Miracle-dz/UniFAR-FTUs huggingface dataset:
Terahertz ratchet in graphene 2D metamaterial formed by a patterned gate with an antidot arrayd

We report the observation of the terahertz-induced ratchet effect in graphene-based two-dimensional (2D) metamaterials. The metamaterial consists of a graphite gate patterned with an array of triangular antidots placed under a graphene monolayer. We show that the ratchet current appears due to the noncentrosymmetry of the periodic structure unit cell. The ratchet current is generated owing to the combined action of a spatially periodic in-plane electrostatic potential and a periodically modulated radiation electric field caused by near-field diffraction. The magnitude and direction of the ratchet current are shown to be controlled by voltages applied to both back and patterned gates, which change the lateral asymmetry, carrier type and density. The phenomenological and microscopic theories of ratchet effects in graphene-based 2D metamaterials are developed. The experimental data are discussed in the light of the theory based on the solution of the Boltzmann kinetic equation and the calculated electrostatic potential profile. The theory describes well all the experimental results and shows that the observed ratchet current consists of the Seebeck thermoratchet contribution as well as the linear contribution, which is sensitive to the orientation of the radiation electric field vector with respect to the triangles.
Weakly supervised covariance matrices alignment through Stiefel matrices estimation for MEG applications

This paper introduces a novel domain adaptation technique for time series data, called Mixing model Stiefel Adaptation (MSA), specifically addressing the challenge of limited labeled signals in the target dataset. Leveraging a domain-dependent mixing model and the optimal transport domain adaptation assumption, we exploit abundant unlabeled data in the target domain to ensure effective prediction by establishing pairwise correspondence with equivalent signal variances between domains. Theoretical foundations are laid for identifying crucial Stiefel matrices, essential for recovering underlying signal variances from a Riemannian representation of observed signal covariances. We propose an integrated cost function that simultaneously learns these matrices, pairwise domain relationships, and a predictor, classifier, or regressor, depending on the task. Applied to neuroscience problems, MSA outperforms recent methods in brain-age regression with task variations using magnetoencephalography (MEG) signals from the Cam-CAN dataset.
Rich representations and superrigidity

We investigate and compare applications of the Zilber-Pink conjecture and dynamical methods to rigidity problems for arithmetic real and complex hyperbolic lattices. Along the way we obtain new general results about reconstructing a variation of Hodge structures from its typical Hodge locus that may be of independent interest. Applications to Siu's immersion problem are also discussed.
On a positive-preserving, energy-stable numerical scheme to mass-action kinetics with detailed balance

In this paper, we provide a detailed theoretical analysis of the numerical scheme introduced in J. Comput. Phys. 436 (2021) 110253 for the reaction kinetics of a class of chemical reaction networks that satisfies detailed balance condition. In contrast to conventional numerical approximations, which are typically constructed based on ordinary differential equations (ODEs) for the concentrations of all involved species, the scheme is developed using the equations of reaction trajectories, which can be viewed as a generalized gradient flow of physically relevant free energy. The unique solvability, positivity-preserving, and energy-stable properties are proved for the general case involving multiple reactions, under a mild condition on the stoichiometric matrix.
Polyp-DDPM: Diffusion-Based Semantic Polyp Synthesis for Enhanced Segmentation

This study introduces Polyp-DDPM, a diffusion-based method for generating realistic images of polyps conditioned on masks, aimed at enhancing the segmentation of gastrointestinal (GI) tract polyps. Our approach addresses the challenges of data limitations, high annotation costs, and privacy concerns associated with medical images. By conditioning the diffusion model on segmentation masks-binary masks that represent abnormal areas-Polyp-DDPM outperforms state-of-the-art methods in terms of image quality (achieving a Frechet Inception Distance (FID) score of 78.47, compared to scores above 83.79) and segmentation performance (achieving an Intersection over Union (IoU) of 0.7156, versus less than 0.6694 for synthetic images from baseline models and 0.7067 for real data). Our method generates a high-quality, diverse synthetic dataset for training, thereby enhancing polyp segmentation models to be comparable with real images and offering greater data augmentation capabilities to improve segmentation models. The source code and pretrained weights for Polyp-DDPM are made publicly available at https://github.com/mobaidoctor/polyp-ddpm.
Employee Turnover Analysis Using Machine Learning Algorithms

Employee's knowledge is an organization asset. Turnover may impose apparent and hidden costs and irreparable damages. To overcome and mitigate this risk, employee's condition should be monitored. Due to high complexity of analyzing well-being features, employee's turnover predicting can be delegated to machine learning techniques. In this paper, we discuss employee's attrition rate. Three different supervised learning algorithms comprising AdaBoost, SVM and RandomForest are used to benchmark employee attrition accuracy. Attained models can help out at establishing predictive analytics.
Observation of the double quantum spin Hall phase in moir\'e WSe2

