Miracle-dz/UniFAR-FTUs huggingface dataset:
Direct Imaging Explorations for Companions around Mid–Late M Stars from the Subaru/IRD Strategic Program

The Subaru telescope is currently performing a strategic program (SSP) using the high-precision near-infrared (NIR) spectrometer IRD to search for exoplanets around nearby mid/late M dwarfs via radial velocity (RV) monitoring. As part of the observing strategy for the exoplanet survey, signatures of massive companions such as RV trends are used to reduce the priority of those stars. However, this RV information remains useful for studying the stellar multiplicity of nearby M dwarfs. To search for companions around such “deprioritized” M dwarfs, we observed 14 IRD-SSP targets using Keck/NIRC2 with pyramid wave-front sensing at NIR wavelengths, leading to high sensitivity to substellar-mass companions within a few arcseconds. We detected two new companions (LSPM J1002+1459 B and LSPM J2204+1505 B) and two new candidates that are likely companions (LSPM J0825+6902 B and LSPM J1645+0444 B), as well as one known companion. Including two known companions resolved by the IRD fiber injection module camera, we detected seven (four new) companions at projected separations between ∼2 and 20 au in total. A comparison of the colors with the spectral library suggests that LSPM J2204+1505 B and LSPM J0825+6902 B are located at the boundary between late M and early L spectral types. Our deep high-contrast imaging for targets where no bright companions were resolved did not reveal any additional companion candidates. The NIRC2 detection limits could constrain potential substellar-mass companions (∼10–75 M Jup) at 10 au or further. The failure with Keck/NIRC2 around the IRD-SSP stars having significant RV trends makes these objects promising targets for further RV monitoring or deeper imaging with the James Webb Space Telescope to search for smaller-mass companions below the NIRC2 detection limits.
Finding Support Examples for In-Context Learning

Additionally, the strong dependency among in-context examples makes it an NP-hard combinatorial optimization problem and enumerating all permutations is infeasible. Hence we propose LENS, a fiLter-thEN-Search method to tackle this challenge in two stages: First we filter the dataset to obtain informative in-context examples individually. Specifically, we propose a novel metric, InfoScore, to evaluate the example's in-context informativeness based on the language model's feedback, and further propose a progressive filtering process to filter out uninformative examples. Then we propose diversity-guided example search which iteratively refines and evaluates the selected example permutations, to find examples that fully depict the task. The experimental results show that LENS significantly outperforms a wide range of baselines.
Correlation between phase stiffness and condensation energy across the non-Fermi to Fermi-liquid crossover in the Yukawa-Sachdev-Ye-Kitaev model on a lattice

We construct and analyze a lattice generalization of the Yukawa-Sachdev-Ye-Kitaev model, where spinful fermions experience on-site, random, all-to-all interactions with an Einstein bosonic mode, and random intersite coherent hopping. We obtain the exact self-consistent numerical solution of the model at mean-field level, and analytical approximations, for all values of fermion-boson coupling and hopping, under the spin-singlet ansatz and at particle-hole symmetry, both in the normal and superconducting states, thus tracing the entire phase diagram. In the normal state, the competition between hopping and coupling leads to crossovers between Fermi-liquid and non-Fermi liquid states, as reflected by the fermionic and bosonic spectral functions and the normal-state entropy. We calculate the finite phase stiffness of the superconducting state through the equilibrium electromagnetic response. Furthermore, we study the critical temperature $T_c$, as well as the spectral functions, the quasiparticle weight, the gap, and the condensation energy in the superconducting state. At weak coupling, we retrieve a disordered generalization of Bardeen-Cooper-Schrieffer theory. At strong coupling, asymptotically $T_c$ saturates but the stiffness decreases, which suggests strong superconducting fluctuations. $T_c$ is maximum in the single-dot limit, while the stiffness peaks exactly at the crossover between non-Fermi liquid and Fermi-liquid phases. We discover that the quasiparticle weight, the stiffness, and the condensation energy, are all correlated as a function of coupling, reminiscent of the correlations observed in high-temperature cuprate superconductors.
Ensemble learning for Physics Informed Neural Networks: a Gradient Boosting approach

While the popularity of physics-informed neural networks (PINNs) is steadily rising, to this date, PINNs have not been successful in simulating multi-scale and singular perturbation problems. In this work, we present a new training paradigm referred to as"gradient boosting"(GB), which significantly enhances the performance of physics informed neural networks (PINNs). Rather than learning the solution of a given PDE using a single neural network directly, our algorithm employs a sequence of neural networks to achieve a superior outcome. This approach allows us to solve problems presenting great challenges for traditional PINNs. Our numerical experiments demonstrate the effectiveness of our algorithm through various benchmarks, including comparisons with finite element methods and PINNs. Furthermore, this work also unlocks the door to employing ensemble learning techniques in PINNs, providing opportunities for further improvement in solving PDEs.
Entanglement wedge cross section growth during thermalization

