Miracle-dz/UniFAR-FTUs huggingface dataset:
Tensor renormalization group approach to (
1+1
)-dimensional SU(2) principal chiral model at finite density

We apply the tensor renormalization group method to the (1+1)-dimensional SU(2) principal chiral model at finite chemical potential with the use of the Gauss-Legendre quadrature to discretize the SU(2) Lie group. The internal energy at vanishing chemical potential $\mu=0$ shows good consistency with the prediction of the strong and weak coupling expansions. This indicates an effectiveness of the Gauss-Legendre quadrature for the partitioning of the SU(2) Lie group. In the finite density region with $\mu\ne 0$ at the strong coupling we observe the Silver-Blaze phenomenon for the number density.
Boundary-Aware Network for Abdominal Multi-Organ Segmentation

Automated abdominal multi-organ segmentation is a crucial yet challenging task in the computer-aided diagnosis of abdominal organ-related diseases. Although numerous deep learning models have achieved remarkable success in many medical image segmentation tasks, accurate segmentation of abdominal organs remains challenging, due to the varying sizes of abdominal organs and the ambiguous boundaries among them. In this paper, we propose a boundary-aware network (BA-Net) to segment abdominal organs on CT scans and MRI scans. This model contains a shared encoder, a boundary decoder, and a segmentation decoder. The multi-scale deep supervision strategy is adopted on both decoders, which can alleviate the issues caused by variable organ sizes. The boundary probability maps produced by the boundary decoder at each scale are used as attention to enhance the segmentation feature maps. We evaluated the BA-Net on the Abdominal Multi-Organ Segmentation (AMOS) Challenge dataset and achieved an average Dice score of 89.29 % for multi-organ segmentation on CT scans and an average Dice score of 71.92 % on MRI scans. The results demonstrate that BA-Net is superior to nnUNet on both segmentation tasks.
Towards Boosting the Open-Domain Chatbot with Human Feedback

Many open-domain dialogue models pre-trained with social media comments can generate coherent replies but have difficulties producing engaging responses. This phenomenon might mainly result from the deficiency of annotated human-human conversations and the misalignment with human preference. In this paper, we propose a novel and efficient framework Diamante to boost the open-domain chatbot, where two kinds of human feedback (including explicit demonstration and implicit preference) are collected and leveraged. By asking annotators to select or amend the model-generated candidate responses, Diamante efficiently collects the human demonstrated responses and constructs a Chinese chit-chat dataset. To enhance the alignment with human preference, Diamante leverages the implicit preference in the data collection process and introduces the generation-evaluation joint training. Comprehensive experiments indicate that the Diamante dataset and joint training paradigm can significantly boost the performance of pre-trained dialogue models. The overall engagingness of the previous state-of-the-art model has been improved remarkably by 50% in Chinese open-domain conversations.
Expert Opinion Elicitation for Assisting Deep Learning based Lyme Disease Classifier with Patient Data

Diagnosing erythema migrans (EM) skin lesion, the most common early symptom of Lyme disease using deep learning techniques can be effective to prevent long-term complications. Existing works on deep learning based EM recognition only utilizes lesion image due to the lack of a dataset of Lyme disease related images with associated patient data. Physicians rely on patient information about the background of the skin lesion to confirm their diagnosis. In order to assist the deep learning model with a probability score calculated from patient data, this study elicited opinion from fifteen doctors. For the elicitation process, a questionnaire with questions and possible answers related to EM was prepared. Doctors provided relative weights to different answers to the questions. We converted doctors evaluations to probability scores using Gaussian mixture based density estimation. For elicited probability model validation, we exploited formal concept analysis and decision tree. The elicited probability scores can be utilized to make image based deep learning Lyme disease pre-scanners robust.
Quantum scrambling in a toy model of photodetectors

Quantum measurement is a process that involves the interaction between a quantum system and a macroscopic measurement apparatus containing many degrees of freedom. The photodetector is such an apparatus with many electrons interacting with the incoming quantum photon. Therefore the incoming photon will spread and get scrambled in the photodetector, that is, the operator of the initial incoming local photons will grow and becomes highly non-local through the interaction process. Investigating this scrambling process in detail is useful for understanding the interaction between the quantum system and the measurement apparatus. In this paper, we study the quantum scrambling process in an effective toy model of photodetectors in three different physical scenarios, by numerically simulating the evolution of the out-of-time correlators (OTOC). In particular, the integrability of the effective model is explored through level spacing statistics, and the effect of the spatially/temporarily distributed disorders on the system evolution is carefully investigated using the OTOC. Looking into these detailed dynamical processes paves the way to the quantum simulation and manipulation of photondetector, which would provide insights into understanding the wave-function collapse processes.
Spacecraft Depth Completion Based on the Gray Image and the Sparse Depth Map

