength of the magnetic field required to alleviate the tension is of the right order to also explain the observed magnetic fields in galaxies, clusters of galaxies and voids. These findings motivate further detailed studies of recombination in the presence of PMFs and observational tests of this hypothesis.
MentalHealthAI: Utilizing Personal Health Device Data to Optimize Psychiatry Treatment

Mental health disorders remain a significant challenge in modern healthcare, with diagnosis and treatment often relying on subjective patient descriptions and past medical history. To address this issue, we propose a personalized mental health tracking and mood prediction system that utilizes patient physiological data collected through personal health devices. Our system leverages a decentralized learning mechanism that combines transfer and federated machine learning concepts using smart contracts, allowing data to remain on users' devices and enabling effective tracking of mental health conditions for psychiatric treatment and management in a privacy-aware and accountable manner. We evaluated our model using a popular mental health dataset, which yielded promising results. By utilizing connected health systems and machine learning models, our approach offers a novel solution to the challenge of providing psychiatrists with further insight into their patients' mental health outside of traditional office visits.
The GECAM Real-Time Burst Alert System

Gravitational Wave High-energy Electromagnetic Counterpart All-sky Monitor (GECAM), consisting of two micro-satellites, is designed to detect gamma-ray bursts associated with gravitational-wave events. Here, we introduce the real-time burst alert system of GECAM, with the adoption of the BeiDou-3 short message communication service. We present the post-trigger operations, the detailed ground-based analysis, and the performance of the system. In the first year of the in-flight operation, GECAM was triggered by 42 GRBs. GECAM real-time burst alert system has the ability to distribute the alert within $\sim$1 minute after being triggered, which enables timely follow-up observations.
Invariant Scattering Transform for Medical Imaging

Invariant scattering transform introduces new area of research that merges the signal processing with deep learning for computer vision. Nowadays, Deep Learning algorithms are able to solve a variety of problems in medical sector. Medical images are used to detect diseases brain cancer or tumor, Alzheimer's disease, breast cancer, Parkinson's disease and many others. During pandemic back in 2020, machine learning and deep learning has played a critical role to detect COVID-19 which included mutation analysis, prediction, diagnosis and decision making. Medical images like X-ray, MRI known as magnetic resonance imaging, CT scans are used for detecting diseases. There is another method in deep learning for medical imaging which is scattering transform. It builds useful signal representation for image classification. It is a wavelet technique; which is impactful for medical image classification problems. This research article discusses scattering transform as the efficient system for medical image analysis where it's figured by scattering the signal information implemented in a deep convolutional network. A step by step case study is manifested at this research work.
Neural Point-based Volumetric Avatar: Surface-guided Neural Points for Efficient and Photorealistic Volumetric Head Avatar

Rendering photorealistic and dynamically moving human heads is crucial for ensuring a pleasant and immersive experience in AR/VR and video conferencing applications. However, existing methods often struggle to model challenging facial regions (e.g., mouth interior, eyes, and beard), resulting in unrealistic and blurry results. In this paper, we propose Neural Point-based Volumetric Avatar (NPVA), a method that adopts the neural point representation as well as the neural volume rendering process and discards the predefined connectivity and hard correspondence imposed by mesh-based approaches. Specifically, the neural points are strategically constrained around the surface of the target expression via a high-resolution UV displacement map, achieving increased modeling capacity and more accurate control. We introduce three technical innovations to improve the rendering and training efficiency: a patch-wise depth-guided (shading point) sampling strategy, a lightweight radiance decoding process, and a Grid-Error-Patch (GEP) ray sampling strategy during training. By design, our NPVA is better equipped to handle topologically changing regions and thin structures while also ensuring accurate expression control when animating avatars. Experiments conducted on three subjects from the Multiface dataset demonstrate the effectiveness of our designs, outperforming previous state-of-the-art methods, especially in handling challenging facial regions.
Direct sampling of short paths for contiguous partitioning

In this paper, we provide a family of dynamic programming based algorithms to sample nearly-shortest self avoiding walks between two points of the integer lattice $\mathbb{Z}^2$. We show that if the shortest path of between two points has length $n$, then we can sample paths (self-avoiding-walks) of length $n+O(n^{1-\delta})$ in polynomial time. As an example of an application, we will show that the Glauber dynamics Markov chain for partitions of the Aztec Diamonds in $\mathbb{Z}^2$ into two contiguous regions with nearly tight perimeter constraints has exponential mixing time, while the algorithm provided in this paper can be used be used to uniformly (and exactly) sample such partitions efficiently.