Motivated by exploring the thermalization process in relativistic and non-relativistic holographic field theories after a non-local quench, we investigate some features in the time evolution of the entanglement wedge cross section (EWCS). This quantity is a possible holographic dual to some non-local information measures such as entanglement of purification. In particular, we focus on the time dependence of EWCS during black hole formation in D+2 dimensional AdS spacetime as well as geometries with Lifshitz and hyperscaling violating exponents. A combination of analytic and numerical results for large symmetric strip shaped boundary subregions shows that the scaling of EWCS at early times only depends on the Lifshitz exponent. In addition, this early growth regime is followed by a linear growth regime whose velocity depends on the dimensions of spacetime, the Lifshitz exponent, and the hyperscaling parameter. This velocity is the same as the entanglement velocity and for nontrivial dynamical exponent depends on the temperature of the final equilibrium state.
Investigation of the Vertical Structure of Warm-Cloud Microphysical Properties Using the Cloud Evolution Diagram, CFODD, Simulated by a Three-Dimensional Spectral Bin Microphysical Model

This paper investigates the vertical structure of warm-cloud microphysical properties using a threedimensional (3D) spectral bin microphysical model. A time series of contoured frequency by optical depth diagrams(CFODDs),whichwereproposedbypreviousstudies,arecalculatedforthefirsttimebya3Dmodel assuming two types of aerosol conditions (i.e., polluted and pristine). This contrasts with previous studies that obtained CFODDs using either a two-dimensional model or an accumulation of monthly and global observation data. The results show that the simulated CFODDs are characterized by distinctive patterns of radar reflectivities, similar to the patterns often observed by satellite remote sensing, even though the calculation domain of this study is limited to an area of 30 3 30 km 2 , whereas the satellite observations are of a global scale. A cloud microphysical box model is then applied to the simulated cloud field at each time step to identify the dominant process for each of the patterns. The results reveal that the wide variety of satelliteobserved CFODD patterns can be attributed to different microphysical processes occurring in multiple cloud cells at various stages of the cloud life cycle.
Effects of spacecraft potential on three-dimensional electron measurements in the solar wind

Using the three-dimensional, low-energy electron spectrometer aboard the Ulysses spacecraft, we have measured the gyrotropicity of electron distributions in the solar wind. In order to make these observations, we have developed a new technique for correcting spacecraft charging effects in three-dimensional, low-energy particle measurements. Comparisons of ion and electron number and current densities, and the alignment of electron temperature anisotropies with the local magnetic field, are presented as evidence of the improvement in the accuracy of the electron moments resulting from the spacecraft charging corrections. The implications of our charging correction technique go beyond simple scalar corrections to the Ulysses measurements. We discuss the effects of our charging correction upon the measurements of temporal and radial gradients in a plasma environment and for two-dimensionally obtained low-energy particle data.
The ultimate size of carrier-borne epidemics

SUMMARY An epidemic model is discussed in which there is an initial introduction of a carrier or carriers into a population and further carriers may be created from the susceptibles. The probability distribution for the number of survivors is derived and approximations and numerical illustrations are given. In a recent paper Weiss (1965) has considered an epidemic in a closed population which is spread not by the infected individuals but by carriers. He supposed that one or more carriers were introduced into the population and that these carriers infected susceptible individuals until detected, the time before detection having an exponential distribution. At no time during the epidemic were any additional carriers introduced or created and the epidemic terminated as soon as all the carriers were detected or all susceptibles had been infected. Weiss's model has the mathematical advantage that it is completely soluble in terms of elementary functions; see Dietz (1966) and Downton (1967b). It is, however, unrealistic in its assumption that no new carriers can be created. The present paper discusses a model in which it is supposed that after the initial introduction of a carrier or carriers, no new carriers are introduced from outside the population, but that new carriers may be created from the susceptibles in that population. The model is appropriate for the situation where a proportion 7T of those infected contract the disease in such a mild form that their symptoms are unnoticeable even though they are capable of passing on the disease. Such subelinically infected persons would then act as carriers until detected. For such a case we would expect oT to be small; oT = O corresponds to the Weiss model. The main aim of this paper is to obtain approximate expressions for the probability distribution and moments of the number of susceptibles surviving the epidemic in that case. The ultimate behaviour of the epidemic does not, however, depend upon oT in a simple way so that while valid approximations have been obtained for small it, even if this results in a large epidemic, it turns out that these approximations may be used in certain cases even when or is not small. In particular, they may be employed even if 7t = 1, when the mathematics becomes identical with that of the Kermack-McKendrick (1927) model for the general stochastic epidemic provided attention is confined to small subcritical epidemics. The stochastic model describing the process is defined in ? 2, where an explicit expression for the probability distribution of the number of survivors is given, together with an indication of the ways in which it may be obtained. In ? 3 the deterministic analogue of the stochastic model is discussed, providing the deterministic approximation to the mean number of survivors. This approximation is valid for small epidemics in large populations. Returning to the stochastic model ? 4 shows that the survivor probabilities may be expressed in terms of
Intentional Action and Side Effects in Ordinary Language

