Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel distributed sensor using long-distance Mach-Zehnder interferometer

This paper demonstrates the recent achievements in the field of distributed optical fiber sensor (DOFS). A novel type of distributed optic fiber vibration sensor is presented to detect and locate a time-varying disturbance along the whole fiber length. It measures the first simultaneous information by using Mach-Zehnder Interferometer(MZI). The sensor consists of a MZI where two directional optical signals are simultaneously traveling. We discuss the principles of distributed optic fiber vibration sensor based on long-length MZI, and present the preliminary results on the application of distributed optic fiber sensor for the measurements of distributed signals in long-distance region. The sensor is illuminated by one continuous-wave laser, which is different from the conventional scheme. The phase shift caused by any disturbance around the fiber is detected and converted to the information on the perturbation position and amplitude, which can be determined by combining two phase signals from the above device. In addition, we adopt a new technique by using optical delay effect to improve the spatial resolution of distributed optic fiber sensor. It is completely different from the spatial-resolution technique of optical time-domain reflectometry (OTDR). The system has capability for cross-correlation algorithm and fast Fourier transform (FFT) analysis of the detected disturbance signals. For the sake of determining the disturbance position and type, high speed digital processing (DSP) technology is used in the sensor system, which can carry out all operations in real time and promote the resolution of localization. The hardware design of the sensor is completed and the experimental results are obtained. This type of optic fiber sensor has better sensitivity and resolution than the others. It achieves a 100Hz-20KHz frequency of vibration resolution, with 100-m practical spatial resolution, over a sensing length of 8 km. At the time of writing, the system is only partially completed; therefore the content of this paper will focus on the principle of distributed optical fiber sensor. The results mainly are presented in laboratory.
Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costs

Inverse optimal control can be used to characterize behavior in sequential decision-making tasks. Most existing work, however, is limited to fully observable or linear systems, or requires the action signals to be known. Here, we introduce a probabilistic approach to inverse optimal control for partially observable stochastic non-linear systems with unobserved action signals, which unifies previous approaches to inverse optimal control with maximum causal entropy formulations. Using an explicit model of the noise characteristics of the sensory and motor systems of the agent in conjunction with local linearization techniques, we derive an approximate likelihood function for the model parameters, which can be computed within a single forward pass. We present quantitative evaluations on stochastic and partially observable versions of two classic control tasks and two human behavioral tasks. Importantly, we show that our method can disentangle perceptual factors and behavioral costs despite the fact that epistemic and pragmatic actions are intertwined in sequential decision-making under uncertainty, such as in active sensing and active learning. The proposed method has broad applicability, ranging from imitation learning to sensorimotor neuroscience.
Implicit Visual Bias Mitigation by Posterior Estimate Sharpening of a Bayesian Neural Network

The fairness of a deep neural network is strongly affected by dataset bias and spurious correlations, both of which are usually present in modern feature-rich and complex visual datasets. Due to the difficulty and variability of the task, no single de-biasing method has been universally successful. In particular, implicit methods not requiring explicit knowledge of bias variables are especially relevant for real-world applications. We propose a novel implicit mitigation method using a Bayesian neural network, allowing us to leverage the relationship between epistemic uncertainties and the presence of bias or spurious correlations in a sample. Our proposed posterior estimate sharpening procedure encourages the network to focus on core features that do not contribute to high uncertainties. Experimental results on three benchmark datasets demonstrate that Bayesian networks with sharpened posterior estimates perform comparably to prior existing methods and show potential worthy of further exploration.
Emotional Framing in the Spreading of False and True Claims

The explosive growth of online misinformation, such as false claims, has affected the social behavior of online users. In order to be persuasive and mislead the audience, false claims are made to trigger emotions in their audience. This paper contributes to understanding how misinformation in social media is shaped by investigating the emotional framing that authors of the claims try to create for their audience. We investigate how, firstly, the existence of emotional framing in the claims depends on the topic and credibility of the claims. Secondly, we explore how emotionally framed content triggers emotional response posts by social media users, and how emotions expressed in claims and corresponding users’ response posts affect their sharing behavior on social media. Analysis of four data sets covering different topics (politics, health, Syrian war, and COVID-19) reveals that authors shape their claims depending on the topic area to pass targeted emotions to their audience. By analysing responses to claims, we show that the credibility of the claim influences the distribution of emotions that the claim incites in its audience. Moreover, our analysis shows that emotions expressed in the claims are repeated in the users’ responses. Finally, the analysis of users’ sharing behavior shows that negative emotional framing such as anger, fear, and sadness of false claims leads to more interaction among users than positive emotions. This analysis also reveals that in the claims that trigger happy responses, true claims result in more sharing compared to false claims.
Dispersion relation reconstruction for 2D Photonic Crystals based on polynomial interpolation

