Miracle-dz/UniFAR-FTUs huggingface dataset:
Floquet theory and computational method for the optical absorption of laser-dressed solids

Recent advances in laser technology now enable engineering the electronic structure of matter through strong light-matter interactions. However, the effective physicochemical properties of these laser-dressed nonequilibrium materials are not well understood. Here we develop a general theory that now enables modeling and interpreting the linear optical absorption of solids that are dressed by light of arbitrary strength and photon energy. The theory applies to any crystalline solid and quantum materials. In the theory, the dressing of Bloch electrons by the driving laser is treated exactly using Floquet theory. The effective optical properties of this laser-dressed material are probed through a weak laser whose effects are captured to first order in perturbation theory. Remarkably, in this nonequilibrium system the time- and space-periodic Floquet-Bloch modes play the role of the pristine eigenstates of matter as the optical absorption is seen to emerge from transitions among them. We implement the theoretical framework into a code FloqticS: Floquet optics in Solids) available through Github. To isolate the emergent phenomenology, we performed computations in a model solid with a cosine-shaped lattice potential driven by strong nonresonant light. The computations recover the dynamical Franz-Keldysh effect and identify novel dramatic changes in the optical absorption upon increasing the amplitude of the driving laser. The Floquet replicas open absorption sidebands separated by integer multiples of the drive photon energy. The hybridization of the Floquet-Bloch modes, create intense low-frequency absorption and stimulated emissions, and dips in the absorption spectrum. We assign these emerging effects as purely-optical tell-tale signatures of the Floquet-Bloch modes. These advances can be used to model, control and characterize the response properties of laser-dressed materials.
Inertial-Based Navigation by Polynomial Optimization: Inertial-Magnetic Attitude Estimation

Inertial-based navigation refers to the navigation methods or systems that have inertial information or sensors as the core part and integrated a spectrum of other kinds of sensors for enhanced performance. Through a series of papers, the authors attempt to explore information blending of inertial-based navigation by the polynomial optimization method. The basic idea is to model rigid motions as finite-order polynomials and then attack the involved navigation problems by optimally solving their coefficients, considering the constraints posed by inertial sensors and others. This article proposes a continuous-time attitude estimation approach, which transforms attitude estimation into a constant parameter determination problem by polynomial optimization. Specifically, the continuous attitude is first approximated by the Chebyshev polynomial, of which the unknown Chebyshev coefficients are determined by minimizing the weighted residuals of initial conditions, dynamics, and measurements. We apply the derived estimator to the attitude estimation with the magnetic and inertial sensors. Simulation and field tests show that the estimator has much better stability and faster convergence than that of the traditional extended Kalman filter, especially in challenging large initial state error scenarios.
Assessing the Impact of Context Inference Error and Partial Observability on RL Methods for Just-In-Time Adaptive Interventions

Just-in-Time Adaptive Interventions (JITAIs) are a class of personalized health interventions developed within the behavioral science community. JITAIs aim to provide the right type and amount of support by iteratively selecting a sequence of intervention options from a pre-defined set of components in response to each individual's time varying state. In this work, we explore the application of reinforcement learning methods to the problem of learning intervention option selection policies. We study the effect of context inference error and partial observability on the ability to learn effective policies. Our results show that the propagation of uncertainty from context inferences is critical to improving intervention efficacy as context uncertainty increases, while policy gradient algorithms can provide remarkable robustness to partially observed behavioral state information.
Sparsifying Spiking Networks through Local Rhythms

It has been well-established that within conventional neural networks, many of the values produced at each layer are zero. In this work, it is demonstrated that spiking neural networks can prevent the transmission of spikes representing values close to zero using local information. This can reduce the amount of energy required for communication and computation in these networks while preserving accuracy. Additionally, this demonstrates a novel application of biologically observed spiking rhythms.
River of No Return: Graph Percolation Embeddings for Efficient Knowledge Graph Reasoning

We study Graph Neural Networks (GNNs)-based embedding techniques for knowledge graph (KG) reasoning. For the first time, we link the path redundancy issue in the state-of-the-art KG reasoning models based on path encoding and message passing to the transformation error in model training, which brings us new theoretical insights into KG reasoning, as well as high efficacy in practice. On the theoretical side, we analyze the entropy of transformation error in KG paths and point out query-specific redundant paths causing entropy increases. These findings guide us to maintain the shortest paths and remove redundant paths for minimized-entropy message passing. To achieve this goal, on the practical side, we propose an efficient Graph Percolation Process motivated by the percolation model in Fluid Mechanics, and design a lightweight GNN-based KG reasoning framework called Graph Percolation Embeddings (GraPE). GraPE outperforms previous state-of-the-art methods in both transductive and inductive reasoning tasks while requiring fewer training parameters and less inference time.
Variability of Known Exoplanet Host Stars Observed by TESS