An experimental study of the conditions under which people claim, in ordinary language, that a particular behavior was performed 'intentionally. ' The study was primarily concerned with the status of what are normally called 'side-effects. ' An outcome is considered a side-effect of an agent's behavior when (1) the agent performed the behavior knowing that she would thereby bring about the outcome but (2) the agent did not actually perform the behavior for the pupose of bringing about that outcome. The experiments reported here explore the question as to whether people ordinarily say that the side-effects of an agent's behavior are brought about 'intentionally. '
Koszul complexes and relative homological algebra of functors over posets

In relative homological algebra of vector space valued functors indexed by a poset, free functors are replaced by an arbitrary family of functors. Relative Betti diagrams encode the multiplicities of these functors in relative minimal resolutions. In this article we show that, under certain conditions, grading the chosen family of functors by an upper semilattice guarantees the existence of relative minimal resolutions and the uniqueness of direct sum decompositions in these resolutions. These conditions are necessary for defining relative Betti diagrams and computing these diagrams using Koszul complexes.
Average case analysis of Lasso under ultra-sparse conditions

We analyze the performance of the least absolute shrinkage and selection operator (Lasso) for the linear model when the number of regressors $N$ grows larger keeping the true support size $d$ finite, i.e., the ultra-sparse case. The result is based on a novel treatment of the non-rigorous replica method in statistical physics, which has been applied only to problem settings where $N$ ,$d$ and the number of observations $M$ tend to infinity at the same rate. Our analysis makes it possible to assess the average performance of Lasso with Gaussian sensing matrices without assumptions on the scaling of $N$ and $M$, the noise distribution, and the profile of the true signal. Under mild conditions on the noise distribution, the analysis also offers a lower bound on the sample complexity necessary for partial and perfect support recovery when $M$ diverges as $M = O(\log N)$. The obtained bound for perfect support recovery is a generalization of that given in previous literature, which only considers the case of Gaussian noise and diverging $d$. Extensive numerical experiments strongly support our analysis.
Full Stack Optimization of Transformer Inference: a Survey

Recent advances in state-of-the-art DNN architecture design have been moving toward Transformer models. These models achieve superior accuracy across a wide range of applications. This trend has been consistent over the past several years since Transformer models were originally introduced. However, the amount of compute and bandwidth required for inference of recent Transformer models is growing at a significant rate, and this has made their deployment in latency-sensitive applications challenging. As such, there has been an increased focus on making Transformer models more efficient, with methods that range from changing the architecture design, all the way to developing dedicated domain-specific accelerators. In this work, we survey different approaches for efficient Transformer inference, including: (i) analysis and profiling of the bottlenecks in existing Transformer architectures and their similarities and differences with previous convolutional models; (ii) implications of Transformer architecture on hardware, including the impact of non-linear operations such as Layer Normalization, Softmax, and GELU, as well as linear operations, on hardware design; (iii) approaches for optimizing a fixed Transformer architecture; (iv) challenges in finding the right mapping and scheduling of operations for Transformer models; and (v) approaches for optimizing Transformer models by adapting the architecture using neural architecture search. Finally, we perform a case study by applying the surveyed optimizations on Gemmini, the open-source, full-stack DNN accelerator generator, and we show how each of these approaches can yield improvements, compared to previous benchmark results on Gemmini. Among other things, we find that a full-stack co-design approach with the aforementioned methods can result in up to 88.7x speedup with a minimal performance degradation for Transformer inference.
The blame game: Understanding blame assignment in social media

Cognitive and psychological studies on morality have proposed underlying linguistic and semantic factors. However, laboratory experiments in the philosophical literature often lack the nuances and complexity of real life. This paper examines how well the findings of these cognitive studies generalize to a corpus of over 30,000 narratives of tense social situations submitted to a popular social media forum. These narratives describe interpersonal moral situations or misgivings; other users judge from the post whether the author (protagonist) or the opposing side (antagonist) is morally culpable. Whereas previous work focuses on predicting the polarity of normative behaviors, we extend and apply natural language processing (NLP) techniques to understand the effects of descriptions of the people involved in these posts. We conduct extensive experiments to investigate the effect sizes of features to understand how they affect the assignment of blame on social media. Our findings show that aggregating psychology theories enables understanding real-life moral situations. Moreover, our results suggest that there exist biases in blame assignment on social media, such as males are more likely to receive blame no matter whether they are protagonists or antagonists.
Solar Energetic Particle Events with Short and Long Onset Times

