Miracle-dz/UniFAR-FTUs huggingface dataset:
Not so fuzzy: excluding FDM with sizes and stellar kinematics of ultra-faint dwarf galaxies

We use observations of ultra-faint dwarf (UFD) galaxies to constrain the particle mass of ultra-light dark matter. Potential fluctuations created by wave interference in virialized"fuzzy"dark matter (FDM) halos dynamically heat stellar orbits in UFDs, some of which exhibit velocity dispersions of $\lesssim$ 3 km/s and sizes $\lesssim$ 40 pc. Using simulations of FDM halos, and existing measurements of sizes and stellar radial velocities in Segue 1 and Segue 2 UFDs, we derive a lower limit on the dark matter particle mass of $m_{fdm}>3\times 10^{-19}$ eV at 99% confidence, marginalized over host halo circular velocity. This constraint is conservative as it is derived under the assumption that soliton heating is negligible, and that no other sources of non-FDM dynamical heating of stars operate to increase velocity dispersion. It can potentially be strengthened by future spectroscopic observations of additional stars in ultra-faint galaxies and by tightening theoretical constraints on the soliton size-halo mass relation. However, even the current conservative lower limit on the FDM mass makes this model indistinguishable from Cold Dark Matter at the scales probed by existing astronomical observations.
How to Accomplish Inflation and a Constant Dark Energy Density

It is commonly assumed that the Lagrangian of multi-ﬁeld theories of gravity contains the sum of kinetic terms of scalar ﬁelds. However, we propose here the Lagrangian contains not the sum but the quotient of kinetic terms. With this novel change, we ﬁnd the inﬂationary universe can be acquired with two scalars without the slow-roll approximation. On the other hand, a constant dark energy density can also be achieved with two scalars while without the need of Einstein cosmological constant.
Reprogramming FairGANs with Variational Auto-Encoders: A New Transfer Learning Model

Fairness-aware GANs (FairGANs) exploit the mechanisms of Generative Adversarial Networks (GANs) to impose fairness on the generated data, freeing them from both disparate impact and disparate treatment. Given the model's advantages and performance, we introduce a novel learning framework to transfer a pre-trained FairGAN to other tasks. This reprogramming process has the goal of maintaining the FairGAN's main targets of data utility, classification utility, and data fairness, while widening its applicability and ease of use. In this paper we present the technical extensions required to adapt the original architecture to this new framework (and in particular the use of Variational Auto-Encoders), and discuss the benefits, trade-offs, and limitations of the new model.
Triple Band Y Slotted Rectangular Microstrip Patch Antenna for WLAN/WiMAX Applications

A new, compact, simple and slotted Rectangular microstrip patch antenna is presented in this paper In this work the rectangular microstrip patch antenna design is loaded with Y slot . The proposed antenna design has triple frequency band in the frequency range 1.0GHz to 5.0 GHz. This frequency band is suitable for WLAN/WiMAX and other wireless communication applications. Triple band of bandwidth 27.22%, 17.36% and 4.94% is obtained. The gain of proposed antenna has been improved up to 4.42dBi and antenna efficiency is 90.67 % at frequency 1.82GHz. The proposed slotted Microstrip antenna is directly fed by 50 Ω probe feed. The proposed antenna is simulated by IE3D(9.0 version)Zealand simulation software based on method of moments.
Flexible Amortized Variational Inference in qBOLD MRI