Both direct and indirect methods of exoplanet detection rely upon detailed knowledge of the potential host stars. Such stellar characterization allows for accurate extraction of planetary properties, as well as contributing to our overall understanding of exoplanetary system architecture. In this analysis, we examine the photometry of 264 known exoplanet host stars (harboring 337 planetary companions) that were observed during the Transiting Exoplanet Survey Satellite (TESS) Prime Mission. We identify periodic signatures in the lightcurves of these stars and make possible connections to stellar pulsations and their rotation periods, and compare the stellar variability to the published planetary orbital periods. From these comparisons, we quantify the effects of stellar variability on exoplanet detection, confirming that exoplanets detection is biased toward lower variability stars, but larger exoplanets dominate the population of exoplanets around variable stars. Exoplanet detection methods represented among these systems are distinct between stellar spectral types across the main sequence, though notable outliers exist. In addition, biases present in both the sourced data from TESS and the host star selection process, which strongly influences the representation of both stellar and planetary characteristics in the final populations. We also determine whether the host stars photometric variability affects or mimics the behavior or properties of the system’s planets. These results are discussed in the context of how the behavior of the host star is responsible for how we observe exoplanet characteristics, most notably their radii and atmospheric properties, and how the activity may alter our measurements or impact the evolution of planetary properties.
Nonparametric estimation of the interventional disparity indirect effect among the exposed

In situations with non-manipulable exposures, interventions can be targeted to shift the distribution of intermediate variables between exposure groups to define interventional disparity indirect effects. In this work, we present a theoretical study of identification and nonparametric estimation of the interventional disparity indirect effect among the exposed. The targeted estimand is intended for applications examining the outcome risk among an exposed population for which the risk is expected to be reduced if the distribution of a mediating variable was changed by a (hypothetical) policy or health intervention that targets the exposed population specifically. We derive the nonparametric efficient influence function, study its double robustness properties and present a targeted minimum loss-based estimation (TMLE) procedure. All theoretical results and algorithms are provided for both uncensored and right-censored survival outcomes. With offset in the ongoing discussion of the interpretation of non-manipulable exposures, we discuss relevant interpretations of the estimand under different sets of assumptions of no unmeasured confounding and provide a comparison of our estimand to other related estimands within the framework of interventional (disparity) effects. Small-sample performance and double robustness properties of our estimation procedure are investigated and illustrated in a simulation study.
FlashBench: A lightning nowcasting framework based on the hybrid deep learning and physics-based dynamical models

Lightning strikes are a well-known danger, and are a leading cause of accidental fatality worldwide. Unfortunately, lightning hazards seldom make headlines in international media coverage because of their infrequency and the low number of casualties each incidence. According to readings from the TRMM LIS lightning sensor, thunderstorms are more common in the tropics while being extremely rare in the polar regions. To improve the precision of lightning forecasts, we develop a technique similar to LightNet's, with one key modification. We didn't just base our model off the results of preliminary numerical simulations; we also factored in the observed fields' time-dependent development. The effectiveness of the lightning forecast rose dramatically once this adjustment was made. The model was tested in a case study during a thunderstorm. Using lightning parameterization in the WRF model simulation, we compared the simulated fields. As the first of its type, this research has the potential to set the bar for how regional lightning predictions are conducted in the future because of its data-driven approach. In addition, we have built a cloud-based lightning forecast system based on Google Earth Engine. With this setup, lightning forecasts over West India may be made in real time, giving critically important information for the area.
Almost complex blow-ups and positive closed $(1,1)$-forms on $4$-dimensional almost complex manifolds

Let $(M,J)$ be a $2n$-dimensional almost complex manifold and let $x\in M$. We define the notion of almost complex blow-up of $(M,J)$ at $x$. We prove the existence of almost complex blow-ups at $x$ under suitable assumptions on the almost complex structure $J$ and we provide explicit examples of such a construction. We note that almost complex blow-ups are unique. When $(M,J)$ is a $4$-dimensional almost complex manifold, we give an obstruction on $J$ to the existence of almost complex blow-ups at a point and prove that the almost complex blow-up at a point of a compact almost K\"ahler manifold is almost K\"ahler.
A tonal scaling contrast in Majorcan Catalan interrogatives

