Miracle-dz/UniFAR-FTUs huggingface dataset:
Reflected Spectroscopy of Small Exoplanets II: Characterization of Terrestrial Exoplanets

A space telescope capable of high-contrast imaging has been recognized as the avenue toward finding terrestrial planets around nearby Sun-like stars and characterizing their potential habitability. It is thus essential to quantify the capability of reflected light spectroscopy obtained through direct imaging for terrestrial exoplanets, and existing work focused on planetary analogs of modern Earth. Here we go beyond Earth analogs and use a Bayesian retrieval algorithm, ExoReL R , to determine what we could learn about terrestrial exoplanets from their reflected light spectra. Recognizing the potential diversity of terrestrial exoplanets, our focus is to distinguish atmospheric scenarios without any a priori knowledge of the dominant gas. We find that, while a moderate-resolution spectrum in the optical band (0.4−1.0 μm) may sufficiently characterize a modern Earth analog, it would likely result in incorrect interpretation for planets similar to Archean Earth or having CO2-dominated atmospheres. Including observations in the near-infrared bands (1.0−1.8 μm) can prevent this error, determine the main component (N2, O2, or CO2), and quantify trace gases (H2O, O3, and CH4) of the atmosphere. These results are useful to define the science requirements and design the wavelength bandwidth and observation plans of exoplanet direct imaging missions in the future.
Pragmatic Clinical Trials in the Rubric of Structural Causal Models

Explanatory studies, such as randomized controlled trials, are targeted to extract the true causal eﬀect of interventions on outcomes and are by design adjusted for covariates through randomization. On the contrary, observational studies are a representation of events that occurred without intervention. Both can be illustrated using the Structural Causal Model (SCM), and do-calculus can be employed to estimate the causal eﬀects. Pragmatic clinical trials (PCT) fall between these two ends of the trial design spectra and are thus hard to deﬁne. Due to its pragmatic nature, no standardized representation of PCT through SCM has been yet established. In this paper, we approach this problem by proposing a generalized representation of PCT under the rubric of structural causal models (SCM). We discuss diﬀerent analysis techniques commonly employed in PCT using the proposed graphical model, such as intention-to-treat, as-treated, and per-protocol analysis. To show the application of our proposed approach, we leverage an experimental dataset from a pragmatic clinical trial. Our proposition of SCM through PCT creates a pathway to leveraging do-calculus and related mathematical operations on clinical datasets.
A Babcock–Leighton-type Solar Dynamo Operating in the Bulk of the Convection Zone

The toroidal magnetic field is assumed to be generated in the tachocline in most Babcock–Leighton (BL)-type solar dynamo models, in which the poloidal field is produced by the emergence and subsequent dispersal of sunspot groups. However, magnetic activity of fully convective stars and MHD simulations of global stellar convection have recently raised serious doubts regarding the importance of the tachocline in the generation of the toroidal field. In this study, we aim to develop a new BL-type dynamo model, in which the dynamo operates mainly within the bulk of the convection zone. Our 2D model includes the effect of solar-like differential rotation, one-cell meridional flow, near-surface radial pumping, strong turbulent diffusion, BL-type poloidal source, and nonlinear back-reaction of the magnetic field on its source with a vertical outer boundary condition. The model leads to a simple dipolar configuration of the poloidal field that has the dominant latitudinal component, which is wound up by the latitudinal shear within the bulk of the convection zone to generate the toroidal flux. As a result, the tachocline plays a negligible role in the model. The model reproduces the basic properties of the solar cycle, including (a) approximately 11 yr cycle period and 18 yr extended cycle period; (b) equatorward propagation of the antisymmetric toroidal field starting from high latitudes; and (c) polar field evolution that is consistent with observations. Our model opens the possibility for a paradigm shift in understanding the solar cycle to transition from the classical flux transport dynamo.
Czech Dataset for Cross-lingual Subjectivity Classification

In this paper, we introduce a new Czech subjectivity dataset of 10k manually annotated subjective and objective sentences from movie reviews and descriptions. Our prime motivation is to provide a reliable dataset that can be used with the existing English dataset as a benchmark to test the ability of pre-trained multilingual models to transfer knowledge between Czech and English and vice versa. Two annotators annotated the dataset reaching 0.83 of the Cohen’s K inter-annotator agreement. To the best of our knowledge, this is the first subjectivity dataset for the Czech language. We also created an additional dataset that consists of 200k automatically labeled sentences. Both datasets are freely available for research purposes. Furthermore, we fine-tune five pre-trained BERT-like models to set a monolingual baseline for the new dataset and we achieve 93.56% of accuracy. We fine-tune models on the existing English dataset for which we obtained results that are on par with the current state-of-the-art results. Finally, we perform zero-shot cross-lingual subjectivity classification between Czech and English to verify the usability of our dataset as the cross-lingual benchmark. We compare and discuss the cross-lingual and monolingual results and the ability of multilingual models to transfer knowledge between languages.
Investigating writing style as a contributor to gender gaps in science and technology