Streamlined qBOLD acquisitions enable experimentally straightforward observations of brain oxygen metabolism. $R_2^\prime$ maps are easily inferred; however, the Oxygen extraction fraction (OEF) and deoxygenated blood volume (DBV) are more ambiguously determined from the data. As such, existing inference methods tend to yield very noisy and underestimated OEF maps, while overestimating DBV. This work describes a novel probabilistic machine learning approach that can infer plausible distributions of OEF and DBV. Initially, we create a model that produces informative voxelwise prior distribution based on synthetic training data. Contrary to prior work, we model the joint distribution of OEF and DBV through a scaled multivariate logit-Normal distribution, which enables the values to be constrained within a plausible range. The prior distribution model is used to train an efficient amortized variational Bayesian inference model. This model learns to infer OEF and DBV by predicting real image data, with few training data required, using the signal equations as a forward model. We demonstrate that our approach enables the inference of smooth OEF and DBV maps, with a physiologically plausible distribution that can be adapted through specification of an informative prior distribution. Other benefits include model comparison (via the evidence lower bound) and uncertainty quantification for identifying image artefacts. Results are demonstrated on a small study comparing subjects undergoing hyperventilation and at rest. We illustrate that the proposed approach allows measurement of gray matter differences in OEF and DBV and enables voxelwise comparison between conditions, where we observe significant increases in OEF and $R_2^\prime$ during hyperventilation.
A Comparative Study of non-deep Learning, Deep Learning, and Ensemble Learning Methods for Sunspot Number Prediction

ABSTRACT Solar activity has significant impacts on human activities and health. One most commonly used measure of solar activity is the sunspot number. This paper compares three important non-deep learning models, four popular deep learning models, and their five ensemble models in forecasting sunspot numbers. In particular, we propose an ensemble model called XGBoost-DL, which uses XGBoost as a two-level nonlinear ensemble method to combine the deep learning models. Our XGBoost-DL achieves the best forecasting performance (RMSE and MAE ) in the comparison, outperforming the best non-deep learning model SARIMA (RMSE and MAE ), the best deep learning model Informer (RMSE and MAE ) and the NASA’s forecast (RMSE and MAE ). Our XGBoost-DL forecasts a peak sunspot number of 133.47 in May 2025 for Solar Cycle 25 and 164.62 in November 2035 for Solar Cycle 26, similar to but later than the NASA’s at 137.7 in October 2024 and 161.2 in December 2034. An open-source Python package of our XGBoost-DL for the sunspot number prediction is available at https://github.com/yd1008/ts_ensemble_sunspot.
Solving Simulation Systematics in and with AI/ML

Training an AI/ML system on simulated data while using that system to infer on data from real detectors introduces a systematic error which is difficult to estimate and in many analyses is simply not confronted. It is crucial to minimize and to quantitatively estimate the uncertainties in such analysis and do so with a precision and accuracy that matches those that AI/ML techniques bring. Here we highlight the need to confront this class of systematic error, discuss conventional ways to estimate it and describe ways to quantify and to minimize the uncertainty using methods which are themselves based on the power of AI/ML. We also describe methods to introduce a simulation into an AI/ML network to allow for training of its semantically meaningful parameters. This whitepaper is a contribution to the Computational Frontier of Snowmass21.
Performance Analysis and Optimal Node-aware Communication for Enlarged Conjugate Gradient Methods

Krylov methods are a key way of solving large sparse linear systems of equations but suffer from poor strong scalability on distributed memory machines. This is due to high synchronization costs from large numbers of collective communication calls alongside a low computational workload. Enlarged Krylov methods address this issue by decreasing the total iterations to convergence, an artifact of splitting the initial residual and resulting in operations on block vectors. In this article, we present a performance study of an enlarged Krylov method, Enlarged Conjugate Gradients (ECG), noting the impact of block vectors on parallel performance at scale. Most notably, we observe the increased overhead of point-to-point communication as a result of denser messages in the sparse matrix-block vector multiplication kernel. Additionally, we present models to analyze expected performance of ECG, as well as motivate design decisions. Most importantly, we introduce a new point-to-point communication approach based on node-aware communication techniques that increases efficiency of the method at scale.
Personalized Execution Time Optimization for the Scheduled Jobs