This paper reports the application of the Categorical Perception paradigm to a pitch height contrast in the nuclear accent between yes-no and what-questions in Majorcan Catalan. Using two natural tokens produced by a female speaker, two intonational continua were created, from yes-no to what-question contour and vice versa, by shifting the peak in 4 steps of 15 Hz each. 42 Majorcan Catalan listeners participated in a two-part experiment, consisting of an identification and a discrimination task. The results from the identification task showed that it is possible to switch the perceived category by manipulating the pitch height of the leading tone. Also, Reaction Times were shorter within categories and longer between categories. Discrimination results revealed that the shift in the identification function corresponded to the peak in the discrimination function. The comparisons between obtained and predicted discrimination results indicated that discrimination can be predicted from identification results on the basis of phonetic categorization. These results confirmed that the difference in pitch height of the leading tone in nuclear accent for yes-no and what-questions in Majorcan Catalan is discrete and has a phonological character. In addition, the discrimination results revealed that Majorcan listeners are more sensitive to F0 differences when the first token is lower in frequency than the second.
Strange particle production in p+p collisions using PYTHIA8 at LHC energy

Strange particles being produced only during high-energy collisions carry important information regarding collision dynamics. Recent results of the ALICE Collaboration on strangeness enhancement in high-multiplicity p+p collisions have pointed out the importance of rope hadronization in high-energy nucleon-nucleon collisions. With the help of the PYTHIA8 model, we made an attempt to discuss the strange particle production in high-energy p+p collisions at the LHC energy in the light of diﬀerent color reconnection models and rope hadronization mechanism. The eﬀect of color reconnection ranges on diﬀerent observables is also discussed. The integrated yield of strange hadrons and bayon-to-meson ratios as a function charged particle multiplicity in p+p collisions at √ s = 13 TeV, is well described by the hadronization mechanism of color ropes together with QCD-based color reconnections scheme. The average transverse momentum, (cid:104) p T (cid:105) , and its hardening as a function of (cid:104) dN/dy (cid:105) are explained by MPI-based color reconnection mechanism with a reconnection range, RR = 3.6, whereas it is underestimated by the rope hadronization model.
“I’m fully who I am”: Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation

Warning: This paper contains examples of gender non-affirmative language which could be offensive, upsetting, and/or triggering. Transgender and non-binary (TGNB) individuals disproportionately experience discrimination and exclusion from daily life. Given the recent popularity and adoption of language generation technologies, the potential to further marginalize this population only grows. Although a multitude of NLP fairness literature focuses on illuminating and addressing gender biases, assessing gender harms for TGNB identities requires understanding how such identities uniquely interact with societal gender norms and how they differ from gender binary-centric perspectives. Such measurement frameworks inherently require centering TGNB voices to help guide the alignment between gender-inclusive NLP and whom they are intended to serve. Towards this goal, we ground our work in the TGNB community and existing interdisciplinary literature to assess how the social reality surrounding experienced marginalization of TGNB persons contributes to and persists within Open Language Generation (OLG). This social knowledge serves as a guide for evaluating popular large language models (LLMs) on two key aspects: (1) misgendering and (2) harmful responses to gender disclosure. To do this, we introduce TANGO, a dataset of template-based real-world text curated from a TGNB-oriented community. We discover a dominance of binary gender norms reflected by the models; LLMs least misgendered subjects in generated text when triggered by prompts whose subjects used binary pronouns. Meanwhile, misgendering was most prevalent when triggering generation with singular they and neopronouns. When prompted with gender disclosures, TGNB disclosure generated the most stigmatizing language and scored most toxic, on average. Our findings warrant further research on how TGNB harms manifest in LLMs and serve as a broader case study toward concretely grounding the design of gender-inclusive AI in community voices and interdisciplinary literature.
From Region to Patch: Attribute-Aware Foreground-Background Contrastive Learning for Fine-Grained Fashion Retrieval

Attribute-specific fashion retrieval (ASFR) is a challenging information retrieval task, which has attracted increasing attention in recent years. Different from traditional fashion retrieval which mainly focuses on optimizing holistic similarity, the ASFR task concentrates on attribute-specific similarity, resulting in more fine-grained and interpretable retrieval results. As the attribute-specific similarity typically corresponds to the specific subtle regions of images, we propose a Region-to-Patch Framework (RPF) that consists of a region-aware branch and a patch-aware branch to extract fine-grained attribute-related visual features for precise retrieval in a coarse-to-fine manner. In particular, the region-aware branch is first to be utilized to locate the potential regions related to the semantic of the given attribute. Then, considering that the located region is coarse and still contains the background visual contents, the patch-aware branch is proposed to capture patch-wise attribute-related details from the previous amplified region. Such a hybrid architecture strikes a proper balance between region localization and feature extraction. Besides, different from previous works that solely focus on discriminating the attribute-relevant foreground visual features, we argue that the attribute-irrelevant background features are also crucial for distinguishing the detailed visual contexts in a contrastive manner. Therefore, a novel E-InfoNCE loss based on the foreground and background representations is further proposed to improve the discrimination of attribute-specific representation. Extensive experiments on three datasets demonstrate the effectiveness of our proposed framework, and also show a decent generalization of our RPF on out-of-domain fashion images. Our source code is available at https://github.com/HuiGuanLab/RPF.
Accelerating Transformer Inference for Translation via Parallel Decoding