Quantum spin Hall (QSH) insulators are a topologically protected phase of matter in two dimensions that can support non-dissipative spin transport. A hallmark of the phase is a pair of helical edge states surrounding an insulating bulk. A higher (even) number of helical edge state pairs is usually not possible in real materials because spin mixing would gap out the edge states. Multiple pairs of helical edge states have been proposed in materials with spin conservation symmetry and high spin Chern bands, but remained experimentally elusive. Here, we demonstrate a QSH phase with one and two pairs of helical edge states in twisted bilayer WSe2 at moir\'e hole filling factor {\nu}= 2 and 4, respectively. We observe nearly quantized conductance or resistance plateaus of h/({\nu}e^2 ) at {\nu} = 2 and 4 while the bulk is insulating. The conductance is nearly independent of out-of-plane magnetic field and decreases under an in-plane magnetic field. We also observe nonlocal transport, which is sensitive only to the in-plane magnetic field. The results agree with quantum transport of helical edge states protected by Ising spin conservation symmetry and open a promising platform for low-power spintronics.
Encoding Version History Context for Better Code Representation

With the exponential growth of AI tools that generate source code, understanding software has become crucial. When developers comprehend a program, they may refer to additional contexts to look for information, e.g. program documentation or historical code versions. Therefore, we argue that encoding this additional contextual information could also benefit code representation for deep learning. Recent papers incorporate contextual data (e.g. call hierarchy) into vector representation to address program comprehension problems. This motivates further studies to explore additional contexts, such as version history, to enhance models' understanding of programs. That is, insights from version history enable recognition of patterns in code evolution over time, recurring issues, and the effectiveness of past solutions. Our paper presents preliminary evidence of the potential benefit of encoding contextual information from the version history to predict code clones and perform code classification. We experiment with two representative deep learning models, ASTNN and CodeBERT, to investigate whether combining additional contexts with different aggregations may benefit downstream activities. The experimental result affirms the positive impact of combining version history into source code representation in all scenarios; however, to ensure the technique performs consistently, we need to conduct a holistic investigation on a larger code base using different combinations of contexts, aggregation, and models. Therefore, we propose a research agenda aimed at exploring various aspects of encoding additional context to improve code representation and its optimal utilisation in specific situations.
Multi-class Road Defect Detection and Segmentation using Spatial and Channel-wise Attention for Autonomous Road Repairing

Road pavement detection and segmentation are critical for developing autonomous road repair systems. However, developing an instance segmentation method that simultaneously performs multi-class defect detection and segmentation is challenging due to the textural simplicity of road pavement image, the diversity of defect geometries, and the morphological ambiguity between classes. We propose a novel end-to-end method for multi-class road defect detection and segmentation. The proposed method comprises multiple spatial and channel-wise attention blocks available to learn global representations across spatial and channel-wise dimensions. Through these attention blocks, more globally generalised representations of morphological information (spatial characteristics) of road defects and colour and depth information of images can be learned. To demonstrate the effectiveness of our framework, we conducted various ablation studies and comparisons with prior methods on a newly collected dataset annotated with nine road defect classes. The experiments show that our proposed method outperforms existing state-of-the-art methods for multi-class road defect detection and segmentation methods.
Dynamic flux surrogate-based partitioned methods for interface problems

Partitioned methods for coupled problems rely on data transfers between subdomains to synchronize the subdomain equations and enable their independent solution. By treating each subproblem as a separate entity, these methods enable code reuse, increase concurrency and provide a convenient framework for plug-and-play multiphysics simulations. However, accuracy and stability of partitioned methods depends critically on the type of information exchanged between the subproblems. The exchange mechanisms can vary from minimally intrusive remap across interfaces to more accurate but also more intrusive and expensive estimates of the necessary information based on monolithic formulations of the coupled system. These transfer mechanisms are separated by accuracy, performance and intrusiveness gaps that tend to limit the scope of the resulting partitioned methods to specific simulation scenarios. Data-driven system identification techniques provide an opportunity to close these gaps by enabling the construction of accurate, computationally efficient and minimally intrusive data transfer surrogates. This approach shifts the principal computational burden to an offline phase, leaving the application of the surrogate as the sole additional cost during the online simulation phase. In this paper we formulate and demonstrate such a \emph{dynamic flux surrogate-based} partitioned method for a model advection-diffusion transmission problem by using Dynamic Mode Decomposition (DMD) to learn the dynamics of the interface flux from data. The accuracy of the resulting DMD flux surrogate is comparable to that of a dual Schur complement reconstruction, yet its application cost is significantly lower. Numerical results confirm the attractive properties of the new partitioned approach.
Rethinking RGB Color Representation for Image Restoration Models