Perceiving the 3D structure of the spacecraft is a prerequisite for successfully executing many on-orbit space missions, and it can provide critical input for many downstream vision algorithms. In this paper, we propose to sense the 3D structure of spacecraft using light detection and ranging sensor (LIDAR) and a monocular camera. To this end, Spacecraft Depth Completion Network (SDCNet) is proposed to recover the dense depth map based on gray image and sparse depth map. Specifically, SDCNet decomposes the spacecraft depth completion task into foreground segmentation subtask and foreground depth completion subtask, which segments the spacecraft region first and then performs depth completion on the segmented foreground area. In this way, the background interference to foreground spacecraft depth completion is effectively avoided. Moreover, an attention-based feature fusion module is also proposed to aggregate the complementary information between different inputs, which deduces the correlation between different features along the channel and the spatial dimension sequentially. Besides, four metrics are also proposed to evaluate object-level depth completion performance. Finally, a large-scale satellite depth completion dataset is constructed for training and testing spacecraft depth completion algorithms. Empirical experiments on the dataset demonstrate the effectiveness of the proposed SDCNet, which achieves 0.225 m mean absolute error of interest and 0.778 m mean absolute truncation error, surpassing state-of-the-art methods by a large margin. The pose estimation experiment is also conducted based on the depth completion results, and the experimental results indicate that the predicted dense depth map could meet the needs of downstream vision tasks.
Opportunistic Search for Continuous Gravitational Waves from Compact Objects in Long-period Binaries

Most all-sky searches for continuous gravitational waves assume the source to be isolated. In this paper, we allow for an unknown companion object in a long-period orbit and opportunistically use previous results from an all-sky search for isolated sources to constrain the continuous gravitational-wave amplitude over a large and unexplored range of binary orbital parameters without explicitly performing a dedicated search for binary systems. The resulting limits are significantly more constraining than any existing upper limits for unknown binary systems, albeit the latter apply to different orbital parameter ranges that are computationally much costlier to explore.
Generalizing the Affine Framework to HJM and Random Field Models

We identify a class of term structure models possessing a generalized affine-structure that significantly extends the class studied by Duffie, Pan, and Singleton (2000) and Chacko and Das (2002). This class of models, which includes both infinite-state-variable (i.e., HJM-type) and infinite-factor (random field) models, possesses analytic solutions for the characteristic function, which in turn provides closed-form solutions for many types of fixed income derivatives. In addition, the generalized affine framework provides analytic solutions to the optimal portfolio choice problem. In a random field setting, the optimal portfolio decision is unique, in turn providing a justification for 'preferred habitat' theories.
Emission Reduction and Energy Performance Improvement with Different Regional Treatment Intensity in China

China’s industrial sector, which has a significant position in the world, is the main sector of China’s energy consumption and waste gas emission. China’s government has promulgated a Guiding Opinion, setting key regions to establish an emission reduction target of air pollutants during the 12th five year plan (2011–2015). Thus, there is a different regional treatment of industrial waste gas in China. This study considers the waste gas treatment expenditure as a new input and employs the non-radial directional distance function in the framework of the meta-frontier model to investigate the energy and emission reduction performance of China’s industrial sectors. The study is aimed at finding a significant and expanded technical gap between key and non-key regions in the energy and emission reduction efficiencies. The empirical result presents an effective method to improve the performance by increasing the emission treatment expenditure to reduce emissions.
van der Waals coefficients for alkali-metal atoms in material media

The damping coefficients for the alkali atoms are determined very accurately by taking into account the optical properties of the atoms and three distinct types of trapping materials such as Au (metal), Si (semi-conductor) and vitreous SiO2 (dielectric). Dynamic dipole polarizabilities are calculated precisely for the alkali atoms that reproduce the damping coefficients in the perfect conducting medium within 0.2% accuracy. Upon the consideration of the available optical data of the above wall materials, the damping coefficients are found to be substantially different than those of the ideal conductor. We also evaluated dispersion coefficients for the alkali dimers and compared them with the previously reported values. These coefficients are fitted into a ready-to-use functional form to aid the experimentalists the interaction potentials only with the knowledge of distances.
Sim-to-Real Transfer of Robotic Assembly with Visual Inputs Using CycleGAN and Force Control

Recently, deep reinforcement learning (RL) has shown some impressive successes in robotic manipulation applications. However, training robots in the real world is nontrivial owing to sample efficiency and safety concerns. Sim-to-real transfer is proposed to address the aforementioned concerns but introduces a new issue called the reality gap. In this work, we introduce a sim-to-real learning framework for vision-based assembly tasks and perform training in a simulated environment by employing inputs from a single camera to address the aforementioned issues. We present a domain adaptation method based on cycle-consistent generative adversarial networks (CycleGAN) and a force control transfer approach to bridge the reality gap. We demonstrate that the proposed framework trained in a simulated environment can be successfully transferred to a real peg-in-hole setup.
Towards Adversarial Purification using Denoising AutoEncoders