Autoregressive decoding limits the efficiency of transformers for Machine Translation (MT). The community proposed specific network architectures and learning-based methods to solve this issue, which are expensive and require changes to the MT model, trading inference speed at the cost of the translation quality. In this paper, we propose to address the problem from the point of view of decoding algorithms, as a less explored but rather compelling direction. We propose to reframe the standard greedy autoregressive decoding of MT with a parallel formulation leveraging Jacobi and Gauss-Seidel fixed-point iteration methods for fast inference.This formulation allows to speed up existing models without training or modifications while retaining translation quality. We present three parallel decoding algorithms and test them on different languages and models showing how the parallelization introduces a speedup up to 38% w.r. t. the standard autoregressive decoding and nearly 2x when scaling the method on parallel resources. Finally, we introduce a decoding dependency graph visualizer (DDGviz) that let us see how the model has learned the conditional dependence between tokens and inspect the decoding procedure.
A quantitative study of NLP approaches to question difficulty estimation

Recent years witnessed an increase in the amount of research on the task of Question Difficulty Estimation from Text QDET with Natural Language Processing (NLP) techniques, with the goal of targeting the limitations of traditional approaches to question calibration. However, almost the entirety of previous research focused on single silos, without performing quantitative comparisons between different models or across datasets from different educational domains. In this work, we aim at filling this gap, by quantitatively analyzing several approaches proposed in previous research, and comparing their performance on three publicly available real world datasets containing questions of different types from different educational domains. Specifically, we consider reading comprehension Multiple Choice Questions (MCQs), science MCQs, and math questions. We find that Transformer based models are the best performing across different educational domains, with DistilBERT performing almost as well as BERT, and that they outperform other approaches even on smaller datasets. As for the other models, the hybrid ones often outperform the ones based on a single type of features, the ones based on linguistic features perform well on reading comprehension questions, while frequency based features (TF-IDF) and word embeddings (word2vec) perform better in domain knowledge assessment.
WIMP decay as a possible Warm Dark Matter model

The Weakly Interacting Massive Particles (WIMPs) have long been the favoured Cold Dark Matter (CDM) candidate in the standard ΛCDM model. However, owing to great improvement in the experimental sensitivity in the past decade, some parameter space of the Supersymmetric (SUSY)-based WIMP model is ruled out. In addition, a massive stable WIMP as the CDM particle is also at variance with other astrophysical observables at small scales. We consider a model that addresses both these issues. In the model, the WIMP decays into a massive particle and radiation. We study the background evolution and the first order perturbation theory (coupled Einstein-Boltzmann equations) for this model and show that the dynamics can be captured by a single parameter r = mL/q, which is the ratio of the lighter mass and the comoving momentum of the decay particle. We incorporate the relevant equations in the existing Boltzmann code CLASS to compute the matter power spectra and Cosmic Microwave Background (CMB) angular power spectra. The decaying WIMP model is akin to a non-thermal Warm Dark Matter (WDM) model and suppresses matter power at small scales, which could alleviate several issues that plague the CDM model at small scales. We compare the predictions of the model with CMB and galaxy clustering data. As the model deviates from the ΛCDM model at small scales, the evolution of the collapse fraction of matter in the universe is compared with the high-redshift Sloan Digital Sky Survey (SDSS) HI data. Both these data sets yield r ≳ 106, which can be translated into the bounds on other parameters. In particular, we obtain the following lower bounds on the thermally-averaged self-annihilation cross-section of WIMPs, ⟨σv⟩, and the lighter mass: ⟨σv⟩ ≳ 4.9 × 10-34 cm3 sec-1 and mL ≳ 2.4 keV. The lower limit on mL is comparable to constraints on the mass of thermally-produced WDM particle. The limit on the self-annihilation cross-section greatly expands the available parameter space as compared to the stable WIMP scenario.
A Comparative Study of GAN-Generated Handwriting Images and MNIST Images using t-SNE Visualization

The quality of GAN-generated images on the MNIST dataset was explored in this paper by comparing them to the original images using t-distributed stochastic neighbor embedding (t- SNE) visualization. A GAN was trained with the dataset to generate images and the result of generating all synthetic images, the corresponding labels were saved. The dimensionality of the generated images and the original MNIST dataset was reduced using t-SNE and the resulting embeddings were plotted. The rate of the GAN-generated images was examined by comparing the t-SNE plots of the generated images and the original MNIST images. It was found that the GAN- generated images were similar to the original images but had some differences in the distribution of the features. It is believed that this study provides a useful evaluation method for assessing the quality of GAN-generated images and can help to improve their generation in the future.