Gradual solar energetic particle (SEP) events, usually attributed to shock waves driven by coronal mass ejections (CMEs), show a wide variety of temporal behaviors. For example, TO, the >10 MeV proton onset time with respect to the launch of the CME, has a distribution of at least an order of magnitude, even when the source region is not far from the so-called well-connected longitudes. It is important to understand what controls TO, especially in the context of space weather prediction. Here we study two SEP events from the western hemisphere that are different in TO on the basis of >10 MeV proton data from the Geostationary Operations Environmental Satellite, despite being similar in the CME speed and longitude of the source regions. We try to find the reasons for different TO, or proton release times, in how the CME-driven shock develops and the Alfvén Mach number of the shock wave reaches some threshold by combining the CME height-time profiles with radio dynamic spectra. We also discuss how CME–CME interactions and active region properties may affect the proton release times.
Off-Resonant Dicke Quantum Battery: Charging by Virtual Photons

We investigate a Dicke quantum battery in the dispersive regime, where the photons trapped in a resonant cavity are much more energetic with respect to the two-level systems embedded into it. Under such off-resonant conditions, even an empty cavity can lead to the charging of the quantum battery through a proper modulation of the matter–radiation coupling. This counterintuitive behaviour has its roots in the effective interaction between two-level systems mediated by virtual photons emerging from the fluctuations of the quantum electromagnetic field. In order to properly characterize it, we address relevant figures of merit such as the stored energy, the time required to reach the maximum charging, and the averaged charging power. Moreover, the possibility of efficiently extracting energy in various ranges of parameters is discussed. The scaling of stored energy and power as a function of the number N of two-level systems and for different values of the matter–radiation coupling is also discussed, showing, in the strong coupling regime, performances in line with what is reported for the Dicke quantum battery in the resonant regime.
Unsupervised Video Anomaly Detection For Stereotypical Behaviours in Autism

Monitoring and analyzing stereotypical behaviours is important for early intervention and care taking in Autism Spectrum Disorder (ASD). This paper focuses on automatically detecting stereotypical behaviours with computer vision techniques. Off-the-shelf methods tackle this task by supervised classification and activity recognition techniques. However, the un-bounded types of stereotypical behaviours and the difficulty in collecting video recordings of ASD patients largely limit the feasibility of the existing supervised detection methods. As a result, we tackle these challenges from a new perspective, i.e. unsupervised video anomaly detection for stereotypical behaviours detection. The models can be trained among unlabeled videos containing only normal behaviours and unknown types of abnormal behaviours can be detected during inference. Correspondingly, we propose a Dual Stream deep model for Stereotypical Behaviours Detection, DS-SBD, based on the temporal trajectory of human poses and the repetition patterns of human actions. Extensive experiments are conducted to verify the effectiveness of our proposed method and suggest that it serves as a potential benchmark for future research.
Scalar and Vector Electromagnetic Solitary Waves in Nonlinear Hyperbolic Media

In this paper, we investigate the problem of electromagnetic wave propagation in hyperbolic nonlinear media. To address this problem, we consider the scalar hyperbolic nonlinear Schr\"odinger system and its coupled version, namely hyperbolic Manakov type equations. These hyperbolic systems are shown to be non-integrable. Then, we examine the propagation properties of both the scalar and vector electromagnetic solitary waves by deriving their exact analytical forms through the Hirota bilinear method. A detailed analysis shows that the presence of hyperbolic transverse dispersion provides an additional degree of freedom to prevent the formation of singularity in both the scalar and vector solitary wave structures in this hyperbolic nonlinear media. Besides this, we realize that the solitary waves in this media possess fascinating propagation properties which cannot be observed in conventional nonlinear media. We believe that the present study will be very useful in analyzing electromagnetic wave propagation in hyperbolic nonlinear metamaterials.
The Role of Pre-training Data in Transfer Learning

The transfer learning paradigm of model pre-training and subsequent fine-tuning produces high-accuracy models. While most studies recommend scaling the pre-training size to benefit most from transfer learning, a question remains: what data and method should be used for pre-training? We investigate the impact of pre-training data distribution on the few-shot and full fine-tuning performance using 3 pre-training methods (supervised, contrastive language-image and image-image), 7 pre-training datasets, and 9 downstream datasets. Through extensive controlled experiments, we find that the choice of the pre-training data source is essential for the few-shot transfer, but its role decreases as more data is made available for fine-tuning. Additionally, we explore the role of data curation and examine the trade-offs between label noise and the size of the pre-training dataset. We find that using 2000X more pre-training data from LAION can match the performance of supervised ImageNet pre-training. Furthermore, we investigate the effect of pre-training methods, comparing language-image contrastive vs. image-image contrastive, and find that the latter leads to better downstream accuracy