Miracle-dz/UniFAR-FTUs huggingface dataset:
Directed Network Comparison Using Motifs

Analyzing and characterizing the differences between networks is a fundamental and challenging problem in network science. Most previous network comparison methods that rely on topological properties have been restricted to measuring differences between two undirected networks. However, many networks, such as biological networks, social networks, and transportation networks, exhibit inherent directionality and higher-order attributes that should not be ignored when comparing networks. Therefore, we propose a motif-based directed network comparison method that captures local, global, and higher-order differences between two directed networks. Specifically, we first construct a motif distribution vector for each node, which captures the information of a node’s involvement in different directed motifs. Then, the dissimilarity between two directed networks is defined on the basis of a matrix, which is composed of the motif distribution vector of every node and the Jensen–Shannon divergence. The performance of our method is evaluated via the comparison of six real directed networks with their null models, as well as their perturbed networks based on edge perturbation. Our method is superior to the state-of-the-art baselines and is robust with different parameter settings.
A Closed-form Solution for Weight Optimization in Fully-connected Feed-forward Neural Networks

This work addresses weight optimization problem for fully-connected feed-forward neural networks. Unlike existing approaches that are based on back-propagation (BP) and chain rule gradient-based optimization (which implies iterative execution, potentially burdensome and time-consuming in some cases), the proposed approach offers the solution for weight optimization in closed-form by means of least squares (LS) methodology. In the case where the input-to-output mapping is injective, the new approach optimizes the weights in a back-propagating fashion in a single iteration by jointly optimizing a set of weights in each layer for each neuron. In the case where the input-to-output mapping is not injective (e.g., in classification problems), the proposed solution is easily adapted to obtain its final solution in a few iterations. An important advantage over the existing solutions is that these computations (for all neurons in a layer) are independent from each other; thus, they can be carried out in parallel to optimize all weights in a given layer simultaneously. Furthermore, its running time is deterministic in the sense that one can obtain the exact number of computations necessary to optimize the weights in all network layers (per iteration, in the case of non-injective mapping). Our simulation and empirical results show that the proposed scheme, BPLS, works well and is competitive with existing ones in terms of accuracy, but significantly surpasses them in terms of running time. To summarize, the new method is straightforward to implement, is competitive and computationally more efficient than the existing ones, and is well-tailored for parallel implementation.
The Effect of Value-Focused Discussions on Scientists' Ethical Decision Making

Many scientists view science as value-free, despite the fact that both epistemic and non-epistemic values structure scientific inquiry. Current ethics training usually focuses on transmitting knowledge about high-level ethical concepts or rules and is widely regarded as ineffective. We argue that ethics training will be more effective at improving ethical decision making if it focuses on connecting values to science. We pull from philosophy and psychology to define ethical decision making using the Four Component Model. This model states that in order to make an ethical decision someone must consider four components: moral sensitivity, moral reasoning, moral motivation, and moral implementation. We formed a moderated fellowship of fourteen science faculty from different disciplines who met for ten sessions over the course of a year, where they discussed the values embedded in different scientific norms. We then conducted interviews before and after the year-long fellowship that involved guided reflection of scenarios where there was some kind of ethical misconduct where the scientific practice required value judgements (e.g using unpublished data in their own work). We looked at how the fellowship affected the scientists' ability to recognize ethical dimensions regarding the scenarios. We found that this fellowship improved moral sensitivity, but their moral reasoning does not improve. We outlined our approach on how to look at scientists' ethical decision making and made recommendations on how to improve our approach. This work can inform future ethical training to align better with what scientists value and introduce useful concepts from philosophy and psychology to education research in physics.
Some remarks on Linear-quadratic closed-loop games with many players

We identify structural assumptions which provide solvability of the Nash system arising from a linear-quadratic closed-loop game, with stable properties with respect to the number of players. In a setting of interactions governed by a sparse graph, both short-time and long-time existence of a classical solution for the Nash system set in infinitely many dimensions are addressed, as well as convergence to the solution to the respective ergodic problem as the time horizon goes to infinity; in addition, equilibria for the infinite-dimensional game are shown to provide $\epsilon$-Nash closed-loop equilibria for the $N$-player game. In a setting of generalized mean-field type (where the number of interactions is large but not necessarily symmetric), directly from the $N$-player Nash system estimates on the value functions are deduced on an arbitrary large time horizon, which should pave the way for a convergence result as $N$ goes to infinity.
Batch-ICL: Effective, Efficient, and Order-Agnostic In-Context Learning

