Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of rotation on the multimessenger signatures of a hadron-quark phase transition in core-collapse supernovae

We study the impact of rotation on the multimessenger signals of core-collapse supernovae (CCSNe) with the occurrence of a first-order hadron-quark phase transition (HQPT). We simulate CCSNe with the \texttt{FLASH} code starting from a 20~$M_\odot$ progenitor with different rotation rates, and using the RDF equation of state from \textit{Bastian} 2021 that prescribes the HQPT. Rotation is found to delay the onset of the HQPT and the resulting dynamical collapse of the protocompact star (PCS) due to the centrifugal support. All models with the HQPT experience a second bounce shock which leads to a successful explosion. The oblate PCS as deformed by rotation gives rise to strong gravitational-wave (GW) emission around the second bounce with a peak amplitude larger by a factor of $\sim10$ than that around the first bounce. The breakout of the second bounce shock at the neutrinosphere produces a $\bar{\nu}_e$-rich neutrino burst with a luminosity of serveral 10$^{53}$~erg~s$^{-1}$. In rapidly rotating models the PCS pulsation following the second bounce generates oscillations in the neutrino signal after the burst. In the fastest rotating model with the HQPT, a clear correlation is found between the oscillations in the GW and neutrino signals immediately after the second bounce. In addition, the HQPT-induced collapse leads to a jump in the ratio of rotational kinetic energy to gravitational energy ($\beta$) of the PCS, for which persistent GW emission may arise due to secular nonaxisymmetric instabilities.
Evaluating the Effects of Reducing Voltage Margins for Energy-Efficient Operation of MPSoCs

Voltage margins, or guardbands, are imposed on DVFS systems to account for process, voltage, and temperature variability effects. While necessary to assure correctness, guardbands reduce energy efficiency, a crucial requirement for embedded systems. The literature shows that error detection techniques can be used to maintain the system’s reliability while reducing or eliminating the guardbands. This letter assesses the practically available margins of a commercial RISC-V MPSoC while violating its guardband limits. The primary motivation of this work is to support the development of an efficient system leveraging the redundancy of multicore architectures for an error detection and correction scheme capable of mitigating the errors caused by aggressive voltage margin reduction. For an equivalent performance, we achieved up to 27% energy reduction while violating the manufacturer’s defined guardband, leaving reasonable energy margins for further development.
Learning to Learn with Generative Models of Neural Network Checkpoints

We explore a data-driven approach for learning to optimize neural networks. We construct a dataset of neural network checkpoints and train a generative model on the parameters. In particular, our model is a conditional diffusion transformer that, given an initial input parameter vector and a prompted loss, error, or return, predicts the distribution over parameter updates that achieve the desired metric. At test time, it can optimize neural networks with unseen parameters for downstream tasks in just one update. We find that our approach successfully generates parameters for a wide range of loss prompts. Moreover, it can sample multimodal parameter solutions and has favorable scaling properties. We apply our method to different neural network architectures and tasks in supervised and reinforcement learning.
Cognitive Architecture for Co-Evolutionary Hybrid Intelligence

This paper questions the feasibility of a strong (general) data-centric artificial intelligence (AI). The disadvantages of this type of intelligence are discussed. As an alternative, the concept of co-evolutionary hybrid intelligence is proposed. It is based on the cognitive interoperability of man and machine. An analysis of existing approaches to the construction of cognitive architectures is given. An architecture seamlessly incorporates a human into the loop of intelligent problem solving is considered. The article is organized as follows. The first part contains a critique of data-centric intelligent systems. The reasons why it is impossible to create a strong artificial intelligence based on this type of intelligence are indicated. The second part briefly presents the concept of co-evolutionary hybrid intelligence and shows its advantages. The third part gives an overview and analysis of existing cognitive architectures. It is concluded that many do not consider humans part of the intelligent data processing process. The next part discusses the cognitive architecture for co-evolutionary hybrid intelligence, providing integration with humans. It finishes with general conclusions about the feasibility of developing intelligent systems with humans in the problem-solving loop.
SPRITZ-1.5C: Employing Deep Ensemble Learning for Improving the Security of Computer Networks against Adversarial Attacks

