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Recent text-to-image diffusion models are able to learn and synthesize images containing novel, personalized concepts (e.g., their own pets or specific items) with just a few examples for training. This paper tackles two interconnected issues within this realm of personalizing text-to-image diffusion models. First, current personalization techniques fail to reliably extend to multiple concepts -- we hypothesize this to be due to the mismatch between complex scenes and simple text descriptions in the pre-training dataset (e.g., LAION). Second, given an image containing multiple personalized concepts, there lacks a holistic metric that evaluates performance on not just the degree of resemblance of personalized concepts, but also whether all concepts are present in the image and whether the image accurately reflects the overall text description. To address these issues, we introduce Gen4Gen, a semi-automated dataset creation pipeline utilizing generative models to combine personalized concepts into complex compositions along with text-descriptions. Using this, we create a dataset called MyCanvas, that can be used to benchmark the task of multi-concept personalization. In addition, we design a comprehensive metric comprising two scores (CP-CLIP and TI-CLIP) for better quantifying the performance of multi-concept, personalized text-to-image diffusion methods. We provide a simple baseline built on top of Custom Diffusion with empirical prompting strategies for future researchers to evaluate on MyCanvas. We show that by improving data quality and prompting strategies, we can significantly increase multi-concept personalized image generation quality, without requiring any modifications to model architecture or training algorithms., Comment: Preprint; Project Page: https://danielchyeh.github.io/Gen4Gen/

This work introduces a Transformer-based image compression system. It has the flexibility to switch between the standard image reconstruction and the denoising reconstruction from a single compressed bitstream. Instead of training separate decoders for these tasks, we incorporate two add-on modules to adapt a pre-trained image decoder from performing the standard image reconstruction to joint decoding and denoising. Our scheme adopts a two-pronged approach. It features a latent refinement module to refine the latent representation of a noisy input image for reconstructing a noise-free image. Additionally, it incorporates an instance-specific prompt generator that adapts the decoding process to improve on the latent refinement. Experimental results show that our method achieves a similar level of denoising quality to training a separate decoder for joint decoding and denoising at the expense of only a modest increase in the decoder's model size and computational complexity., Comment: Accepted to PCS 2024

Learning from off-policy data is essential for sample-efficient reinforcement learning. In the present work, we build on the insight that the advantage function can be understood as the causal effect of an action on the return, and show that this allows us to decompose the return of a trajectory into parts caused by the agent's actions (skill) and parts outside of the agent's control (luck). Furthermore, this decomposition enables us to naturally extend Direct Advantage Estimation (DAE) to off-policy settings (Off-policy DAE). The resulting method can learn from off-policy trajectories without relying on importance sampling techniques or truncating off-policy actions. We draw connections between Off-policy DAE and previous methods to demonstrate how it can speed up learning and when the proposed off-policy corrections are important. Finally, we use the MinAtar environments to illustrate how ignoring off-policy corrections can lead to suboptimal policy optimization performance., Comment: ICLR 2024

Learned hierarchical B-frame coding aims to leverage bi-directional reference frames for better coding efficiency. However, the domain shift between training and test scenarios due to dataset limitations poses a challenge. This issue arises from training the codec with small groups of pictures (GOP) but testing it on large GOPs. Specifically, the motion estimation network, when trained on small GOPs, is unable to handle large motion at test time, incurring a negative impact on compression performance. To mitigate the domain shift, we present an online motion resolution adaptation (OMRA) method. It adapts the spatial resolution of video frames on a per-frame basis to suit the capability of the motion estimation network in a pre-trained B-frame codec. Our OMRA is an online, inference technique. It need not re-train the codec and is readily applicable to existing B-frame codecs that adopt hierarchical bi-directional prediction. Experimental results show that OMRA significantly enhances the compression performance of two state-of-the-art learned B-frame codecs on commonly used datasets., Comment: 7 pages, submitted to IEEE ICIP 2024

Due to the constraints on power supply and limited encryption capability, data security based on physical layer security (PLS) techniques in backscatter communications has attracted a lot of attention. In this work, we propose to enhance PLS in a full-duplex symbiotic radio (FDSR) system with a proactive eavesdropper, which may overhear the information and interfere legitimate communications simultaneously by emitting attack signals. To deal with the eavesdroppers, we propose a security strategy based on pseudo-decoding and artificial noise (AN) injection to ensure the performance of legitimate communications through forward noise suppression. A novel AN signal generation scheme is proposed using a pseudo-decoding method, where AN signal is superimposed on data signal to safeguard the legitimate channel. The phase control in the forward noise suppression scheme and the power allocation between AN and data signals are optimized to maximize security throughput. The formulated problem can be solved via problem decomposition and alternate optimization algorithms. Simulation results demonstrate the superiority of the proposed scheme in terms of security throughput and attack mitigation performance.

The process of dissociation for two hydrofluorocarbon molecules in low triplet states excited by electron impact in plasma is investigated by ab initio Molecular Dynamics (AIMD). The interest in dissociation of hydrofluorocarbons in plasma is motivated by their role in plasma etching in microelectronic technologies. Dissociation of triplet states is very fast, and the reaction products can be predicted. In this work, it was found that higher triplet states relax into the lowest triplet state within a few femtoseconds due to nonadiabatic dynamics, so that the simplest ab initio MD on the lowest triplet state seems to give a reasonable estimate of the reaction channels branching ratios. We provide evidence for the existence of simple rules for the dissociation of hydrofluorocarbon molecules in triplet states. For molecules with a double bond, the bonds adjacent to it dissociate faster than the other bonds.