In this paper, by treating in-context learning (ICL) as a meta-optimization process, we explain why LLMs are sensitive to the order of ICL examples. This understanding leads us to the development of Batch-ICL, an effective, efficient, and order-agnostic inference algorithm for ICL. Differing from the standard N-shot learning approach, Batch-ICL employs $N$ separate 1-shot forward computations and aggregates the resulting meta-gradients. These aggregated meta-gradients are then applied to the forward computation of a zero-shot query to generate the final prediction. This batch processing approach renders the LLM agnostic to the order of ICL examples. Through extensive experiments and analysis, we demonstrate that Batch-ICL consistently outperforms most permutations of ICL examples. In some cases, it even exceeds the performance of the best order for standard ICL, all while reducing the computational resources required. Furthermore, we develop a novel variant of Batch-ICL featuring multiple"epochs"of meta-optimization. This variant implicitly explores permutations of ICL examples, further enhancing ICL performance.
Decoupling Pixel Flipping and Occlusion Strategy for Consistent XAI Benchmarks

Feature removal is a central building block for eXplainable AI (XAI), both for occlusion-based explanations (Shapley values) as well as their evaluation (pixel flipping, PF). However, occlusion strategies can vary significantly from simple mean replacement up to inpainting with state-of-the-art diffusion models. This ambiguity limits the usefulness of occlusion-based approaches. For example, PF benchmarks lead to contradicting rankings. This is amplified by competing PF measures: Features are either removed starting with most influential first (MIF) or least influential first (LIF). This study proposes two complementary perspectives to resolve this disagreement problem. Firstly, we address the common criticism of occlusion-based XAI, that artificial samples lead to unreliable model evaluations. We propose to measure the reliability by the R(eference)-Out-of-Model-Scope (OMS) score. The R-OMS score enables a systematic comparison of occlusion strategies and resolves the disagreement problem by grouping consistent PF rankings. Secondly, we show that the insightfulness of MIF and LIF is conversely dependent on the R-OMS score. To leverage this, we combine the MIF and LIF measures into the symmetric relevance gain (SRG) measure. This breaks the inherent connection to the underlying occlusion strategy and leads to consistent rankings. This resolves the disagreement problem, which we verify for a set of 40 different occlusion strategies.
Spin Resonance Spectroscopy with an Electron Microscope

Coherent spin resonance methods, such as nuclear magnetic resonance and electron spin resonance spectroscopy, have led to spectrally highly sensitive, non-invasive quantum imaging techniques. Here, we propose a pump-probe spin resonance spectroscopy approach, designed for electron microscopy, based on microwave pump fields and electron probes. We investigate how quantum spin systems couple to electron matter waves through their magnetic moments and how the resulting phase shifts can be utilized to gain information about the states and dynamics of these systems. Notably, state-of-the-art transmission electron microscopy provides the means to detect phase shifts almost as small as that due to a single electron spin. This could enable state-selective observation of spin dynamics on the nanoscale and indirect measurement of the environment of the examined spin systems, providing information, for example, on the atomic structure, local chemical composition and neighboring spins.
Resonant Solitary States in Complex Networks

Partially synchronized solitary states occur frequently when a synchronous system of networked oscillators is perturbed locally. Remarkably, several asymptotic states of different frequencies can coexist at the same node. Here, we uncover the mechanism underlying this multistability: The resonant back-reaction of the network's modes on the solitary can lead to a large energy transfer between them, and the solitary's frequency adaptation can tune the system to this resonance. We provide a full analytic analysis of this mechanism, and show which network structures enable its presence.
Advantage of Quantum Neural Networks as Quantum Information Decoders

A promising strategy to protect quantum information from noise-induced errors is to encode it into the low-energy states of a topological quantum memory device. However, readout errors from such memory under realistic settings is less understood. We study the problem of decoding quantum information encoded in the groundspaces of topological stabilizer Hamiltonians in the presence of generic perturbations, such as quenched disorder. We first prove that the standard stabilizer-based error correction and decoding schemes work adequately well in such perturbed quantum codes by showing that the decoding error diminishes exponentially in the distance of the underlying unperturbed code. We then prove that Quantum Neural Network (QNN) decoders provide an almost quadratic improvement on the readout error. Thus, we demonstrate provable advantage of using QNNs for decoding realistic quantum error-correcting codes, and our result enables the exploration of a wider range of non-stabilizer codes in the near-term laboratory settings.
LCB-net: Long-Context Biasing for Audio-Visual Speech Recognition

