Miracle-dz/UniFAR-FTUs huggingface dataset:
Subwavelength resonant antennas enhancing electromagnetic energy harvesting

In this work, an electromagnetic energy harvester operating at microwave frequencies is designed based on a cut- wire metasurface. This metamaterial is known to contain a quasistatic electric dipole resonator leading to a strong resonant electric response when illuminated by electromagnetic fields.1 Starting from an equivalent electrical circuit, we analytically design the parameters of the system to tune the resonance frequency of the harvester at the desired frequency band. Subsequently, we compare these results with numerical simulations, which have been obtained using finite elements numerical simulations. Finally, we optimize the design by investigating the best arrangement for energy harvesting by coupling in parallel and in series many single layers of cut-wire metasurfaces. We also discuss the implementation of different geometries and sizes of the cut-wire metasurface for achieving different center frequencies and bandwidths.
Establishment of image‐guided radiotherapy of orthotopic hepatocellular carcinoma mouse model

Hepatocellular carcinoma (HCC) is the most common type of liver cancer. Recently, developments in radiotherapy technology have led to radiotherapy becoming one of the main therapeutics of HCC. Therefore, a suitable animal model for radiotherapy of the orthotopic HCC mouse model is urgently needed.
An integrated RF energy–harvesting system with broad input voltage range and high power conversion efficiency

Significance Energy harvesting technologies provide a battery-free and sustainable power solution for biomedical electronics, small wearable devices, and other applications where battery dependence is not ideal or preferred. Compared with other popular energy-harvesting studies with piezo or solar energy, RF (radio frequency) energy is easier to access and transmit. RF energy–harvesting can provide higher integration levels and lower fabrication costs and thus be adapted to more applications. We developed a highly integrated, high-efficiency RF energy–harvesting system that scavenges ambient RF energy and converts it into usable DC power which can be further stored in a storage element, such as a supercapacitor.
Alignment–Rheology Relationship of Biosourced Rod-Like Colloids and Polymers under Flow

Fluids composed of biosourced rod-like colloids (RC) and rod-like polymers (RP) have been extensively studied due to various promising applications relying on their flow-induced orientation (e.g., fiber spinning). However, the relationship between RC and RP alignment and the resulting rheological properties is unclear due to experimental challenges. We investigate the alignment–rheology relationship for a variety of biosourced RC and RP, including cellulose-based particles, filamentous viruses, and xanthan gum, by simultaneous measurements of the shear viscosity and fluid anisotropy under rheometric shear flows. For each system, the RC and RP contribution to the fluid viscosity, captured by the specific viscosity ηsp, follows a universal trend with the extent of the RC and RP alignment independent of concentration. We further exploit this unique rheological-structural link to retrieve a dimensionless parameter (β) directly proportional to ηsp at zero shear rate (η0,sp), a parameter often difficult to access from experimental rheometry for RC and RP with relatively long contour lengths. Our results highlight the unique link between the flow-induced structural and rheological changes occurring in RC and RP fluids. We envision that our findings will be relevant in building and testing microstructural constitutive models to predict the flow-induced structural and rheological evolution of fluids containing RC and RP.
Epidemiology, Modern Diagnostics, and the Management of Mucorales Infections

Mucormycosis is an uncommon, yet deadly invasive fungal infection caused by the Mucorales moulds. These pathogens are a WHO-assigned high-priority pathogen group, as mucormycosis incidence is increasing, and there is unacceptably high mortality with current antifungal therapies. Current diagnostic methods have inadequate sensitivity and specificity and may have issues with accessibility or turnaround time. Patients with diabetes mellitus and immune compromise are predisposed to infection with these environmental fungi, but COVID-19 has established itself as a new risk factor. Mucorales also cause healthcare-associated outbreaks, and clusters associated with natural disasters have also been identified. Robust epidemiological surveillance into burden of disease, at-risk populations, and emerging pathogens is required. Emerging serological and molecular techniques may offer a faster route to diagnosis, while newly developed antifungal agents show promise in preliminary studies. Equitable access to these emerging diagnostic techniques and antifungal therapies will be key in identifying and treating mucormycosis, as delayed initiation of therapy is associated with higher mortality.
Accuracy of Digital Surface Models and Orthophotos Derived from Unmanned Aerial Vehicle Photogrammetry

AbstractThis paper explores the influence of flight altitude, terrain morphology, and the number of ground control points (GCPs) on digital surface model (DSM) and orthoimage accuracies obtained with unmanned aerial vehicle (UAV) photogrammetry. For this study, 60 photogrammetric projects were carried out considering five terrain morphologies, four flight altitudes (i.e., 50, 80, 100, and 120 m), and three different numbers of GCPs (i.e., 3, 5, and 10). The UAV was a rotatory wing platform with eight motors, and the sensor was a nonmetric mirrorless reflex camera. The root-mean-square error (RMSE) was used to assess the accuracy of the DSM (Z component) and orthophotos (X, Y, and XY components RMSEX, RMSEY, and RMSEXY, respectively). The results show that RMSEX, RMSEY, and RMSEXY were not influenced by flight altitude or terrain morphology. For horizontal accuracy, differences between terrain morphologies were observed only when 5 or 10 GCPs were used, which were the best accuracies for the flattest morph.
Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring

In credit scoring, machine learning models are known to outperform standard parametric models. As they condition access to credit, banking supervisors and internal model validation teams need to monitor their predictive performance and to identify the features with the highest impact on performance. To facilitate this, we introduce the XPER methodology to decompose a performance metric (e.g., AUC, $R^2$) into specific contributions associated with the various features of a classification or regression model. XPER is theoretically grounded on Shapley values and is both model-agnostic and performance metric-agnostic. Furthermore, it can be implemented either at the model level or at the individual level. Using a novel dataset of car loans, we decompose the AUC of a machine-learning model trained to forecast the default probability of loan applicants. We show that a small number of features can explain a surprisingly large part of the model performance. Furthermore, we find that the features that contribute the most to the predictive performance of the model may not be the ones that contribute the most to individual forecasts (SHAP). We also show how XPER can be used to deal with heterogeneity issues and significantly boost out-of-sample performance.
Understanding Client Reactions in Online Mental Health Counseling

Communication success relies heavily on reading participants’ reactions. Such feedback is especially important for mental health counselors, who must carefully consider the client’s progress and adjust their approach accordingly. However, previous NLP research on counseling has mainly focused on studying counselors’ intervention strategies rather than their clients’ reactions to the intervention. This work aims to fill this gap by developing a theoretically grounded annotation framework that encompasses counselors’ strategies and client reaction behaviors. The framework has been tested against a large-scale, high-quality text-based counseling dataset we collected over the past two years from an online welfare counseling platform. Our study show how clients react to counselors’ strategies, how such reactions affect the final counseling outcomes, and how counselors can adjust their strategies in response to these reactions. We also demonstrate that this study can help counselors automatically predict their clients’ states.
Automatic Annotation of Direct Speech in Written French Narratives

The automatic annotation of direct speech (AADS) in written text has been often used in computational narrative understanding. Methods based on either rules or deep neural networks have been explored, in particular for English or German languages. Yet, for French, our target language, not many works exist. Our goal is to create a unified framework to design and evaluate AADS models in French. For this, we consolidated the largest-to-date French narrative dataset annotated with DS per word; we adapted various baselines for sequence labelling or from AADS in other languages; and we designed and conducted an extensive evaluation focused on generalisation. Results show that the task still requires substantial efforts and emphasise characteristics of each baseline. Although this framework could be improved, it is a step further to encourage more research on the topic.
SSC-RS: Elevate LiDAR Semantic Scene Completion with Representation Separation and BEV Fusion

Semantic scene completion (SSC) jointly predicts the semantics and geometry of the entire 3D scene, which plays an essential role in 3D scene understanding for autonomous driving systems. SSC has achieved rapid progress with the help of semantic context in segmentation. However, how to effectively exploit the relationships between the semantic context in semantic segmentation and geometric structure in scene completion remains under exploration. In this paper, we propose to solve outdoor SSC from the perspective of representation separation and BEV fusion. Specifically, we present the network, named SSC-RS, which uses separate branches with deep supervision to explicitly disentangle the learning procedure of the semantic and geometric representations. And a BEV fusion network equipped with the proposed Adaptive Representation Fusion (ARF) module is presented to aggregate the multi-scale features effectively and efficiently. Due to the low computational burden and powerful representation ability, our model has good generality while running in real-time. Extensive experiments on SemanticKITTI demonstrate our SSC-RS achieves state-of-the-art performance. Code is available at https://github.com/Jieqianyu/SSC-RS.git.
Adaptive Annealed Importance Sampling with Constant Rate Progress

Annealed Importance Sampling (AIS) synthesizes weighted samples from an intractable distribution given its unnormalized density function. This algorithm relies on a sequence of interpolating distributions bridging the target to an initial tractable distribution such as the well-known geometric mean path of unnormalized distributions which is assumed to be suboptimal in general. In this paper, we prove that the geometric annealing corresponds to the distribution path that minimizes the KL divergence between the current particle distribution and the desired target when the feasible change in the particle distribution is constrained. Following this observation, we derive the constant rate discretization schedule for this annealing sequence, which adjusts the schedule to the difficulty of moving samples between the initial and the target distributions. We further extend our results to $f$-divergences and present the respective dynamics of annealing sequences based on which we propose the Constant Rate AIS (CR-AIS) algorithm and its efficient implementation for $\alpha$-divergences. We empirically show that CR-AIS performs well on multiple benchmark distributions while avoiding the computationally expensive tuning loop in existing Adaptive AIS.
MindDial: Belief Dynamics Tracking with Theory-of-Mind Modeling for Situated Neural Dialogue Generation

