Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Comprehensive Protection Effectiveness of Collaborative Detection Resources under Strong Confrontation Conditions

In response to the challenge of evaluating the comprehensive protection effectiveness of collaborative detection resources under strong adversarial conditions, this article first uses the comprehensive protection theory as a guide to analyze the evaluation index system, and uses a combination of Analytic Hierarchy Process (AHP) and improved grey target technology to assign weights to the evaluation index combination. Then, Combining the technique for order preference by similarity to ideal solution (TOPSIS) with improved grey target evaluation technology, Propose a comprehensive protection effectiveness evaluation model for collaborative detection resources based on improved TOPSIS. Finally, taking an example of the comprehensive protection effectiveness evaluation of collaborative detection resources under strong adversarial conditions, the effectiveness of the case was verified by comparing it with the TOPSIS evaluation model and the grey correlation evaluation model, and the scientific effect of the comprehensive protection effectiveness evaluation was achieved. CCS CONCEPTS • Computing methodologies∼Modeling and simulation∼Simulation evaluation•Theory of computation∼Design and analysis of algorithms∼Algorithm design techniques
InsectVR: Simulating Crawling Insects in Virtual Reality for Biology Edutainment

In this paper, we propose InsectVR, a Virtual Reality (VR) platform designed for biology edutainment that simulates crawling insects in realistic and immersive virtual environments. InsectVR offers a unique opportunity for educators to teach biology concepts related to insects and their behavior in an engaging way. InsectVR is designed to replicate the real-world behavior of crawling insects using a realistic mathematical model called the random walk algorithm. Through InsectVR, users can observe the movement of the insects and their behavior in immersive virtual environments which can help foster users’ understanding of these amazing creatures. InsectVR uses state-of-the-art VR technology to create a highly realistic insect world, which includes different types of insects and various environmental conditions. After being tested through a series of numerical experiments and preliminary user studies, InsectVR demonstrates the potential to revolutionize biology edutainment by providing users with immersive virtual experiences.
Towards Energy-Efficient Spiking Neural Networks: A Robust Hybrid CMOS-Memristive Accelerator

Spiking Neural Networks (SNNs) are energy-efficient artificial neural network models that can carry out data-intensive applications. Energy consumption, latency, and memory bottleneck are some of the major issues that arise in machine learning applications due to their data-demanding nature. Memristor-enabled Computing-In-Memory (CIM) architectures have been able to tackle the memory wall issue, eliminating the energy and time-consuming movement of data. In this work we develop a scalable CIM-based SNN architecture with our fabricated two-layer memristor crossbar array. In addition to having an enhanced heat dissipation capability, our memristor exhibits substantial enhancement of 10% to 66% in design area, power and latency compared to state-of-the-art memristors. This design incorporates an inter-spike interval (ISI) encoding scheme due to its high information density to convert the incoming input signals into spikes. Furthermore, we include a time-to-first-spike (TTFS) based output processing stage for its energy-efficiency to carry out the final classification. With the combination of ISI, CIM and TTFS, this network has a competitive inference speed of 2μs/image and can successfully classify handwritten digits with 2.9mW of power and 2.51pJ energy per spike. The proposed architecture with the ISI encoding scheme can achieve ∼10% higher accuracy than those of other encoding schemes in the MNIST dataset.
Assessing the evidence of ecosystem services studies: a framework and its application

Reliability of scientific findings is always important, but even more so in ecosystem service science, where results can directly lead to policy implementations. The assessment of reliability resulted in the introduction of the evidence-based concept in the medical sciences. It aims at identifying the best available information to answer the question of interest. In environmental sciences, the evidence-based concept is well developed [very few people actually use it! ] in conservation, but so far no guidelines exists for ecosystem services science. To apply the evidence-based concept in practice we need a scale to rank study designs commonly used in ecosystem services science as well as an assessment of the actual quality of implementation in a specific study. Here, we define a framework for evidence-based ecosystem service science, together with a design scale and a critical-appraisal checklist. The approach is described with detailed examples and specific suggestions for different user groups.
Creating an Intelligent Storage Plane for Next Generation Mobile Networks