The growing prevalence of online conferences and courses presents a new challenge in improving automatic speech recognition (ASR) with enriched textual information from video slides. In contrast to rare phrase lists, the slides within videos are synchronized in real-time with the speech, enabling the extraction of long contextual bias. Therefore, we propose a novel long-context biasing network (LCB-net) for audio-visual speech recognition (AVSR) to leverage the long-context information available in videos effectively. Specifically, we adopt a bi-encoder architecture to simultaneously model audio and long-context biasing. Besides, we also propose a biasing prediction module that utilizes binary cross entropy (BCE) loss to explicitly determine biased phrases in the long-context biasing. Furthermore, we introduce a dynamic contextual phrases simulation to enhance the generalization and robustness of our LCB-net. Experiments on the SlideSpeech, a large-scale audio-visual corpus enriched with slides, reveal that our proposed LCB-net outperforms general ASR model by 9.4%/9.1%/10.9% relative WER/U-WER/B-WER reduction on test set, which enjoys high unbiased and biased performance. Moreover, we also evaluate our model on LibriSpeech corpus, leading to 23.8%/19.2%/35.4% relative WER/U-WER/B-WER reduction over the ASR model.
First principles study of electronic structure and transport in graphene grain boundaries

Grain boundaries play a major role for electron transport in graphene sheets grown by chemical vapor deposition. Here we investigate the electronic structure and transport properties of idealized graphene grain boundaries (GBs) in bi-crystals using first principles density functional theory (DFT) and non-equilibrium Greens functions (NEGF). We generated \TotalStructs different grain boundaries using an automated workflow where their geometry is relaxed with DFT. We find that the GBs generally show a quasi-1D bandstructure along the GB. We group the GBs in four classes based on their conductive properties: transparent, opaque, insulating, and spin-polarizing and show how this is related to angular mismatch, quantum mechanical interference, and out-of-plane buckling. Especially, we find that spin-polarization in the GB correlates with out-of-plane buckling. We further investigate the characteristics of these classes in simulated scanning tunnelling spectroscopy and diffusive transport along the GB which demonstrate how current can be guided along the GB.
Visions Of Destruction: Exploring Human Impact on Nature by Navigating the Latent Space of a Diffusion Model via Gaze

This paper discusses the artwork"Visions of Destruction", with a primary conceptual focus on the Anthropocene, which is communicated through audience interaction and generative AI as artistic research methods. Gaze-based interaction transitions the audience from mere observers to agents of landscape transformation, fostering a profound, on-the-edge engagement with pressing issues such as climate change and planetary destruction. The paper looks into early references of interactive art history that deploy eye-tracking as a method for audience interaction, and presents recent AI-aided artworks that demonstrate interactive latent space navigation.
Machine Learning for Refining Knowledge Graphs: A Survey

Knowledge graph (KG) refinement refers to the process of filling in missing information, removing redundancies, and resolving inconsistencies in knowledge graphs. With the growing popularity of KG in various domains, many techniques involving machine learning have been applied, but there is no survey dedicated to machine learning-based KG refinement yet. Based on a novel framework following the KG refinement process, this paper presents a survey of machine learning approaches to KG refinement according to the kind of operations in KG refinement, the training datasets, mode of learning, and process multiplicity. Furthermore, the survey aims to provide broad practical insights into the development of fully automated KG refinement.
Main geological hazards and monitoring methods in common use in the southwest region of China

Geology hazard is particularly outstanding and take place frequently because of big mountainous area in the southwest China.The main geology hazards are collapses,landslides and debris flows.The monitoring work of geology hazards is mostly professional monitoring to reduce the hazard of engineering,the integration monitoring to reduce the disaster of town residential area and the pooling all the brains and brawn for monitoring and controlling the hazard in the country.The Ministry of Land and Resources has established the nationwide monitoring system of geology environment including the collapses,landslides,debris flows.Especially in the three Gorges region,professional monitoring of geology hazards is made by unification commanding of Center charge department,and professionals using the monitoring methods and apparatus.The main monitoring methods using currently include the earth's surface displacement monitoring,the deep displacement of landslides monitoring,the stress and strain of landslides monitoring,groundwater monitoring,3S technique application,InSar technique application,the automation monitoring based on modern communication technology etc.The monitoring plays an important role in emigrant moving and built in the three Gorges region,economic construction and establishing harmonious society.
The Factors Affecting Customers’ Satisfaction and Continuance Intention in Platform-To-Consumer Environments: A Case of Mobile Food Ordering Platforms

ABSTRACT:This research studied customer satisfaction and continuance intention in platformto-consumer environments. We investigated the structural relationship between performance expectancy, effort expectancy, social influence, perceived security, satisfaction, and continuance intention for mobile food ordering platforms in Taiwan. Data were collected by surveying users of mobile food ordering apps (MFOAs). The research model was tested with structural equation modelling method. Among hypothesized relationships, performance expectancy, effort expectancy, and perceived security on satisfaction are significant. For continuance intention, performance expectancy and social influence have direct effect, but effort expectancy and perceived security do not. The relationship between satisfaction and continuance intention is significant. This research contributes to the literature by expanding the study of customer decision making via an extended model of Unified Theory of Acceptance and Usage of Technology for a new type of business model (i.e., mobile platform apps).
Role of Penconazole in salt stress amelioration in Sesamum indicum L.