Dispersion relation reflects the dependence of wave frequency on its wave vector when the wave passes through certain material. It demonstrates the properties of this material and thus it is critical. However, dispersion relation reconstruction is very time consuming and expensive. To address this bottleneck, we propose in this paper an efficient dispersion relation reconstruction scheme based on global polynomial interpolation for the approximation of 2D photonic band functions. Our method relies on the fact that the band functions are piecewise analytic with respect to the wave vector in the first Brillouin zone. We utilize suitable sampling points in the first Brillouin zone at which we solve the eigenvalue problem involved in the band function calculation, and then employ Lagrange interpolation to approximate the band functions on the whole first Brillouin zone. Numerical results show that our proposed methods can significantly improve the computational efficiency.
Judicial Intelligent Assistant System: Extracting Events from Divorce Cases to Detect Disputes for the Judge

In formal procedure of civil cases, the textual materials provided by different parties describe the development process of the cases. It is a difficult but necessary task to extract the key information for the cases from these textual materials and to clarify the dispute focus of related parties. Currently, officers read the materials manually and use methods, such as keyword searching and regular matching, to get the target information. These approaches are time-consuming and heavily depending on prior knowledge and carefulness of the officers. To assist the officers to enhance working efficiency and accuracy, we propose an approach to detect disputes from divorce cases based on a two-round-labeling event extracting technique in this paper. We implement the Judicial Intelligent Assistant (JIA) system according to the proposed approach to 1) automatically extract focus events from divorce case materials, 2) align events by identifying co-reference among them, and 3) detect conflicts among events brought by the plaintiff and the defendant. With the JIA system, it is convenient for judges to determine the disputed issues. Experimental results demonstrate that the proposed approach and system can obtain the focus of cases and detect conflicts more effectively and efficiently comparing with existing method.
EnKSGD: A Class Of Preconditioned Black Box Optimization And Inversion Algorithms

In this paper, we introduce the Ensemble Kalman-Stein Gradient Descent (EnKSGD) class of algorithms. The EnKSGD class of algorithms builds on the ensemble Kalman filter (EnKF) line of work, applying techniques from sequential data assimilation to unconstrained optimization and parameter estimation problems. The essential idea is to exploit the EnKF as a black box (i.e. derivative-free, zeroth order) optimization tool if iterated to convergence. In this paper, we return to the foundations of the EnKF as a sequential data assimilation technique, including its continuous-time and mean-field limits, with the goal of developing faster optimization algorithms suited to noisy black box optimization and inverse problems. The resulting EnKSGD class of algorithms can be designed to both maintain the desirable property of affine-invariance, and employ the well-known backtracking line search. Furthermore, EnKSGD algorithms are designed to not necessitate the subspace restriction property and variance collapse property of previous iterated EnKF approaches to optimization, as both these properties can be undesirable in an optimization context. EnKSGD also generalizes beyond the $L^{2}$ loss, and is thus applicable to a wider class of problems than the standard EnKF. Numerical experiments with both linear and nonlinear least squares problems, as well as maximum likelihood estimation, demonstrate the faster convergence of EnKSGD relative to alternative EnKF approaches to optimization.
Probing the Star Formation Main Sequence Down to 108 M ⊙ at 1.0 < z < 3.0

We investigate the star formation main sequence (MS; SFR–M ⋆) down to 108−9 M ⊙ using a sample of 34,061 newly discovered ultrafaint (27 mag ≲ i ≲ 30 mag) galaxies at 1 < z < 3 detected in the GOODS-N field. Virtually none of these galaxies are contained in previous public catalogs, effectively doubling the number of known sources in the field. The sample was constructed by stacking the optical broadband observations taken by the HST/GOODS-CANDELS surveys, as well as the 25 ultradeep medium-band images gathered by the GTC/SHARDS project. Our sources are faint (average observed magnitudes 〈i〉 ∼ 28.2 and 〈H〉 ∼ 27.9 mag), blue (UV slope 〈β〉 ∼ −1.9), star-forming (rest-frame colors 〈U − V〉 ∼ 0.10 and 〈V − J〉 ∼ 0.17 mag) galaxies. These observational characteristics are identified with young (mass-weighted age 〈t M−w 〉 ∼ 0.014 Gyr) stellar populations subject to low attenuations (〈A(V)〉 ∼ 0.30 mag). Our sample allows us to probe the MS down to 108.0 M ⊙ at z = 1 and 108.5 M ⊙ at z = 3, around 0.6 dex deeper than previous analyses. In the low-mass galaxy regime, we find an average value for the slope of 0.97 at 1 < z < 2 and 1.12 at 2 < z < 3. Nearly 60% of our sample presents stellar masses in the range 106-8 M ⊙ at 1 < z
Difference-based covariance matrix estimate in time series nonparametric regression with applications to specification tests