With the demand for increased service level agreements for Beyond 5G and 6G networks, new ways to optimize the core network are being developed, including the use of artificial intelligence to monitor network and service functions for network automation purposes. However, the high data volume expected for these operations makes it necessary to also optimize the way the data itself is handled, to allow in-network AI applications to work at their full potential. In this paper, we describe a recently started Ph.D. research on the design of a telco data pipelining system for cellular traffic networks. We sketch preliminary ideas on the way data processing and load-balancing functions shall be distributed. We also elaborate on the applicability of existing software stacks to be possibly adopted and extended.
Network Kernel API with the Control Socket for Extending Transport Protocols

The transport layer performs a key role in satisfying various applications' requirements for communication over diversified links. However, middleboxes such as Network Address Translation (NAT) ossify transport protocols. Thus, transport protocols over UDP, such as QUIC, have been proposed. However, UDP-based transport protocols have difficulty in controlling network resources from the kernel because they are implemented in user space but do not have sufficient Application Programming Interface (API) to interact with the kernel. In this paper, we discuss the API between applications and the kernel for UDP-based transport protocols and propose the control socket for exchanging information with the kernel, in addition to the usual socket for data transmission. As an experiment, we implement a control agent running in user space and a Berkeley Packet Filter (BPF) program running in the kernel. We demonstrated that the user space program could set the UDP timestamp option of UDP Options proposed in the Internet Engineering Task Force (IETF) through the control socket. We also confirmed that user space programs could retrieve the round-trip time (RTT) of packets calculated based on the time stamp option in kernel space.
In search of lost time: tradition and identity in culinary narratives about old Hanoi

ABSTRACT This paper studies an emergent sociocultural phenomenon involving the construction of Hanoi’s old town as a signifier of taste, tradition and identity. Starting with the insights gotten from ethnographic observation and analysis, it then draws on local literary works to provide an account of narratives having been put together around the old Hanoi’s superior cuisine, prestigious tradition, and noble identity. In light of the Bourdieusian thesis of taste and social distinction supported by further theoretical developments from the perspective of post-modern culinary consumption, it wraps up by discussing the impacts of these narratives on the commodification of food and foodways in the old town and the construction of contemporary Hanoian identity. The results show that this phenomenon embodies a yearning for sociohistorical belonging, an appeal for identity status enhancement, and an elevation of cultural nationalism in post-reform Hanoi.
Fisher-Rao and pullback Hilbert cone distances on the multivariate Gaussian manifold with applications to simplification and quantization of mixtures

Data sets of multivariate normal distributions abound in many scientiﬁc areas like diffusion tensor medical imaging, structure tensor computer vision, radar signal processing, machine learning, etc. In order to process those data sets for downstream tasks like ﬁltering, classiﬁcation or clustering, one needs to deﬁne proper notions of dissimilarities and paths joining normal distributions. The Fisher-Rao distance deﬁned as the Riemannian geodesic distance induced by the Fisher information is such a principled distance which however is not known in closed-form excepts on a few particular cases. We ﬁrst report a fast and robust method to approximate arbitrarily ﬁnely the Fisher-Rao distance between normal distributions. Second, we introduce a distance based on a diffeomorphic embedding of the Gaussian manifold into a submanifold of the higher-dimensional symmetric positive-deﬁnite cone. We show that the projective Hilbert distance on the cone is a metric on the embedded Gaussian submanifold and pullback that distance with the straight line Hilbert cone geodesics to obtain a distance and paths between normal distributions. Compared to the Fisher-Rao distance approximation, the pullback Hilbert cone distance is computationally light since it requires to compute only extreme eigenvalues of matrices. Finally, we show how to use those distances in clustering tasks.
SPROUT: Authoring Programming Tutorials with Interactive Visualization of Large Language Model Generation Process