ABSTRACT Sesame (Sesamum indicum L.) is one of the world’s important oil crops. The aim of the present study was to investigate the effects of salt stress on physiological and biochemical parameters of sesame and assess the improvement of salt tolerance by Penconazole (PEN). Plants were treated with various NaCl concentrations (0, 50, 100, and 200 mM) without or with PEN (15 mg L−1). Salt stress inhibited the growth in sesame. Our results showed sesame plant under salinity used of the enzymatic and non-enzymatic system and compatible osmolytes to overcome on oxidative and osmotic stress, respectively. PEN improved the growth and reduced the negative effects of salinity in sesame. In fact, PEN increased the resistance of this plant under salt stress by improving the antioxidant system and osmotic regulation. Thus, this compound can be used as stress-ameliorating agents in this crop plant.
Application of ICF-CY Core Sets in Children with Spastic Cerebral Palsy

Abstract In this study, we applied brief age-specific core sets of international classification of functioning, disability, and health in children and youth (ICF-CY) in children with spastic cerebral palsy (CP). It was a descriptive cross-sectional study. One hundred children with spastic CP, age range 1 to 6 years, were included in the study. Thirty-one age-specific brief ICF core sets were applied to children with spastic CP to assess their functional status. Common areas of impairments in children with spastic CP were the body functions and activities and participation. Environmental factors were also a major limitation in the care of CP. The application of ICF core sets provides a novel and standardized approach in describing the functional profile of children with CP. Through application of these core sets, we can standardize the functional assessments of CP worldwide.
Measuring cloud thermodynamic phase with shortwave infrared imaging spectroscopy

Shortwave Infrared imaging spectroscopy enables accurate remote mapping of cloud thermodynamic phase at high spatial resolution. We describe a measurement strategy to exploit signatures of liquid and ice absorption in cloud top apparent reflectance spectra from 1.4 to 1.8 μm. This signal is generally insensitive to confounding factors such as solar angles, view angles, and surface albedo. We first evaluate the approach in simulation and then apply it to airborne data acquired in the Calwater‐2/ACAPEX campaign of Winter 2015. Here NASA's “Classic” Airborne Visible Infrared Imaging Spectrometer (AVIRIS‐C) remotely observed diverse cloud formations while the U.S. Department of Energy ARM Aerial Facility G‐1 aircraft measured cloud integral and microphysical properties in situ. The coincident measurements demonstrate good separation of the thermodynamic phases for relatively homogeneous clouds.
Modeling and analysis of CSAMT field source effect and its characteristics

Controlled-source audio-frequency magnetotellurics (CSAMT) has been a highly successful geophysical tool used in a variety of geological exploration studies for many years. However, due to the artificial source used in the CSAMT technique, two important factors are considered during interpretation: non-plane-wave or geometric effects and source overprint effects. Hence, in this paper we simulate the source overprint effects and analyzed the rule and characteristics of its influence on CSAMT applications. Two-dimensional modeling was carried out using an adaptive unstructured finite element method to simulate several typical models. Also, we summarized the characteristics and rule of the source overprint effects and analyzed its influence on the data taken over several mining areas. The results obtained from the study shows that the occurrence and strength of the source overprint effect is dependent on the location of the source dipole, in relation to the receiver and the subsurface geology. In order to avoid source overprint effects, three principle were suggested to determine the best location for the grounded dipole source in the field.
EHES Working

The Act of Union (1707) unified England and Scotland politically and economically, formally establishing the United Kingdom of Great Britain, and a customs union throughout the island of Britain. In this paper, we examine the impact of union on British market integration using wheat prices from a sample of English and Scottish markets. We estimate a coefficient of variation and a dynamic factor model to examine the evolution of price convergence and market efficiency across English and Scottish markets from the 1640s to the mid-eighteenth century. Our results suggest that union strongly influenced price convergence but had little impact on market efficiency. There was an immediate sharp increase in the level of price convergence across British markets following the union, suggesting that the elimination of tariffs and other trade frictions was a strong driver of price convergence. To formally test the impact of union on price convergence, we estimate border effects which show the impact of the pre-union border on price gaps between English and Scottish markets. The results suggest that the customs union strongly influenced price convergence, lowering the average price gap by 16%, and implying a pre-union border width of 160-162km.