—In the past few years, Convolutional Neural Networks (CNN) have demonstrated promising performance in various real-world cybersecurity applications, such as network and multimedia security. However, the underlying fragility of CNN structures poses major security problems, making them inappropriate for use in security-oriented applications including such computer networks. Protecting these architectures from adversarial attacks necessitates using security-wise architectures that are challenging to attack. In this study, we present a novel architecture based on an ensemble classiﬁer that combines the enhanced security of 1-Class classiﬁcation (known as 1C) with the high performance of conventional 2-Class classiﬁcation (known as 2C) in the absence of attacks.Our architecture is referred to as the 1.5-Class (SPRITZ-1.5C) classiﬁer and constructed using a ﬁnal dense classiﬁer, one 2C classiﬁer (i.e., CNNs), and two parallel 1C classiﬁers (i.e., auto-encoders). In our experiments, we evaluated the robustness of our proposed architecture by considering eight possible adversarial attacks in various scenarios. We performed these attacks on the 2C and SPRITZ-1.5C architectures separately. The experimental results of our study showed that the Attack Success Rate (ASR) of the I-FGSM attack against a 2C classiﬁer trained with the N-BaIoT dataset is 0.9900. In contrast, the ASR is 0.0000 for the SPRITZ-1.5C classiﬁer.
Overcoming Referential Ambiguity in Language-Guided Goal-Conditioned Reinforcement Learning

Teaching an agent to perform new tasks using natural language can easily be hindered by ambiguities in interpretation. When a teacher provides an instruction to a learner about an object by referring to its features, the learner can misunderstand the teacher's intentions, for instance if the instruction ambiguously refer to features of the object, a phenomenon called referential ambiguity. We study how two concepts derived from cognitive sciences can help resolve those referential ambiguities: pedagogy (selecting the right instructions) and pragmatism (learning the preferences of the other agents using inductive reasoning). We apply those ideas to a teacher/learner setup with two artificial agents on a simulated robotic task (block-stacking). We show that these concepts improve sample efficiency for training the learner.
Delayed Geometric Discounts: An Alternative Criterion for Reinforcement Learning

of autonomous capable of complex theoretical background to optimal In on geometric discounts to evaluate this optimality. processes where future returns are not less valuable. Depending the sample-inefﬁciency are decayed) mecha-nisms (to deal with sparse, deceptive or adversarial rewards). In this paper, we tackle these issues by generalizing the discounted problem formulation with a family of delayed objective functions. We investigate the underlying RL problem to derive: 1) the optimal stationary solution and 2) an approximation of the optimal non-stationary control. The devised algorithms solved hard exploration problems on tabular environment and improved sample-efﬁciency on classic simulated robotics benchmarks.
D$^{\bf{3}}$: Duplicate Detection Decontaminator for Multi-Athlete Tracking in Sports Videos

Tracking multiple athletes in sports videos is a very challenging Multi-Object Tracking (MOT) task, since athletes often have the same appearance and are intimately covered with each other, making a common occlusion problem becomes an abhorrent duplicate detection. In this paper, the duplicate detection is newly and precisely defined as occlusion misreporting on the same athlete by multiple detection boxes in one frame. To address this problem, we meticulously design a novel transformer-based Duplicate Detection Decontaminator (D$^3$) for training, and a specific algorithm Rally-Hungarian (RH) for matching. Once duplicate detection occurs, D$^3$ immediately modifies the procedure by generating enhanced boxes losses. RH, triggered by the team sports substitution rules, is exceedingly suitable for sports videos. Moreover, to complement the tracking dataset that without shot changes, we release a new dataset based on sports video named RallyTrack. Extensive experiments on RallyTrack show that combining D$^3$ and RH can dramatically improve the tracking performance with 9.2 in MOTA and 4.5 in HOTA. Meanwhile, experiments on MOT-series and DanceTrack discover that D$^3$ can accelerate convergence during training, especially save up to 80 percent of the original training time on MOT17. Finally, our model, which is trained only with volleyball videos, can be applied directly to basketball and soccer videos for MAT, which shows priority of our method. Our dataset is available at https://github.com/heruihr/rallytrack.
Hand Hygiene Assessment via Joint Step Segmentation and Key Action Scorer