Image restoration models are typically trained with a pixel-wise distance loss defined over the RGB color representation space, which is well known to be a source of blurry and unrealistic textures in the restored images. The reason, we believe, is that the three-channel RGB space is insufficient for supervising the restoration models. To this end, we augment the representation to hold structural information of local neighborhoods at each pixel while keeping the color information and pixel-grainedness unharmed. The result is a new representation space, dubbed augmented RGB ($a$RGB) space. Substituting the underlying representation space for the per-pixel losses facilitates the training of image restoration models, thereby improving the performance without affecting the evaluation phase. Notably, when combined with auxiliary objectives such as adversarial or perceptual losses, our $a$RGB space consistently improves overall metrics by reconstructing both color and local structures, overcoming the conventional perception-distortion trade-off.
Statistical Test for Anomaly Detections by Variational Auto-Encoders

In this study, we consider the reliability assessment of anomaly detection (AD) using Variational Autoencoder (VAE). Over the last decade, VAE-based AD has been actively studied in various perspective, from method development to applied research. However, when the results of ADs are used in high-stakes decision-making, such as in medical diagnosis, it is necessary to ensure the reliability of the detected anomalies. In this study, we propose the VAE-AD Test as a method for quantifying the statistical reliability of VAE-based AD within the framework of statistical testing. Using the VAE-AD Test, the reliability of the anomaly regions detected by a VAE can be quantified in the form of p-values. This means that if an anomaly is declared when the p-value is below a certain threshold, it is possible to control the probability of false detection to a desired level. Since the VAE-AD Test is constructed based on a new statistical inference framework called selective inference, its validity is theoretically guaranteed in finite samples. To demonstrate the validity and effectiveness of the proposed VAE-AD Test, numerical experiments on artificial data and applications to brain image analysis are conducted.
Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes

We address the problem of learning Granger causality from asynchronous, interdependent, multi-type event sequences. In particular, we are interested in discovering instance-level causal structures in an unsupervised manner. Instance-level causality identifies causal relationships among individual events, providing more fine-grained information for decision-making. Existing work in the literature either requires strong assumptions, such as linearity in the intensity function, or heuristically defined model parameters that do not necessarily meet the requirements of Granger causality. We propose Instance-wise Self-Attentive Hawkes Processes (ISAHP), a novel deep learning framework that can directly infer the Granger causality at the event instance level. ISAHP is the first neural point process model that meets the requirements of Granger causality. It leverages the self-attention mechanism of the transformer to align with the principles of Granger causality. We empirically demonstrate that ISAHP is capable of discovering complex instance-level causal structures that cannot be handled by classical models. We also show that ISAHP achieves state-of-the-art performance in proxy tasks involving type-level causal discovery and instance-level event type prediction.
On Learning Spatial Provenance in Privacy-Constrained Wireless Networks

In Vehicle-to-Everything networks that involve multi-hop communication, the Road Side Units (RSUs) typically aim to collect location information from the participating vehicles to provide security and network diagnostics features. While the vehicles commonly use the Global Positioning System (GPS) for navigation, they may refrain from sharing their precise GPS coordinates with the RSUs due to privacy concerns. Therefore, to jointly address the high localization requirements by the RSUs as well as the vehicles' privacy, we present a novel spatial-provenance framework wherein each vehicle uses Bloom filters to embed their partial location information when forwarding the packets. In this framework, the RSUs and the vehicles agree upon fragmenting the coverage area into several smaller regions so that the vehicles can embed the identity of their regions through Bloom filters. Given the probabilistic nature of Bloom filters, we derive an analytical expression on the error-rates in provenance recovery and then pose an optimization problem to choose the underlying parameters. With the help of extensive simulation results, we show that our method offers near-optimal Bloom filter parameters in learning spatial provenance. Some interesting trade-offs between the communication-overhead, spatial privacy of the vehicles and the error rates in provenance recovery are also discussed.
IMUSIC: IMU-based Facial Expression Capture