With the rapid advancement and increased use of deep learning models in image identification, security becomes a major concern to their deployment in safety-critical systems. Since the accuracy and robustness of deep learning models are primarily attributed from the purity of the training samples, therefore the deep learning architectures are often susceptible to adversarial attacks. Adversarial attacks are often obtained by making subtle perturbations to normal images, which are mostly imperceptible to humans, but can seriously confuse the state-of-the-art machine learning models. We propose a framework, named APuDAE, leveraging Denoising AutoEncoders (DAEs) to purify these samples by using them in an adaptive way and thus improve the classification accuracy of the target classifier networks that have been attacked. We also show how using DAEs adaptively instead of using them directly, improves classification accuracy further and is more robust to the possibility of designing adaptive attacks to fool them. We demonstrate our results over MNIST, CIFAR-10, ImageNet dataset and show how our framework (APuDAE) provides comparable and in most cases better performance to the baseline methods in purifying adversaries. We also design adaptive attack specifically designed to attack our purifying model and demonstrate how our defense is robust to that.
RAGUEL: Recourse-Aware Group Unfairness Elimination

While machine learning and ranking-based systems are in widespread use for sensitive decision-making processes (e.g., determining job candidates, assigning credit scores), they are rife with concerns over unintended biases in their outcomes, which makes algorithmic fairness (e.g., demographic parity, equal opportunity) an objective of interest. 'Algorithmic recourse' offers feasible recovery actions to change unwanted outcomes through the modification of attributes. We introduce the notion of ranked group-level recourse fairness, and develop a 'recourse-aware ranking' solution that satisfies ranked recourse fairness constraints while minimizing the cost of suggested modifications. Our solution suggests interventions that can reorder the ranked list of database records and mitigate group-level unfairness; specifically, disproportionate representation of sub-groups and recourse cost imbalance. This re-ranking identifies the minimum modifications to data points, with these attribute modifications weighted according to their ease of recourse. We then present an efficient block-based extension that enables re-ranking at any granularity (e.g., multiple brackets of bank loan interest rates, multiple pages of search engine results). Evaluation on real datasets shows that, while existing methods may even exacerbate recourse unfairness, our solution – RAGUEL – significantly improves recourse-aware fairness. RAGUEL outperforms alternatives at improving recourse fairness, through a combined process of counterfactual generation and re-ranking, whilst remaining efficient for large-scale datasets.
Survey on Architectural Attacks: A Unified Classification and Attack Model

According to the World Economic Forum, cyberattacks are considered as one of the most important sources of risk to companies and institutions worldwide. Attacks can target the network, software, and/or hardware. Over the years, much knowledge has been developed to understand and mitigate cyberattacks. However, new threats have appeared in recent years regarding software attacks that exploit hardware vulnerabilities. This article defines these attacks as architectural attacks. Today, both industry and academia have only limited comprehension of architectural attacks, which represents a critical issue for the design of future systems. To this end, this work proposes a new taxonomy, a new attack model, and a complete survey of existing architectural attacks. As a result, it provides the tools to understand architectural attacks in more depth and to start building improved designs and protection mechanisms.
Magnetoelectric Multiferroic Janus Monolayers VOXY (X/Y = F, Cl, Br, or I, and X ≠ Y) with In-Plane Ferroelectricity and Out-of-Plane Piezoelectricity

Multifunctional two-dimensional (2D) multiferroics are promising materials for designing low-dimensional multipurpose devices. The key to multifunctionality in these materials is breaking the space-inversion and the time-reversal symmetry, which results in spontaneous electric polarization and magnetization in the same phase. A new class of 2D materials, Janus 2D materials, has emerged, which works on a similar principle of breaking out-of-plane symmetry to invoke new exciting functionalities in the 2D materials, such as an out-of-plane piezoelectric polarization. In this work, a new group of 2D multiferroic Janus monolayers VOXY (X/Y = F, Cl, Br, or I, and X$\not=$Y) is derived by breaking the out-of-plane symmetry in the parent multiferroics VOX$_2$ (X = F, Cl, Br, or I). The structural, magnetic, and ferroelectric properties of multiferroics VOX$_2$ are compared with their Janus derivatives. We calculated in-plane and out-of-plane piezoelectric polarization for VOX$_2$ and VOXY series, where VOFCl, VOFBr, VOFI, and VOClI are found to have significant out-of-plane piezoelectric polarization. Our theoretical work predicts a new series of 2D multiferroic materials and encourages their further investigation for high-performance nanoelectronic devices.
Similarity-based Link Prediction from Modular Compression of Network Flows