Scheduled batch jobs have been widely used on the asynchronous computing platforms to execute various enterprise applications, including the scheduled notifications and the candidate pre-computation for the modern recommender systems. It is important to deliver or update the information to the users at the right time to maintain the user experience and the execution impact. However, it is challenging to provide a versatile execution time optimization solution for the user-basis scheduled jobs to satisfy various product scenarios while maintaining reasonable infrastructure resource consumption. In this paper, we describe how we apply a learning-to-rank approach plus a"best time policy"in the best time selection. In addition, we propose an ensemble learner to minimize the ranking loss by efficiently leveraging multiple streams of user activity signals in our scheduling decisions of the execution time. Especially, we observe the cannibalization cross use cases to compete the user's peak time slot and introduce a coordination system to mitigate the problem. Our optimization approach has been successfully tested with production traffic that serves billions of users per day, with statistically significant improvements in various product metrics, including the notifications and content candidate generation. To the best of our knowledge, our study represents the first ML-based multi-tenant solution of the execution time optimization problem for the scheduled jobs at a large industrial scale cross different product domains.
Fully polarized nonlinear Breit-Wheeler pair production in pulsed plane waves

Fully polarized nonlinear Breit-Wheeler pair production from a beam of polarized seed photons is investigated in pulsed plane-wave backgrounds. The particle (electron and positron) spin and photon polarization are comprehensively described with the theory of the density matrix. The compact expressions for the energy spectrum and spin polarization of the produced particles, depending on the initial polarization of the seed photon beam, are derived and discussed in both linearly and circularly polarized laser backgrounds. The numerical results suggest an appreciable improvement of the positron yield by orthogonalizing the photon polarization to the laser polarization, and the generation of a highly polarized positron beam from a beam of circularly polarized seed photons. The locally monochromatic approximation and the locally constant field approximation are derived and benchmarked with the full quantum electrodynamic calculations.
Registering Articulated Objects With Human-in-the-loop Corrections

Remotely programming robots to execute tasks often relies on registering objects of interest in the robot's environment. Frequently, these tasks involve articulating objects such as opening or closing a valve. However, existing human-in-the-loop methods for registering objects do not consider articulations and the corresponding impact to the geometry of the object, which can cause the methods to fail. In this work, we present an approach where the registration system attempts to automatically determine the object model, pose, and articulation for user-selected points using nonlinear fitting and the iterative closest point algorithm. When the fitting is incorrect, the operator can iteratively intervene with corrections after which the system will refit the object. We present an implementation of our fitting procedure for one degree-of-freedom (DOF) objects with revolute joints and evaluate it with a user study that shows that it can improve user performance, in measures of time on task and task load, ease of use, and usefulness compared to a manual registration approach. We also present a situated example that integrates our method into an end-to-end system for articulating a remote valve.
Anonymous Hyperlocal Communities: What do they talk about?

In this paper, we study what users talk about in a plethora of independent hyperlocal and anonymous online communities in a single country: Saudi Arabia (KSA). We base this perspective on performing a content classification of the Jodel network in the KSA. To do so, we first contribute a content classification schema that assesses both the intent (why) and the topic (what) of posts. We use the schema to label 15k randomly sampled posts and further classify the top 1k hashtags. We observe a rich set of benign (yet at times controversial in conservative regimes) intents and topics that dominantly address information requests, entertainment, or dating/flirting. By comparing two large cities (Riyadh and Jeddah), we further show that hyperlocality leads to shifts in topic popularity between local communities. By evaluating votes (content appreciation) and replies (reactions), we show that the communities react differently to different topics; e.g., entertaining posts are much appreciated through votes, receiving the least replies, while beliefs & politics receive similarly few replies but are controversially voted.
An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering

Recommender systems are information retrieval methods that predict user preferences to personalize services. These systems use the feedback and the ratings provided by users to model the behavior of users and to generate recommendations. Typically, the ratings are quite sparse, i.e., only a small fraction of items are rated by each user. To address this issue and enhance the performance, active learning strategies can be used to select the most informative items to be rated. This rating elicitation procedure enriches the interaction matrix with informative ratings and therefore assists the recommender system to better model the preferences of the users. In this paper, we evaluate various non-personalized and personalized rating elicitation strategies. We also propose a hybrid strategy that adaptively combines a non-personalized and a personalized strategy. Furthermore, we propose a new procedure to obtain free ratings based on the side information of the items. We evaluate these ideas on the MovieLens dataset. The experiments reveal that our proposed hybrid strategy outperforms the strategies from the literature. We also propose the extent to which free ratings are obtained, improving further the performance and also the user experience.
Classification From Positive and Biased Negative Data With Skewed Labeled Posterior Probability

Abstract The binary classification problem has a situation where only biased data are observed in one of the classes. In this letter, we propose a new method to approach the positive and biased negative (PbN) classification problem, which is a weakly supervised learning method to learn a binary classifier from positive data and negative data with biased observations. We incorporate a method to correct the negative influence due to a skewed confidence, which is represented by the posterior probability that the observed data are positive. This reduces the distortion of the posterior probability that the data are labeled, which is necessary for the empirical risk minimization of the PbN classification problem. We verified the effectiveness of the proposed method by synthetic and benchmark data experiments.
Immersive Virtual Reality Simulations of Bionic Vision

Bionic vision uses neuroprostheses to restore useful vision to people living with incurable blindness. However, a major outstanding challenge is predicting what people “see” when they use their devices. The limited field of view of current devices necessitates head movements to scan the scene, which is difficult to simulate on a computer screen. In addition, many computational models of bionic vision lack biological realism. To address these challenges, we present VR-SPV, an open-source virtual reality toolbox for simulated prosthetic vision that uses a psychophysically validated computational model to allow sighted participants to “see through the eyes” of a bionic eye user. To demonstrate its utility, we systematically evaluated how clinically reported visual distortions affect performance in a letter recognition and an immersive obstacle avoidance task. Our results highlight the importance of using an appropriate phosphene model when predicting visual outcomes for bionic vision.
Joint Survey Processing. II. Stellar Proper Motions in the COSMOS Field from Hubble Space Telescope ACS and Subaru Telescope HSC Observations

We analyze stellar proper motions in the COSMOS field to assess the presence of bulk motions. At bright magnitudes (G-band 18.5–20.76 AB), we use the proper motions of 1010 stars in the Gaia DR2 catalog. At the faint end, we compute proper motions of 11,519 pointlike objects at i-band magnitudes 19–25 AB using Hubble ACS and Subaru HSC data, which span two epochs about 11 yr apart. In order to measure these proper motions with unprecedented accuracy at faint magnitudes, we developed a foundational set of astrometric tools that will be required for joint survey processing of data from the next generation of optical/infrared surveys. The astrometric grids of Hubble ACS and Subaru HSC mosaics were corrected at the catalog level using proper motion–propagated and parallax-corrected Gaia DR2 sources. These astrometric corrections were verified using compact extragalactic sources. Upon comparison of our measured proper motions with Gaia DR2, we estimate the uncertainties in our measurements to be ∼2–3 mas yr−1 axis−1, down to 25.5 AB mag. We correct proper motions for the mean motion of the Sun, and we find that late-type main-sequence stars predominantly in the thin disk in the COSMOS field have space velocities mainly toward the Galactic center. We detect candidate high-velocity (≥220 km s−1) stars, six of them at ∼0.4–6 kpc, from the Gaia sample, and five of them at ∼20 kpc, from the faint star HSC and ACS sample. The sources from the faint star sample may be candidate halo members of the Sangarius stream.
Polar Transformation Based Multiple Instance Learning Assisting Weakly Supervised Image Segmentation With Loose Bounding Box Annotations