While universalism is a foundational principle of science, a growing stream of research finds that scientific contributions are evaluated differently depending on the gender of the author, with women tending to receive fewer citations relative to men, even for work of comparable quality. Strikingly, research also suggests that these gender gaps are visible even under blinded review, wherein the evaluator is not aware of the gender of the author. In this article, we consider whether gender differences in writing styles -- how men and women communicate their work -- may contribute to these observed gender gaps. We ground our investigation in a previously established framework for characterizing the linguistic style of written text, which distinguishes between two sets of features -- informational (i.e., features that emphasize facts) and involved (i.e., features that emphasize relationships). Using a large, matched sample of academic papers and patents, we find significant differences in writing style by gender; women use more involved features in their writing, a pattern that holds universally across fields. The magnitude of the effect varies across fields, with larger gender differences observed in the social sciences and arts humanities and smaller gaps in the physical sciences and technology. Subsequently, we show that gender differences in writing style may have parallels in reading preferences; papers and patents with more informational features tend to be cited more by men, while those with more involved features tend to be cited more by women, even after controlling for the gender of the author, inventor, and patent attorney. Our findings suggest that formal written text is not devoid of personal character, which could contribute to bias in evaluation, thereby compromising the norm of universalism.
Fast Sampling of Diffusion Models with Exponential Integrator

The past few years have witnessed the great success of Diffusion models~(DMs) in generating high-fidelity samples in generative modeling tasks. A major limitation of the DM is its notoriously slow sampling procedure which normally requires hundreds to thousands of time discretization steps of the learned diffusion process to reach the desired accuracy. Our goal is to develop a fast sampling method for DMs with a much less number of steps while retaining high sample quality. To this end, we systematically analyze the sampling procedure in DMs and identify key factors that affect the sample quality, among which the method of discretization is most crucial. By carefully examining the learned diffusion process, we propose Diffusion Exponential Integrator Sampler~(DEIS). It is based on the Exponential Integrator designed for discretizing ordinary differential equations (ODEs) and leverages a semilinear structure of the learned diffusion process to reduce the discretization error. The proposed method can be applied to any DMs and can generate high-fidelity samples in as few as 10 steps. In our experiments, it takes about 3 minutes on one A6000 GPU to generate $50k$ images from CIFAR10. Moreover, by directly using pre-trained DMs, we achieve the state-of-art sampling performance when the number of score function evaluation~(NFE) is limited, e.g., 4.17 FID with 10 NFEs, 3.37 FID, and 9.74 IS with only 15 NFEs on CIFAR10. Code is available at https://github.com/qsh-zh/deis
Wide and deep neural networks achieve consistency for classification

Significance While neural networks used in practice are often very deep, the benefit of depth is not well understood. Interestingly, it is known that increasing depth is often harmful for regression tasks. In this work, we show that, in contrast to regression, very deep networks can be Bayes optimal for classification. In particular, we provide simple and explicit activation functions that can be used with standard neural network architectures to achieve consistency. This work provides fundamental understanding of classification using deep neural networks, and we envision it will help guide the design of future neural network architectures.
From noncommutative geometry to random matrix theory

We review recent progress in the analytic study of random matrix models suggested by noncommutative geometry. One considers fuzzy spectral triples where the space of possible Dirac operators is assigned a probability distribution. These ensembles of Dirac operators are constructed as toy models of Euclidean quantum gravity on finite noncommutative spaces and display many interesting properties. The ensembles exhibit spectral phase transitions, and near these phase transitions they show manifold-like behavior. In certain cases one can recover Liouville quantum gravity in the double scaling limit. We highlight examples where bootstrap techniques, Coulomb gas methods, and Topological Recursion are applicable.
Participatory Sensing for Localization of a GNSS Jammer