Node similarity scores are a foundation for machine learning in graphs for clustering, node classification, anomaly detection, and link prediction with applications in biological systems, information networks, and recommender systems. Recent works on link prediction use vector space embeddings to calculate node similarities in undirected networks with good performance. Still, they have several disadvantages: limited interpretability, need for hyperparameter tuning, manual model fitting through dimensionality reduction, and poor performance from symmetric similarities in directed link prediction. We propose MapSim, an information-theoretic measure to assess node similarities based on modular compression of network flows. Unlike vector space embeddings, MapSim represents nodes in a discrete, non-metric space of communities and yields asymmetric similarities in an unsupervised fashion. We compare MapSim on a link prediction task to popular embedding-based algorithms across 47 networks and find that MapSim's average performance across all networks is more than 7% higher than its closest competitor, outperforming all embedding methods in 11 of the 47 networks. Our method demonstrates the potential of compression-based approaches in graph representation learning, with promising applications in other graph learning tasks.
Understanding intra-day price formation process by agent-based financial market simulation: calibrating the extended chiarella model

This article presents XGB-Chiarella, a powerful new approach for deploying agent-based models to generate realistic intra-day artificial financial price data. This approach is based on agent-based models, calibrated by XGBoost machine learning surrogate. Following the Extended Chiarella model, three types of trading agents are introduced in this agent-based model: fundamental traders, momentum traders, and noise traders. In particular, XGB-Chiarella focuses on configuring the simulation to accurately reflect real market behaviours. Instead of using the original Expectation-Maximisation algorithm for parameter estimation, the agent-based Extended Chiarella model is calibrated using XGBoost machine learning surrogate. It is shown that the machine learning surrogate learned in the proposed method is an accurate proxy of the true agent-based market simulation. The proposed calibration method is superior to the original Expectation-Maximisation parameter estimation in terms of the distance between historical and simulated stylised facts. With the same underlying model, the proposed methodology is capable of generating realistic price time series in various stocks listed at three different exchanges, which indicates the universality of intra-day price formation process. For the time scale (minutes) chosen in this paper, one agent per category is shown to be sufficient to capture the intra-day price formation process. The proposed XGB-Chiarella approach provides insights that the price formation process is comprised of the interactions between momentum traders, fundamental traders, and noise traders. It can also be used to enhance risk management by practitioners.
Revealing Double White Dwarf Mergers with Multi-messenger Signals

A significant number of double white dwarfs (DWDs) are believed to merge within the Hubble time due to the gravitational wave (GW) emission during their inspiraling phase. The outcome of a DWD system is either a type Ia Supernova as the double-degenerate model, or a massive, long-lasting merger remnant. Expected multi-messenger signals of these events will help us to distinguish detailed merging physical processes. In this work, we aim to provide a generic scenario of DWD merging, investigate the emission of all major messengers, with a focus on GWs and neutrinos. Our goal is to provide some guidance for current and future (collaborative) efforts of multi-messenger observations. Throughout the merging evolution of a DWD system, different messengers (GW, neutrino and electromagnetic wave) will dominate at different times. In this work, we show that DWD merger events located at the distance of 1 kpc can indeed produce detectable signals of GWs and neutrinos. The GW frequency is in 0.3–0.6 Hz band around 10 days before tidal disruption begins. We estimate that in optimistic situations, the neutrino number detected by upcoming detectors such as JUNO and Hyper-Kamiokande can reach O(1) for a DWD merging event at ∼1 kpc.
A Generic Algorithm for Top-K On-Shelf Utility Mining

On-shelf utility mining (OSUM) is an emerging research direction in data mining. It aims to discover itemsets that have high relative utility in their selling time period. Compared with traditional utility mining, OSUM can find more practical and meaningful patterns in real-life applications. However, there is a major drawback to traditional OSUM. For normal users, it is hard to define a minimum threshold minutil for mining the right amount of on-shelf high utility itemsets. On one hand, if the threshold is set too high, the number of patterns would not be enough. On the other hand, if the threshold is set too low, too many patterns will be discovered and cause an unnecessary waste of time and memory consumption. To address this issue, the user usually directly specifies a parameter k, where only the top-k high relative utility itemsets would be considered. Therefore, in this paper, we propose a generic algorithm named TOIT for mining Top-k On-shelf hIgh-utility paTterns to solve this problem. TOIT applies a novel strategy to raise the minutil based on the on-shelf datasets. Besides, two novel upper-bound strategies named subtree utility and local utility are applied to prune the search space. By adopting the strategies mentioned above, the TOIT algorithm can narrow the search space as early as possible, improve the mining efficiency, and reduce the memory consumption, so it can obtain better performance than other algorithms. A series of experiments have been conducted on real datasets with different styles to compare the effects with the state-of-the-art KOSHU algorithm. The experimental results showed that TOIT outperforms KOSHU in both running time and memory consumption.