Humans talk in free-form while negotiating the expressed meanings or common ground. Despite the impressive conversational abilities of the large generative language models, they do not consider the individual differences in contextual understanding in a shared situated environment. In this work, we propose MindDial, a novel conversational framework that can generate situated free-form responses to negotiate common ground. We design an explicit mind module that can track three-level beliefs -- the speaker's belief, the speaker's prediction of the listener's belief, and the common belief based on the gap between the first two. Then the speaking act classification head will decide to continue to talk, end this turn, or take task-related action. We augment a common ground alignment dataset MutualFriend with belief dynamics annotation, of which the goal is to find a single mutual friend based on the free chat between two agents. Experiments show that our model with mental state modeling can resemble human responses when aligning common ground meanwhile mimic the natural human conversation flow. The ablation study further validates the third-level common belief can aggregate information of the first and second-order beliefs and align common ground more efficiently.
Gain Confidence, Reduce Disappointment: A New Approach to Cross-Validation for Sparse Regression

Ridge regularized sparse regression involves selecting a subset of features that explains the relationship between a design matrix and an output vector in an interpretable manner. To select the sparsity and robustness of linear regressors, techniques like leave-one-out cross-validation are commonly used for hyperparameter tuning. However, cross-validation typically increases the cost of sparse regression by several orders of magnitude. Additionally, validation metrics are noisy estimators of the test-set error, with different hyperparameter combinations giving models with different amounts of noise. Therefore, optimizing over these metrics is vulnerable to out-of-sample disappointment, especially in underdetermined settings. To address this, we make two contributions. First, we leverage the generalization theory literature to propose confidence-adjusted variants of leave-one-out that display less propensity to out-of-sample disappointment. Second, we leverage ideas from the mixed-integer literature to obtain computationally tractable relaxations of confidence-adjusted leave-one-out, thereby minimizing it without solving as many MIOs. Our relaxations give rise to an efficient coordinate descent scheme which allows us to obtain significantly lower leave-one-out errors than via other methods in the literature. We validate our theory by demonstrating we obtain significantly sparser and comparably accurate solutions than via popular methods like GLMNet and suffer from less out-of-sample disappointment. On synthetic datasets, our confidence adjustment procedure generates significantly fewer false discoveries, and improves out-of-sample performance by 2-5% compared to cross-validating without confidence adjustment. Across a suite of 13 real datasets, a calibrated version of our procedure improves the test set error by an average of 4% compared to cross-validating without confidence adjustment.
Same-sign taus signatures of maximally flavor-violating scalars at the LHC

We explore single and double flavor-violating scalar (flavon) production at the 13 and 14 TeV LHC in an effective field theory formulation where flavons always change the flavor of the Standard Model fermions. When those scalars couple to mass, their flavor-changing couplings to top quarks and tau leptons are favored. Focusing on the mass region below the top-quark mass, we find couplings that fit the muon $(g-2)$ discrepancy and avoid several current experimental constraints. We determine the potential of the LHC to exclude or discover such a new physics scenario with clean signatures consisting of same-sign tau leptons and the simultaneous observation of resonances in the tau plus electron or muon invariant mass. We found that in the double production mode, effective couplings down to order $10^{-2}$ TeV$^{-1}$ can be probed for flavon masses in the 10--170 GeV range at the 14 TeV HL-LHC, but couplings down to 0.1 TeV$^{-1}$ can already be excluded at 95\% confidence level with data collected from the 13 TeV LHC in the same mass interval. We also explore the impact of sizeable diagonal flavon couplings on the prospects of LHC for the signals we propose.
No Small-scale Radio Jets Here: Multiepoch Observations of Radio Continuum Structures in NGC 1068 with the VLBA

We present recent Very Long Baseline Array (VLBA) 5 GHz radio observations of the nearby, luminous Seyfert 2 galaxy NGC 1068 for comparison to similar VLBA observations made on 1997 April 26. By cross-correlating the positions of emitting regions across both epochs, we find that spatially resolved extranuclear radio knots in this system have subrelativistic transverse speeds (v ≲0.1c). We discuss sources of the observed knots and how the radio emission relates to additional phases of gas in the central ∼150 pc of this system. We suggest that the most likely explanation for the observed emission is synchrotron radiation formed by shocked host media via interactions between active galactic nucleus winds and the host environment.
Scaling and Resizing Symmetry in Feedforward Networks