The rapid development of large language models (LLMs), such as ChatGPT, has revolutionized the efficiency of creating programming tutorials. LLMs can be instructed with text prompts to generate comprehensive text descriptions of code snippets. However, the lack of transparency in the end-to-end generation process has hindered the understanding of model behavior and limited user control over the generated results. To tackle this challenge, we introduce a novel approach that breaks down the programming tutorial creation task into actionable steps. By employing the tree-of-thought method, LLMs engage in an exploratory process to generate diverse and faithful programming tutorials. We then present SPROUT, an authoring tool equipped with a series of interactive visualizations that empower users to have greater control and understanding of the programming tutorial creation process. A formal user study demonstrated the effectiveness of SPROUT, showing that our tool assists users to actively participate in the programming tutorial creation process, leading to more reliable and customizable results. By providing users with greater control and understanding, SPROUT enhances the user experience and improves the overall quality of programming tutorial. A free copy of this paper and all supplemental materials are available at https://osf.io/uez2t/? view_only=5102e958802341daa414707646428f86.
Survival of magnetic correlations above ordering temperature in a ferromagnetically ordered classical kagom\'e magnet: Li9Cr3(P2O7)3(PO4)2

Motivated by the recent discovery of a semiclassical nematic spin liquid state in a Heisenberg kagom\'e antiferromagnet Li9Fe3(P2O7)3(PO4)2 (LFPO) with S=5/2 [Kermarrec et al. Phys. Rev. Lett. 127, 157202 (2021)], we now investigate the impact of spin quantum number S on the ground state properties by studying the isostructural kagom\'e magnet Li9Cr3(P2O7)3(PO4)2 (LCPO) with active t2g orbitals and S = 3/2. Thermodynamic measurements reveal that the ground state properties of LCPO is dominated by the ferromagnetic interactions with a mean-field temperature $\theta \sim$ 3 K (J<1 K) and the ordering temperature, Tc ~ 2.7 K, and the size of the ordered moment $\sim 1.05 \pm 0.25 $ ~${\mu}_B$ is significantly reduced from that of a fully ordered moment. The ab initio electronic structure calculations nicely corroborate the thermodynamic results and suggest the presence of additional in and out-of-plane further neighbor antiferromagnetic couplings, though significantly weaker in comparison to the dominant first-nearest neighbor ferromagnetic coupling. The spin-lattice relaxation rate measured with fields larger than the saturation field shows a magnetic field induced gap ($\Delta \propto B$) in the excitation spectrum, and in $B \rightarrow 0$ limit the gap has a finite intercept ~ 3 K, equivalent to the mean-field scale. We interpret the origin of this gap is associated with the magnetic interactions inherent to the material. With our experimental results, we establish the stabilization of a ferromagnetic like ground state and the persistence of magnetic-correlations above the ordering temperature in LCPO.
Unsupervised Anomaly Detection using Aggregated Normative Diffusion

Early detection of anomalies in medical images such as brain MRI is highly relevant for diagnosis and treatment of many conditions. Supervised machine learning methods are limited to a small number of pathologies where there is good availability of labeled data. In contrast, unsupervised anomaly detection (UAD) has the potential to identify a broader spectrum of anomalies by spotting deviations from normal patterns. Our research demonstrates that existing state-of-the-art UAD approaches do not generalise well to diverse types of anomalies in realistic multi-modal MR data. To overcome this, we introduce a new UAD method named Aggregated Normative Diffusion (ANDi). ANDi operates by aggregating differences between predicted denoising steps and ground truth backwards transitions in Denoising Diffusion Probabilistic Models (DDPMs) that have been trained on pyramidal Gaussian noise. We validate ANDi against three recent UAD baselines, and across three diverse brain MRI datasets. We show that ANDi, in some cases, substantially surpasses these baselines and shows increased robustness to varying types of anomalies. Particularly in detecting multiple sclerosis (MS) lesions, ANDi achieves improvements of up to 178% in terms of AUPRC.
Tetrahedra tiling problem