Hand hygiene is a standard six-step hand-washing action proposed by the World Health Organization (WHO). However, there is no good way to supervise medical staff to do hand hygiene, which brings the potential risk of disease spread. Existing action assessment works usually make an overall quality prediction on an entire video. However, the internal structures of hand hygiene action are important in hand hygiene assessment. Therefore, we propose a novel fine-grained learning framework to perform step segmentation and key action scorer in a joint manner for accurate hand hygiene assessment. Existing temporal segmentation methods usually employ multi-stage convolutional network to improve the segmentation robustness, but easily lead to over-segmentation due to the lack of the long-range dependence. To address this issue, we design a multi-stage convolution-transformer network for step segmentation. Based on the observation that each hand-washing step involves several key actions which determine the hand-washing quality, we design a set of key action scorers to evaluate the quality of key actions in each step. In addition, there lacks a unified dataset in hand hygiene assessment. Therefore, under the supervision of medical staff, we contribute a video dataset that contains 300 video sequences with fine-grained annotations. Extensive experiments on the dataset suggest that our method well assesses hand hygiene videos and achieves outstanding performance.
3D Reconstruction using Structured Light from off-the-shelf components

The coordinate measuring machine(CMM) has been the benchmark of accuracy in measuring solid objects from nearly past 50 years or more. However with the advent of 3D scanning technology, the accuracy and the density of point cloud generated has taken over. In this project we not only compare the different algorithms that can be used in a 3D scanning software, but also create our own 3D scanner from off-the-shelf components like camera and projector. Our objective has been : 1. To develop a prototype for 3D scanner to achieve a system that performs at optimal accuracy over a wide typology of objects. 2. To minimise the cost using off-the-shelf components. 3. To reach very close to the accuracy of CMM.
Safe Real-World Reinforcement Learning for Mobile Agent Obstacle Avoidance

—Collision avoidance is key for mobile robots and agents to operate safely in the real world. In this work, we present an efﬁcient and effective collision avoidance system that combines real-world reinforcement learning (RL), search-based online trajectory planning, and automatic emergency intervention, e.g. automatic emergency braking (AEB). The goal of the RL is to learn effective search heuristics that speed up the search for collision-free trajectory and reduce the frequency of triggering automatic emergency interventions. This novel setup enables RL to learn safely and directly on mobile robots in a real-world indoor environment, minimizing actual crashes even during training. Our real-world experiments show that, when compared with several baselines, our approach enjoys a higher average speed, lower crash rate, higher goals reached rate, smaller computation overhead, and smoother overall control.
Real-time Anomaly Detection for Multivariate Data Streams

We present a real-time multivariate anomaly detection algorithm for data streams based on the Probabilistic Exponentially Weighted Moving Average (PEWMA). Our formulation is resilient to (abrupt transient, abrupt distributional, and gradual distributional) shifts in the data. The novel anomaly detection routines utilize an incremental online algorithm to handle streams. Furthermore, our proposed anomaly detection algorithm works in an unsupervised manner eliminating the need for labeled examples. Our algorithm performs well and is resilient in the face of concept drifts.
Cooperative Sensing and Heterogeneous Information Fusion in VCPS: A Multi-agent Deep Reinforcement Learning Approach