Long-run covariance matrix estimation is the building block of time series inference. The corresponding difference-based estimator, which avoids detrending, has attracted considerable interest due to its robustness to both smooth and abrupt structural breaks and its competitive finite sample performance. However, existing methods mainly focus on estimators for the univariate process while their direct and multivariate extensions for most linear models are asymptotically biased. We propose a novel difference-based and debiased long-run covariance matrix estimator for functional linear models with time-varying regression coefficients, allowing time series non-stationarity, long-range dependence, state-heteroscedasticity and their mixtures. We apply the new estimator to (i) the structural stability test, overcoming the notorious non-monotonic power phenomena caused by piecewise smooth alternatives for regression coefficients, and (ii) the nonparametric residual-based tests for long memory, improving the performance via the residual-free formula of the proposed estimator. The effectiveness of the proposed method is justified theoretically and demonstrated by superior performance in simulation studies, while its usefulness is elaborated via real data analysis. Our method is implemented in the R package mlrv.
Robust Tumor Detection from Coarse Annotations via Multi-Magnification Ensembles

Cancer detection and classification from gigapixel whole slide images of stained tissue specimens has recently experienced enormous progress in computational histopathology. The limitation of available pixel-wise annotated scans shifted the focus from tumor localization to global slide-level classification on the basis of (weakly-supervised) multiple-instance learning despite the clinical importance of local cancer detection. However, the worse performance of these techniques in comparison to fully supervised methods has limited their usage until now for diagnostic interventions in domains of life-threatening diseases such as cancer. In this work, we put the focus back on tumor localization in form of a patch-level classification task and take up the setting of so-called coarse annotations, which provide greater training supervision while remaining feasible from a clinical standpoint. To this end, we present a novel ensemble method that not only significantly improves the detection accuracy of metastasis on the open CAMELYON16 data set of sentinel lymph nodes of breast cancer patients, but also considerably increases its robustness against noise while training on coarse annotations. Our experiments show that better results can be achieved with our technique making it clinically feasible to use for cancer diagnosis and opening a new avenue for translational and clinical research.
Reinforcement learning for optimization of energy trading strategy

An increasing part of energy is produced from renewable sources by a large number of small producers. The efﬁciency of these sources is volatile and, to some extent, random, exacerbating the energy market balance problem. In many countries, that balancing is performed on day-ahead (DA) energy markets. In this paper, we consider automated trading on a DA energy market by a medium size prosumer. We model this activity as a Markov Decision Process and formalize a framework in which a ready-to-use strategy can be optimized with real-life data. We synthesize parametric trading strategies and optimize them with an evolutionary algo-rithm. We also use state-of-the-art reinforcement learning algorithms to optimize a black-box trading strategy fed with available information from the environment that can impact future prices.
Using Connected Vehicle Trajectory Data to Evaluate the Effects of Speeding

Speeding remains a key factor in traffic fatalities, prompting transportation agencies to propose speed management solutions. While studies have examined speeding percentages above limits, few address its impact on individual journeys. Most studies rely on detector speed data, lacking route insights. This research employs connected vehicle trajectory data to analyze driver paths and variables, predicting speeding levels with various learning models. Extreme Gradient Boosting performed best, achieving 75.6% accuracy. This model elucidates how journey factors influence speeding and forecasts high-speed zones. Results reveal a driver’s total travel time significantly affects speeding, along with environmental features like residential area proportions. These findings aid transportation agencies in understanding trip-specific speeding factors, potentially informing better road safety measures.
A Bandit Learning Method for Continuous Games Under Feedback Delays with Residual Pseudo-Gradient Estimate