Kedlaya, Kolpakov, Poonen, and Rubinstein classified tetrahedra all of whose dihedral angles are rational multiples of $\pi$ into two one-parameter families (a Hill family and a new family) and $59$ sporadic tetrahedra. In this paper, we consider which of them tile space; we show that every member of the Hill family, exactly one member of the new family, and at most $40$ sporadic tetrahedra tile space. As a corollary, we disprove the converse of Debrunner's theorem, showing that not all Dehn invariant zero tetrahedra tile space.
Short-term Precipitation Forecasting in The Netherlands: An Application of Convolutional LSTM neural networks to weather radar data

This work addresses the challenge of short-term precipitation forecasting by applying Convolutional Long Short-Term Memory (ConvLSTM) neural networks to weather radar data from the Royal Netherlands Meteorological Institute (KNMI). The research exploits the combination of Convolutional Neural Networks (CNNs) layers for spatial pattern recognition and LSTM network layers for modelling temporal sequences, integrating these strengths into a ConvLSTM architecture. The model was trained and validated on weather radar data from the Netherlands. The model is an autoencoder consisting of nine layers, uniquely combining convolutional operations with LSTMs temporal processing, enabling it to capture the movement and intensity of precipitation systems. The training set comprised of sequences of radar images, with the model being tasked to predict precipitation patterns 1.5 hours ahead using the preceding data. Results indicate high accuracy in predicting the direction and intensity of precipitation movements. The findings of this study underscore the significant potential of ConvLSTM networks in meteorological forecasting, particularly in regions with complex weather patterns. It contributes to the field by offering a more accurate, data-driven approach to weather prediction, highlighting the broader applicability of ConvLSTM networks in meteorological tasks.
Earthquakes of peninsular India—a seismotectonic study

abstract Seismicity of peninsular India is studied from a detailed consideration of the historical as well as recent earthquake data, and a catalog of earthquakes from the earliest time through 1974 occurring within the region, 5°N-28°N; 67.5°E-90°E, is prepared. Separate epicenter maps showing instrumentally located earthquakes and the earthquakes located by using macroseismic observations are presented. A number of seismic zones, based largely on the distribution of earthquake epicenters but, also, partly on local geology and fault-plane solutions, are tentatively identified. Some of the zones of weakness may be related to the plume-generated triple- or four-armed rifts formed at different times in the geological past. Recent seismicity is related to the rejuvenation of activity along some of these zones of weakness. Fault-plane solutions for six earthquakes, widely separated geographically and related to different seismic zones, obtained by using teleseismic observations from the records of worldwide standardized stations network, show two consistent features. One of these features is that the axis of pressure, P , trends nearly in the north-south direction, and the other is that one of the nodal planes, in at least one possible solution for each event, strikes in a northnortheast to east direction and shows a left lateral sense of motion. The former observation indicates that the high stresses generated by the continental collision, caused by the northward movement of India, have affected the entire Indian peninsula. The latter observation suggests that the whole region may be under a state of left lateral shear. This shear is believed to be a consequence of the geometric disposition of the Indian and Eurasian continents at the time of collision and the subsequent plate kinematics. The latter observation also suggests that the orientation of the zones of weakness with respect to the ambient stress field may be an important factor in determining the faults along which the earthquakes are likely to occur.
TMSR: Tiny Multi-path CNNs for Super Resolution

We proposed a tiny multi-path CNN-based Super-Resolution (SR) method (TMSR) referring to tiny CNN-based SR methods under 5k parameters. The main contribution of the proposed method is the improved multi-path learning. The experimental results showed that TMSR obtained competitive image quality (peak-signal-to-noise ratio and Structural Similarity Index Measure) compared to the related works under 5k parameters.
Data-driven optimal control with neural network modeling of gradient flows