Cooperative sensing and heterogeneous information fusion are critical to realize vehicular cyber-physical systems (VCPSs). This paper makes the first attempt to quantitatively measure the quality of VCPS by designing a new metric called Age of View (AoV). Specifically, we first present the system architecture where heterogeneous information can be cooperatively sensed and uploaded via vehicle-to-infrastructure (V2I) communications in vehicular edge computing (VEC). Logical views are constructed by fusing the heterogeneous information at edge nodes. Further, we formulate the problem by deriving a cooperative sensing model based on the multi-class M/G/1 priority queue, and defining the AoV by modeling the timeliness, completeness and consistency of the logical views. On this basis, a multi-agent deep reinforcement learning solution is proposed. In particular, the system state includes vehicle sensed information, edge cached information and view requirements. The vehicle action space consists of the sensing frequencies and uploading priorities of information. A difference-reward-based credit assignment is designed to divide the system reward, which is defined as the VCPS quality, into the difference reward for vehicles. Edge node allocates V2I bandwidth to vehicles based on predicted vehicle trajectories and view requirements. Finally, we build the simulation model and give a comprehensive performance evaluation, which conclusively demonstrates the superiority of the proposed solution.
A simple computation of $\zeta(2k)$ by using Bernoulli polynomials and a telescoping series

We present a new proof of Euler's formulas for $\zeta(2k)$, where $k = 1,2,3,. $, which uses only the defining properties of the Bernoulli polynomials, obtaining the value of $\zeta(2k)$ by summing a telescoping series. Only basic techniques from Calculus are needed to carry out the computation. The method also applies to $\zeta(2k+1)$ and the harmonic numbers, yielding integral formulas for these.
An Axiomatic Theory of Subject Matter Structure.

Abstract : The report can be viewed as a first step toward development of a formal theory of instruction. An axiomatic theory of subject matter structure was formulated, including both content and task components. The subject-matter content is analyzed in terms of constituents and their relations. Underlying the content structure is the task structure that is related to the content position via coordinating relations. The notion of dependency is introduced and investigated. Dependency leads to precedence in an instructional sense and this notion is also discussed. Formal procedures are developed that can be utilized to formulate the complete dependency relationship for particular subject matters. A comprehensive detailed example is developed that serves as a vehicle to illustrate the theoretical concepts, results, and procedures. (Author)
Euclidean quantum gravity and stochastic approach: Physical reality of complex-valued instantons

In this talk, we compare two states: the stationary state in stochastic inflation and the ground state wave function of the universe. We already know that, for the potential with a static field, two pictures give the same probability distribution. Here, we go beyond this limit and assert that two pictures indeed have deeper relations. We illustrate a simple example so that there is a corresponding instanton if a certain field value has a non-zero probability in the statistical side. This instanton should be complex-valued. Furthermore, the compact manifold in the Euclidean side can be interpreted as a coarse-graining grid size in the stochastic universe. Finally, we summarize the recent status and possible applications.
Spontaneous symmetry emergence as a source of new class of phase transitions

Spontaneous symmetry breaking in systems with symmetry is core-stone phenomenon accompanying second-order phase transitions. Here, we predict an opposite phenomenon, namely, spontaneous symmetry emergence in a system without symmetry. On the example of two coupled oscillators interacting non-symmetrically with a set of oscillators whose frequencies uniformly fill a finite frequency range, we demonstrate that the system state can acquire symmetry, which is not inherent to the system Hamiltonian. The symmetry emergence manifests in the change of the system dynamics, which can be interpreted as a new class of phase transitions.
A heterogeneous group CNN for image super-resolution

Convolutional neural networks (CNNs) have obtained remarkable performance via deep architectures. However, these CNNs often achieve poor robustness for image super-resolution (SR) under complex scenes. In this article, we present a heterogeneous group SR CNN (HGSRCNN) via leveraging structure information of different types to obtain a high-quality image. Specifically, each heterogeneous group block (HGB) of HGSRCNN uses a heterogeneous architecture containing a symmetric group convolutional block and a complementary convolutional block in a parallel way to enhance the internal and external relations of different channels for facilitating richer low-frequency structure information of different types. To prevent the appearance of obtained redundant features, a refinement block (RB) with signal enhancements in a serial way is designed to filter useless information. To prevent the loss of original information, a multilevel enhancement mechanism guides a CNN to achieve a symmetric architecture for promoting expressive ability of HGSRCNN. Besides, a parallel upsampling mechanism is developed to train a blind SR model. Extensive experiments illustrate that the proposed HGSRCNN has obtained excellent SR performance in terms of both quantitative and qualitative analysis. Codes can be accessed at https://github.com/hellloxiaotian/HGSRCNN.
Evolutionary dynamics of sustainable blockchains