Weights initialization in deep neural networks have a strong impact on the speed of converge of the learning map. Recent studies have shown that in the case of random initializations, a chaos/order phase transition occur in the space of variances of random weights and biases. Experiments then had shown that large improvements can be made, in terms of the training speed, if a neural network is initialized on values along the critical line of such phase transition. In this contribution, we show evidence that the scaling property exhibited by physical systems at criticality, is also present in untrained feedforward networks with random weights initialization at the critical line. Additionally, we suggest an additional data-resizing symmetry, which is directly inherited from the scaling symmetry at criticality.
Value-aware Importance Weighting for Off-policy Reinforcement Learning

Importance sampling is a central idea underlying off-policy prediction in reinforcement learning. It provides a strategy for re-weighting samples from a distribution to obtain unbiased estimates under another distribution. However, importance sampling weights tend to exhibit extreme variance, often leading to stability issues in practice. In this work, we consider a broader class of importance weights to correct samples in off-policy learning. We propose the use of $\textit{value-aware importance weights}$ which take into account the sample space to provide lower variance, but still unbiased, estimates under a target distribution. We derive how such weights can be computed, and detail key properties of the resulting importance weights. We then extend several reinforcement learning prediction algorithms to the off-policy setting with these weights, and evaluate them empirically.
RansomAI: AI-Powered Ransomware for Stealthy Encryption

Cybersecurity solutions have shown promising performance when detecting ransomware samples that use fixed algorithms and encryption rates. However, due to the current explosion of Artificial Intelligence (AI), sooner than later, ransomware, and malware in general, will incorporate AI techniques to intelligently and dynamically adapt its behavior to be undetected. It might result in ineffective and obsolete cybersecurity solutions, but the literature lacks AI-powered ransomware samples to verify it. Thus, this work proposes RansomAI, a Reinforcement Learning-based framework that can be integrated into existing ransomware samples to adapt their encryption behavior and stay stealthy while encrypting files. RansomAI presents an agent that learns the best encryption algorithm, rate, and duration that minimizes its detection (using a reward mechanism and a fingerprinting intelligent detection system) while maximizing its damage. The proposed framework was validated with Ransomware-PoC, a ransomware that infected a Raspberry Pi 4 acting as a crowdsensor. A pool of experiments with Deep Q-Learning and Isolation Forest (deployed on the agent and detection system, respectively) has demonstrated that RansomAI evades the detection of Ransomware-PoC affecting the Raspberry Pi 4 in a few minutes with >90% accuracy.
Coupling parameter and particle dynamics for adaptive sampling in Neural Galerkin schemes

Training nonlinear parametrizations such as deep neural networks to numerically approximate solutions of partial differential equations is often based on minimizing a loss that includes the residual, which is analytically available in limited settings only. At the same time, empirically estimating the training loss is challenging because residuals and related quantities can have high variance, especially for transport-dominated and high-dimensional problems that exhibit local features such as waves and coherent structures. Thus, estimators based on data samples from un-informed, uniform distributions are inefficient. This work introduces Neural Galerkin schemes that estimate the training loss with data from adaptive distributions, which are empirically represented via ensembles of particles. The ensembles are actively adapted by evolving the particles with dynamics coupled to the nonlinear parametrizations of the solution fields so that the ensembles remain informative for estimating the training loss. Numerical experiments indicate that few dynamic particles are sufficient for obtaining accurate empirical estimates of the training loss, even for problems with local features and with high-dimensional spatial domains.
Learning-on-the-Drive: Self-supervised Adaptation of Visual Offroad Traversability Models

Autonomous off-road driving requires understanding traversability, which refers to the suitability of a given terrain to drive over. When offroad vehicles travel at high speed ($>10m/s$), they need to reason at long-range ($50m$-$100m$) for safe and deliberate navigation. Moreover, vehicles often operate in new environments and under different weather conditions. LiDAR provides accurate estimates robust to visual appearances, however, it is often too noisy beyond 30m for fine-grained estimates due to sparse measurements. Conversely, visual-based models give dense predictions at further distances but perform poorly at all ranges when out of training distribution. To address these challenges, we present ALTER, an offroad perception module that adapts-on-the-drive to combine the best of both sensors. Our visual model continuously learns from new near-range LiDAR measurements. This self-supervised approach enables accurate long-range traversability prediction in novel environments without hand-labeling. Results on two distinct real-world offroad environments show up to 52.5% improvement in traversability estimation over LiDAR-only estimates and 38.1% improvement over non-adaptive visual baseline.
An Observational Upper Limit on the Rate of Gamma-Ray Bursts with Neutron Star–Black Hole Merger Progenitors

Compact-object binary mergers consisting of one neutron star and one black hole (NSBHs) have long been considered promising progenitors for gamma-ray bursts, whose central engine remains poorly understood. Using gravitational-wave constraints on the population-level NSBH mass and spin distributions we find that at most 20 Gpc−3 yr−1 of gamma-ray bursts in the local universe can have NSBH progenitors.