Extracting physical laws from observation data is a central challenge in many diverse areas of science and engineering. We propose Optimal Control Neural Networks (OCN) to learn the laws of vector fields in dynamical systems, with no assumption on their analytical form, given data consisting of sampled trajectories. The OCN framework consists of a neural network representation and an optimal control formulation. We provide error bounds for both the solution and the vector field. The bounds are shown to depend on both the training error and the time step between the observation data. We also demonstrate the effectiveness of OCN, as well as its generalization ability, by testing on several canonical systems, including the chaotic Lorenz system.
FreeMan: Towards Benchmarking 3D Human Pose Estimation under Real-World Conditions

Estimating the 3D structure of the human body from natural scenes is a fundamental aspect of visual perception. 3D human pose estimation is a vital step in advancing fields like AIGC and human-robot interaction, serving as a crucial technique for understanding and interacting with human actions in real-world settings. However, the current datasets, often collected under single laboratory conditions using complex motion capture equipment and unvarying backgrounds, are insufficient. The absence of datasets on variable conditions is stalling the progress of this crucial task. To facilitate the development of 3D pose estimation, we present FreeMan, the first large-scale, multi-view dataset collected under the real-world conditions. FreeMan was captured by synchronizing 8 smartphones across diverse scenarios. It comprises 11M frames from 8000 sequences, viewed from different perspectives. These sequences cover 40 subjects across 10 different scenarios, each with varying lighting conditions. We have also established an semi-automated pipeline containing error detection to reduce the workload of manual check and ensure precise annotation. We provide comprehensive evaluation baselines for a range of tasks, underlining the significant challenges posed by FreeMan. Further evaluations of standard indoor/outdoor human sensing datasets reveal that FreeMan offers robust representation transferability in real and complex scenes. Code and data will be available at https://wangjiongw.github. io/freeman.
SYZ mirror of Hirzebruch surface $\mathbb{F}_k$ and Morse homotopy

We study homological mirror symmetry for Hirzebruch surface $\mathbb{F}_k$ as a complex manifold by using the Strominger-Yau-Zaslow construction of mirror pair and Morse homotopy. For the toric Fano surfaces, Futaki-Kajiura and the author proved homological mirror symmetry by using Morse homotopy in arXiv:2008.13462, arXiv:2012.06801, and arXiv:2303.07851. In this paper, we extend Futaki-Kajiura's result of Hirzebruch surface $\mathbb{F}_1$ to $\mathbb{F}_k$. We discuss Morse homotopy and show homological mirror symmetry in the sense above holds true.
Koopman-based feedback design with stability guarantees

We present a method to design a state-feedback controller ensuring exponential stability for nonlinear systems using only measurement data. Our approach relies on Koopman operator theory and uses robust control to explicitly account for approximation errors due to finitely many data samples. To simplify practical usage across various applications, we provide a tutorial-style exposition of the feedback design and its stability guarantees for single-input systems. Moreover, we extend this controller design to multi-input systems and more flexible nonlinear state-feedback controllers using gain-scheduling techniques to increase the guaranteed region of attraction. As the proposed controller design is framed as a semidefinite program, it allows for an efficient solution. Finally, we validate the proposed feedback design procedure by means of numerical examples.
ImputeFormer : Graph Transformers for Generalizable Spatiotemporal Imputation

—This paper focuses on the multivariate time series imputation problem using deep neural architectures. The ubiquitous issue of missing data in both scientific and engineering tasks necessitates the development of an effective and general imputation model. Leveraging the wisdom and expertise garnered from low-rank imputation methods, we power the canonical Transformers with three key knowledge-driven enhancements, including projected temporal attention, global adaptive graph convolution, and Fourier imputation loss. These task-agnostic inductive biases exploit the inherent structures of incomplete time series, and thus make our model versatile for a variety of imputation problems. We demonstrate its superiority in terms of accuracy, efficiency, and flexibility on heterogeneous datasets, including traffic speed, traffic volume, solar energy, smart metering, and air quality. Comprehensive case studies are performed to further strengthen the interpretability. Promising empirical results provide strong conviction that incorporating time series primitives, such as low-rank properties, can substantially facilitate the development of a generalizable model to approach a wide range of spatiotemporal imputation problems.