GNSS receivers are vulnerable to jamming and spoofing attacks, and numerous such incidents have been reported worldwide in the last decade. It is important to detect attacks fast and localize attackers, which can be hard if not impossible without dedicated sensing infrastructure. The notion of participatory sensing, or crowdsensing, is that a large ensemble of voluntary contributors provides the measurements, rather than relying on dedicated sensing infrastructure. This work considers embedded GNSS receivers to provide measurements for participatory jamming detection and localization. Specifically, this work proposes a novel jamming localization algorithm, based on participatory sensing, that exploits AGC and $C / N_{0}$ estimates from commercial GNSS receivers. The proposed algorithm does not require knowledge of the jamming power nor of the channels, but automatically estimates all parameters. The algorithm is shown to outperform similar state-of-the-art localization algorithms in relevant scenarios.
Pore-resolved simulations of turbulent boundary layer flow over permeable and impermeable sediment beds

Pore-resolved direct numerical simulations of turbulent open channel ﬂow are performed comparing the structure and dynamics of turbulence over impermeable rough and smooth walls to a porous sediment bed at permeability Reynolds number ( Re K ) of 2.6, representative of aquatic beds. Four conﬁgurations are investigated; namely, (i) permeable bed with randomly packed sediment grains, (ii) an impermeable-wall with full layer of roughness elements matching the top layer of the sediment bed, (iii) an impermeable-wall with half layer of roughness elements , and (iv) a smooth wall. A double-averaging methodology is used to compute the mean velocity, Reynolds stresses, form-induced stresses, and turbulent kinetic energy budget. It is observed that the mean velocity, Reynolds stresses, and form-induced pressure-velocity correlations representing upwelling and downwelling ﬂuxes are similar in magnitude for the permeable-bed and impermeable-full layer wall cases. However, for the impermeable-half layer case, the wall blocking eﬀect results in higher streamwise and lower wall-normal stresses compared to the permeable-bed case. Bed roughness increases Reynolds shear stress whereas permeability has minimal inﬂuence. Bed permeability, in contrast to Reynolds stresses, signiﬁcantly inﬂuences form-induced shear stress. Bed shear stress statistics show that probability of extreme events increases in permeable bed and rough wall cases as compared to the smooth wall. Findings suggest that bed permeability can have signiﬁcant impact on modeling of hyporheic exchange and its eﬀect on bed shear and pressure
Goldilocks-curriculum Domain Randomization and Fractal Perlin Noise with Application to Sim2Real Pneumonia Lesion Detection

A computer-aided detection (CAD) system based on machine learning is expected to assist radiologists in making a diagnosis. It is desirable to build CAD systems for the various types of diseases accumulating daily in a hospital. An obstacle in developing a CAD system for a disease is that the number of medical images is typically too small to improve the performance of the machine learning model. In this paper, we aim to explore ways to address this problem through a sim2real transfer approach in medical image fields. To build a platform to evaluate the performance of sim2real transfer methods in the field of medical imaging, we construct a benchmark dataset that consists of $101$ chest X-images with difficult-to-identify pneumonia lesions judged by an experienced radiologist and a simulator based on fractal Perlin noise and the X-ray principle for generating pseudo pneumonia lesions. We then develop a novel domain randomization method, called Goldilocks-curriculum domain randomization (GDR) and evaluate our method in this platform.
H2H: heterogeneous model to heterogeneous system mapping with computation and communication awareness

The complex nature of real-world problems calls for heterogeneity in both machine learning (ML) models and hardware systems. The heterogeneity in ML models comes from multi-sensor perceiving and multi-task learning, i.e., multi-modality multi-task (MMMT), resulting in diverse deep neural network (DNN) layers and computation patterns. The heterogeneity in systems comes from diverse processing components, as it becomes the prevailing method to integrate multiple dedicated accelerators into one system. Therefore, a new problem emerges: heterogeneous model to heterogeneous system mapping (H2H). While previous mapping algorithms mostly focus on efficient computations, in this work, we argue that it is indispensable to consider computation and communication simultaneously for better system efficiency. We propose a novel H2H mapping algorithm with both computation and communication awareness; by slightly trading computation for communication, the system overall latency and energy consumption can be largely reduced. The superior performance of our work is evaluated based on MAESTRO modeling, demonstrating 15%-74% latency reduction and 23%-64% energy reduction compared with existing computation-prioritized mapping algorithms. Code is publicly available at https://github.com/xyzxinyizhang/H2H.
Leaner and Faster: Two-Stage Model Compression for Lightweight Text-Image Retrieval