The energy sustainability of blockchains, whose consensus protocol rests on the Proof-of-Work, nourishes a heated debate. The underlying issue lies in a highly energy-consuming process, defined as mining, required to validate crypto-asset transactions. Mining is the process of solving a cryptographic puzzle, incentivized by the possibility of gaining a reward. The higher the number of users performing mining, i.e. miners, the higher the overall electricity consumption of a blockchain. For that reason, mining constitutes a negative environmental externality. Here, we study whether miners’ interests can meet the collective need to curb energy consumption. To this end, we introduce the Crypto-Asset Game, namely a model based on the framework of Evolutionary Game Theory devised for studying the dynamics of a population whose agents can play as crypto-asset users or as miners. The proposed model, studied via numerical simulations, reveals a rich spectrum of possible steady states. Interestingly, by setting the miners’ reward in the function of the population size, agents reach a strategy profile that optimizes global energy consumption. To conclude, can a Proof-of-Work-based blockchain become energetically sustainable? Our results suggest that blockchain protocol parameters could have a relevant role in the global energy consumption of this technology.
Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps

Multi-agent collaborative perception could significantly upgrade the perception performance by enabling agents to share complementary information with each other through communication. It inevitably results in a fundamental trade-off between perception performance and communication bandwidth. To tackle this bottleneck issue, we propose a spatial confidence map, which reflects the spatial heterogeneity of perceptual information. It empowers agents to only share spatially sparse, yet perceptually critical information, contributing to where to communicate. Based on this novel spatial confidence map, we propose Where2comm, a communication-efficient collaborative perception framework. Where2comm has two distinct advantages: i) it considers pragmatic compression and uses less communication to achieve higher perception performance by focusing on perceptually critical areas; and ii) it can handle varying communication bandwidth by dynamically adjusting spatial areas involved in communication. To evaluate Where2comm, we consider 3D object detection in both real-world and simulation scenarios with two modalities (camera/LiDAR) and two agent types (cars/drones) on four datasets: OPV2V, V2X-Sim, DAIR-V2X, and our original CoPerception-UAVs. Where2comm consistently outperforms previous methods; for example, it achieves more than $100,000 \times$ lower communication volume and still outperforms DiscoNet and V2X-ViT on OPV2V. Our code is available at https://github.com/MediaBrain-SJTU/where2comm.
An Explainable Machine Learning Approach to Visual-Interactive Labeling: A Case Study on Non-communicable Disease Data

We introduce a new visual-interactive tool: Explainable Labeling Assistant ( XLabel ) that takes an explainable machine learning approach to data labeling. The main component of XLabel is the Explainable Boosting Machine (EBM), a predictive model that can calculate the contribution of each input feature towards the ﬁnal prediction. As a case study, we use XLabel to predict the labels of four non-communicable diseases (NCDs): diabetes, hypertension, chronic kidney disease, and dyslipidemia. We demonstrate that EBM is an excellent choice of predictive model by comparing it against a rule-based and four other machine learning models. By performing 5-fold cross-validation on 427 medical records, EBM ’s prediction accuracy, precision, and F1-score are greater than 0 . 95 in all four NCDs. It performed as well as two black-box models and outperformed the other models in these metrics. In an additional experiment, when 40% of the records were intentionally mislabeled, EBM could recall the correct labels of more than 90% of these records. The detailed code, documentation and installation instructions of XLabel have been made available at: https://github.com/donlapark/XLabel .