Learning in multi-player games can model a large variety of practical scenarios, where each player seeks to optimize its own local objective function, which at the same time relies on the actions taken by others. Motivated by the frequent absence of first-order information such as partial gradients in solving local optimization problems and the prevalence of asynchronicity and feedback delays in multi-agent systems, we introduce a bandit learning algorithm, which integrates mirror descent, residual pseudo-gradient estimates, and the priority-based feedback utilization strategy, to contend with these challenges. We establish that for pseudo-monotone plus games, the actual sequences of play generated by the proposed algorithm converge a.s. to critical points. Compared with the existing method, the proposed algorithm yields more consistent estimates with less variation and allows for more aggressive choices of parameters. Finally, we illustrate the validity of the proposed algorithm through a thermal load management problem of building complexes.
Complex Discretization approximation for the full dynamics of system-environment quantum models

The discretization approximation method used to simulate the open dynamics of the environment in continuum often suffers from recurrence, which results in inefficiency. To address this issue, this paper proposes a generalization of the discretization approximation method in the complex plane using complex Gauss quadratures. The resulting effective Hamiltonian is non-Hermitian due to the dissipative dynamics of the system. The proposed method is applied to two solvable models, namely the dephasing model and the single-excitation open dynamics in the generalized Aubry-Andr\'{e}-Harper model. The results demonstrate that the occurrence of complex discrete modes in the environment can significantly reduce recurrence, thereby enabling the efficient and accurate simulation of open dynamics in both models.
Rps14 upregulation promotes inner ear progenitor proliferation and hair cell regeneration in the neonatal mouse cochlea

Sensorineural hearing loss a result from hair cell damage, which is irreversible in mammals owing to the lack of hair cell regeneration, but recent researches have shown that Lgr5+ supporting cells are progenitors capable of regenerating hair cells. RPS14 (ribosomal protein S14) is a 40S ribosomal subunit component and is associated with erythrocyte differentiation, and in this study, we used a novel adeno‐associated virus‐inner ear system to upregulate Rps14 expression in cultured hair cell progenitors and observed an enhancement on their ability to proliferate and differentiate into hair cells. Similarly, Rps14 overexpression in the mice cochlea could promote supporting cells proliferation by activating the Wnt signalling pathway. In addition, over‐expressing Rps14 induced hair cells regeneration in the organ of Corti, and lineage tracing showed that the new hair cells had transformed from Lgr5+ progenitors. In conclusion, our analysis reveals the potential role of Rps14 in driving hair cell regeneration in mammalian.
Supplementary Materials for: Understanding and Improving Features Learned in Deep Functional Maps

In this document, we collect all the results and discussions, which, due to the page limit, could not find space in the main manuscript. More precisely, we first provide the proof of the theorem we introduced in Sec. 3.3 of the main text in Appendix A. Then, the details of the implementation are provided in Appendix B. We present a more in-depth analysis of the modifications we introduced to the deep functional map pipeline in Appendix C. A comparison with other shape-matching methods is provided in Appendix D, as well as additional results regarding the generalization power of pre-trained features in Appendix E. Finally, some qualitative results are included in Appendix F.
From axioms over graphs to vectors, and back again: evaluating the properties of graph-based ontology embeddings

Several approaches have been developed that generate embeddings for Description Logic ontologies and use these embeddings in machine learning. One approach of generating ontologies embeddings is by first embedding the ontologies into a graph structure, i.e., introducing a set of nodes and edges for named entities and logical axioms, and then applying a graph embedding to embed the graph in $\mathbb{R}^n$. Methods that embed ontologies in graphs (graph projections) have different formal properties related to the type of axioms they can utilize, whether the projections are invertible or not, and whether they can be applied to asserted axioms or their deductive closure. We analyze, qualitatively and quantitatively, several graph projection methods that have been used to embed ontologies, and we demonstrate the effect of the properties of graph projections on the performance of predicting axioms from ontology embeddings. We find that there are substantial differences between different projection methods, and both the projection of axioms into nodes and edges as well ontological choices in representing knowledge will impact the success of using ontology embeddings to predict axioms.
Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching.

The process of matching patients with suitable clinical trials is essential for advancing medical research and providing optimal care. However, current approaches face challenges such as data standardization, ethical considerations, and a lack of interoperability between Electronic Health Records (EHRs) and clinical trial criteria. In this paper, we explore the potential of large language models (LLMs) to address these challenges by leveraging their advanced natural language generation capabilities to improve compatibility between EHRs and clinical trial descriptions. We propose an innovative privacy-aware data augmentation approach for LLM-based patient-trial matching (LLM-PTM), which balances the benefits of LLMs while ensuring the security and confidentiality of sensitive patient data. Our experiments demonstrate a 7.32% average improvement in performance using the proposed LLM-PTM method, and the generalizability to new data is improved by 12.12%. Additionally, we present case studies to further illustrate the effectiveness of our approach and provide a deeper understanding of its underlying principles.