Current text-image approaches (e.g., CLIP) typically adopt dual-encoder architecture using pre-trained vision-language representation. However, these models still pose non-trivial memory requirements and substantial incremental indexing time, which makes them less practical on mobile devices. In this paper, we present an effective two-stage framework to compress large pre-trained dual-encoder for lightweight text-image retrieval. The resulting model is smaller (39% of the original), faster (1.6x/2.9x for processing image/text respectively), yet performs on par with or better than the original full model on Flickr30K and MSCOCO benchmarks. We also open-source an accompanying realistic mobile image search application.
Detecting Textual Adversarial Examples Based on Distributional Characteristics of Data Representations

Although deep neural networks have achieved state-of-the-art performance in various machine learning tasks, adversarial examples, constructed by adding small non-random perturbations to correctly classified inputs, successfully fool highly expressive deep classifiers into incorrect predictions. Approaches to adversarial attacks in natural language tasks have boomed in the last five years using character-level, word-level, phrase-level, or sentence-level textual perturbations. While there is some work in NLP on defending against such attacks through proactive methods, like adversarial training, there is to our knowledge no effective general reactive approaches to defence via detection of textual adversarial examples such as is found in the image processing literature. In this paper, we propose two new reactive methods for NLP to fill this gap, which unlike the few limited application baselines from NLP are based entirely on distribution characteristics of learned representations”:" we adapt one from the image processing literature (Local Intrinsic Dimensionality (LID)), and propose a novel one (MultiDistance Representation Ensemble Method (MDRE)). Adapted LID and MDRE obtain state-of-the-art results on character-level, word-level, and phrase-level attacks on the IMDB dataset as well as on the later two with respect to the MultiNLI dataset. For future research, we publish our code .
The Analysis of a Proposed Experiment to Measure the Speed of Gravity in Short Distances

In order to investigate the speed of gravitational signals travelling in air or through a different medium two experiments were designed. One of the experiments contains 2 masses rotating at very high speed and in the other experiment a sapphire bar will vibrate, in both cases they will emit a periodic tidal gravitational signal and one sapphire device that behaves as a detector, which are suspended in vacuum and cooled down to 4.2 K will act as a detector. The vibrational amplitude of the sapphire detector device is measured by an microwave signal with ultralow phase-noise that uses resonance in the whispering gallery modes inside the detector device. Sapphire has a quite high mechanical Q and electrical Q which implies a very narrow detection band thus reducing the detection sensitivity. A new detector shape for the detector device is presented in this work, yielding a detection band of about half of the device vibrational frequency. With the aid of a Finite Element Program the normal mode frequencies of the detector can be calculated with high precision. The results show a similar expected sensitivity between the two experimental setup, but the experiment with the vibration masses is more stable in frequency then it is chosen for the experimental setup to measure the speed of gravity in short distances. Then a more precise analysis is made with this experiment reaching a signal-noise ratio of 10 at a frequency of 5000 Hz.
Hometown Heroes? Does Proximity To One's Home Affect Performance in Ontario University Athletics?

Every year, university coaches seek to recruit ‘talented’ athletes to add to their rosters with the aim of improving their team. Some coaches take considerable strides to recruit the best athletes from across their region, province and/or across the country. Among many issues to consider, recruited athletes must decide whether to attend schools of closer or farther proximity from their hometown. This study considered whether proximity, defined as the geographic distance between an institution and an individual’s hometown, influences Ontario student-athletes’ university selection and whether this variable influences team and/or individual performance. Data from the Ontario University Athletics (OUA) were collected between 2009 and 2013 (1764 female athletes and 1873 male athletes). Results indicated that athletes were more likely to attend universities greater than 80km from home. However, athletes who attended local universities were more successful and teams comprised of local athletes were more successful as well.
A Sampling Theorem for Exact Identification of Continuous-time Nonlinear Dynamical Systems

Low sampling frequency challenges the exact identification of continuous-time (CT) dynamical systems from sampled data, even when its model is identifiable. A necessary and sufficient condition is proposed– which is built from Koopman operator– to the exact identification of the CT system from sampled data. This condition gives a Nyquist-Shannon-like critical frequency for exact identification of CT nonlinear dynamical systems with a set of valid Koopman eigenfunctions: 1) it establishes a sufficient condition for a sampling frequency that permits a discretized sequence of samples to discover the underlying system and 2) it also establishes a necessary condition for a sampling frequency that leads to system aliasing that the underlying system is indistinguishable. The theoretical criterion has been demonstrated on a number of simulated examples, including linear systems, nonlinear systems with equilibria, and limit cycles.