This study investigates weakly supervised image segmentation using loose bounding box supervision. It presents a multiple instance learning strategy based on polar transformation to assist image segmentation when loose bounding boxes are employed as supervision. In this strategy, weighted smooth maximum approximation is introduced to incorporate the observation that pixels closer to the origin of the polar transformation are more likely to belong to the object in the bounding box. The proposed approach was evaluated on a public medical dataset using Dice coefficient. The results demonstrate its superior performance. The codes are available at \url{https://github.com/wangjuan313/wsis-polartransform}.
Memristors, Spintronics and 2D Materials for Future Computing Systems

In a data-driven economy, virtually all industries beneﬁt from advances in information technology—powerful computing systems are critically important for rapid technological progress. However, this progress might be at risk of slowing down if we do not address the discrepancy between our current computing power demands and what the existing technologies can oﬀer. Key limitations to improving the energy eﬃciency are the excessive growth of data transfer costs associated with the von Neumann architecture and the fundamental limits of complementary metal–oxide–semiconductor (CMOS) technologies, such as transistors. In this perspective article, we discuss three technologies that will likely play an essential role in future computing systems: memristive electronics, spintronics, and electronics based on 2D materials. We present some representative applications for each and speculate how these could ﬁt within future digital, quantum and neuromorphic systems.
Identifiability of Causal-based Fairness Notions: A State of the Art

Machine learning algorithmscan producebiased outcome/prediction, typically, against minorities and under-represented sub-populations. Therefore, fairness is emerging as an important requirement for the large scale application of machine learning based technologies. The most commonly used fairness notions (e.g. statistical parity, equalized odds, predictive parity, etc.) are observational and rely on mere correlation between variables. These notions fail to identify bias in case of statistical anomalies such as Simpson’s or Berkson’s paradoxes. Causality-based fairness notions (e.g. counterfactual fairness, no-proxy discrimination, etc.) are immune to such anomalies and hence more reliable to assess fairness. The problem of causality-based fairness notions, however, is that they are deﬁned in terms of quantities (e.g. causal, counterfactual, and path-speciﬁc eﬀects) that are not always measurable. This is known as the identiﬁability problem and is the topic of a large body of work in the causal inference literature. This paper is a compilation of the major identiﬁability results which are of particular relevance for machine learning fairness. The results are illustrated using a large number of examples and causal graphs. The paper would be of particular interest to fairness researchers, practitioners, and pol-icy makers who are considering the use of causality-based fairness notions as it summarizes and illustrates the major identiﬁability results.
Forecasting the demand of products with short lifecycle based on Norton model

Firstly,the theory and the application range of Norton model is introduced,and then combined the characteristics of products with short lifecycle and the market demand fluctuation,the feasibility of Norton model used in forcasting the short lifecycle products is discussed.After comparing various methods of parameter estimation,the simulated annealing method is selected for estimating the parameters.In the end,taking the three generation's MP3(the capacity of 256M,512M and 1G.) as an example,it has been proved that adopting Norton model to forecast the demand of short lifecycle products gave a good effect and the forcast result is also analyzed and discussed.
Comparing Global Tourism Flows Measured by Official Census and Social Sensing

A better understanding of the behavior of tourists is strategic for improving services in the competitive and important economic segment of global tourism. Critical studies in the literature often explore the issue using traditional data, such as questionnaires or interviews. Traditional approaches provide precious information; however, they impose challenges to obtaining large-scale data, making it hard to study worldwide patterns. Location-based social networks (LBSNs) can potentially mitigate such issues due to the relatively low cost of acquiring large amounts of behavioral data. Nevertheless, before using such data for studying tourists' behavior, it is necessary to verify whether the information adequately reveals the behavior measured with traditional data -- considered the ground truth. Thus, the present work investigates in which countries the global tourism network measured with an LBSN agreeably reflects the behavior estimated by the World Tourism Organization using traditional methods. Although we could find exceptions, the results suggest that, for most countries, LBSN data can satisfactorily represent the behavior studied. We have an indication that, in countries with high correlations between results obtained from both datasets, LBSN data can be used in research regarding the mobility of the tourists in the studied context.