For facial motion capture and analysis, the dominated solutions are generally based on visual cues, which cannot protect privacy and are vulnerable to occlusions. Inertial measurement units (IMUs) serve as potential rescues yet are mainly adopted for full-body motion capture. In this paper, we propose IMUSIC to fill the gap, a novel path for facial expression capture using purely IMU signals, significantly distant from previous visual solutions.The key design in our IMUSIC is a trilogy. We first design micro-IMUs to suit facial capture, companion with an anatomy-driven IMU placement scheme. Then, we contribute a novel IMU-ARKit dataset, which provides rich paired IMU/visual signals for diverse facial expressions and performances. Such unique multi-modality brings huge potential for future directions like IMU-based facial behavior analysis. Moreover, utilizing IMU-ARKit, we introduce a strong baseline approach to accurately predict facial blendshape parameters from purely IMU signals. Specifically, we tailor a Transformer diffusion model with a two-stage training strategy for this novel tracking task. The IMUSIC framework empowers us to perform accurate facial capture in scenarios where visual methods falter and simultaneously safeguard user privacy. We conduct extensive experiments about both the IMU configuration and technical components to validate the effectiveness of our IMUSIC approach. Notably, IMUSIC enables various potential and novel applications, i.e., privacy-protecting facial capture, hybrid capture against occlusions, or detecting minute facial movements that are often invisible through visual cues. We will release our dataset and implementations to enrich more possibilities of facial capture and analysis in our community.
Hierarchical Delay Attribution Classification using Unstructured Text in Train Management Systems

EU directives stipulate a systematic follow-up of train delays. In Sweden, the Swedish Transport Administration registers and assigns an appropriate delay attribution code. However, this delay attribution code is assigned manually, which is a complex task. In this paper, a machine learning-based decision support for assigning delay attribution codes based on event descriptions is investigated. The text is transformed using TF-IDF, and two models, Random Forest and Support Vector Machine, are evaluated against a random uniform classifier and the classification performance of the Swedish Transport Administration. Further, the problem is modeled as both a hierarchical and flat approach. The results indicate that a hierarchical approach performs better than a flat approach. Both approaches perform better than the random uniform classifier but perform worse than the manual classification.
Forecasting Adversarial Actions Using Judgment Decomposition-Recomposition

In domains such as homeland security, cybersecurity and competitive marketing, it is frequently the case that analysts need to forecast adversarial actions that impact the problem of interest. Standard structured expert judgement elicitation techniques may fall short as they do not explicitly take into account intentionality. We present a decomposition technique based on adversarial risk analysis followed by a behavioral recomposition using discrete choice models that facilitate such elicitation process and illustrate its performance through behavioral experiments.
Efficient and Interpretable Traffic Destination Prediction using Explainable Boosting Machines

Developing accurate models for traffic trajectory predictions is crucial for achieving fully autonomous driving. Various deep neural network models have been employed to address this challenge, but their black-box nature hinders transparency and debugging capabilities in a deployed system. Glass-box models offer a solution by providing full interpretability through methods like \ac{GAM}. In this study, we evaluate an efficient additive model called \ac{EBM} for traffic prediction on three popular mixed traffic datasets: \ac{SDD}, \ac{InD}, and Argoverse. Our results show that the \ac{EBM} models perform competitively in predicting pedestrian destinations within \ac{SDD} and \ac{InD} while providing modest predictions for vehicle-dominant Argoverse dataset. Additionally, our transparent trained models allow us to analyse feature importance and interactions, as well as provide qualitative examples of predictions explanation. The full training code will be made public upon publication.
Soft Prompt Tuning for Cross-Lingual Transfer: When Less is More

Soft Prompt Tuning (SPT) is a parameter-efficient method for adapting pre-trained language models (PLMs) to specific tasks by inserting learnable embeddings, or soft prompts, at the input layer of the PLM, without modifying its parameters. This paper investigates the potential of SPT for cross-lingual transfer. Unlike previous studies on SPT for cross-lingual transfer that often fine-tune both the soft prompt and the model parameters, we adhere to the original intent of SPT by keeping the model parameters frozen and only training the soft prompt. This does not only reduce the computational cost and storage overhead of full-model fine-tuning, but we also demonstrate that this very parameter efficiency intrinsic to SPT can enhance cross-lingual transfer performance to linguistically distant languages. Moreover, we explore how different factors related to the prompt, such as the length or its reparameterization, affect cross-lingual transfer performance.
Primordial black hole formation during slow-reheating: a review

In this paper we review the possible mechanisms for the production of primordial black holes (PBHs) during a slow-reheating period in which the energy transfer of the inflaton field to standard model particles becomes effective at slow temperatures, offering a comprehensive examination of the theoretical foundations and conditions required for each of formation channel. In particular, we focus on post-inflationary scenarios where there are no self-resonances and the reheating epoch can be described by the inflaton evolving in a quadratic-like potential. In the hydrodynamical interpretation of this field during the slow-reheating epoch, the gravitational collapse of primordial fluctuations is subject to conditions on their sphericity, limits on their spin, as well as a maximum velocity dispersion. We show how to account for all conditions and show that PBHs form with different masses depending on the collapse mechanism. Finally we show, through an example, how PBH production serves to probe both the physics after primordial inflation, as well as the primordial powerspectrum at the smallest scales.