In this work, we calculate the sensitivities on the gauge-boson couplings $g_{aZZ}$, $g_{aZ\gamma}$, and $g_{aWW}$ of an axion-like particle (ALP) that one can achieve at the LHC with $\sqrt{s}=14$ TeV and integrated luminosities of 300 fb$^{-1}$ (current run) and 3000 fb$^{-1}$ (High-Luminosity LHC). We focus on the associated production processes $pp\to Za \to (l^+l^-)(\gamma\gamma)$ and $pp\to W^\pm a \to (l^\pm \nu)(\gamma\gamma)$. We show that better sensitivities on these gauge couplings can be achieved at the LHC for $M_a = 1-100$ GeV, down to the level of $10^{-4}\,{\rm GeV}^{-1}$. In conclusion, this study emphasizes the significance of the investigated channels in constraining the ALP couplings at the LHC, offering valuable insights for future experiments dedicated to ALP detection., Comment: 22 Pages, 8 Figures and 7 Tables, JHEP Accepted Version

Creating content with specified identities (ID) has attracted significant interest in the field of generative models. In the field of text-to-image generation (T2I), subject-driven creation has achieved great progress with the identity controlled via reference images. However, its extension to video generation is not well explored. In this work, we propose a simple yet effective subject identity controllable video generation framework, termed Video Custom Diffusion (VCD). With a specified identity defined by a few images, VCD reinforces the identity characteristics and injects frame-wise correlation at the initialization stage for stable video outputs. To achieve this, we propose three novel components that are essential for high-quality identity preservation and stable video generation: 1) a noise initialization method with 3D Gaussian Noise Prior for better inter-frame stability; 2) an ID module based on extended Textual Inversion trained with the cropped identity to disentangle the ID information from the background 3) Face VCD and Tiled VCD modules to reinforce faces and upscale the video to higher resolution while preserving the identity's features. We conducted extensive experiments to verify that VCD is able to generate stable videos with better ID over the baselines. Besides, with the transferability of the encoded identity in the ID module, VCD is also working well with personalized text-to-image models available publicly. The codes are available at https://github.com/Zhen-Dong/Magic-Me., Comment: Project Page at https://magic-me-webpage.github.io

This paper presents a newly established sample of 103 unique galaxies or galaxy groups at $0.4\lesssim z\lesssim 0.7$ from the Cosmic Ultraviolet Baryon Survey (CUBS) for studying the warm-hot circumgalactic medium (CGM) probed by both O VI and Ne VIII absorption. The galaxies and associated neighbors are identified at $< 1$ physical Mpc from the sightlines toward 15 CUBS QSOs at $z_{\rm QSO}\gtrsim 0.8$. A total of 30 galaxies or galaxy groups exhibit associated O VI $\lambda\lambda$ 1031, 1037 doublet absorption within a line-of-sight velocity interval of $\pm250$ km/s, while the rest show no trace of O VI to a detection limit of $\log N_{\rm OVI}/{\rm cm^{-2}}\approx13.7$. Meanwhile, only five galaxies or galaxy groups exhibit the Ne VIII $\lambda\lambda$ 770,780 doublet absorption, down to a limiting column density of $\log N_{\rm NeVIII}/{\rm cm^{-2}}\approx14.0$. These O VI- and Ne VIII-bearing halos reside in different galaxy environments with stellar masses ranging from $\log M_{\rm star}/M_\odot \approx 8$ to $\approx11.5$. The warm-hot CGM around galaxies of different stellar masses and star formation rates exhibits different spatial profiles and kinematics. In particular, star-forming galaxies with $\log M_{\rm star}/M_\odot\approx9-11$ show a significant concentration of metal-enriched warm-hot CGM within the virial radius, while massive quiescent galaxies exhibit flatter radial profiles of both column densities and covering fractions. In addition, the velocity dispersion of O VI absorption is broad with $\sigma_v > 40$ km/s for galaxies of $\log M_{\rm star}/M_\odot>9$ within the virial radius, suggesting a more dynamic warm-hot halo around these galaxies. Finally, the warm-hot CGM probed by O VI and Ne VIII is suggested to be the dominant phase in sub-$L^*$ galaxies with $\log M_{\rm star}/M_\odot\approx9-10$ based on their high ionization fractions in the CGM., Comment: Submitted to ApJ after addressing the referee's comments; 28 pages, 16 figures

Anthropogenic influences have been linked to tropical cyclone (TC) poleward migration, TC extreme precipitation, and an increased proportion of major hurricanes [1, 2, 3, 4]. Understanding past TC trends and variability is critical for projecting future TC impacts on human society considering the changing climate [5]. However, past trends of TC structure/energy remain uncertain due to limited observations; subjective-analyzed and spatiotemporal-heterogeneous "best-track" datasets lead to reduced confidence in the assessed TC repose to climate change [6, 7]. Here, we use deep learning to reconstruct past "observations" and yield an objective global TC wind profile dataset during 1981 to 2020, facilitating a comprehensive examination of TC structure/energy. By training with uniquely labeled data integrating best tracks and numerical model analysis of 2004 to 2018 TCs, our model converts multichannel satellite imagery to a 0-750-km wind profile of axisymmetric surface winds. The model performance is verified to be sufficient for climate studies by comparing it to independent satellite-radar surface winds. Based on the new homogenized dataset, the major TC proportion has increased by ~13% in the past four decades. Moreover, the proportion of extremely high-energy TCs has increased by ~25%, along with an increasing trend (> one standard deviation of the 40-y variability) of the mean total energy of high-energy TCs. Although the warming ocean favors TC intensification, the TC track migration to higher latitudes and altered environments further affect TC structure/energy. This new deep learning method/dataset reveals novel trends regarding TC structure extremes and may help verify simulations/studies regarding TCs in the changing climate., Comment: 41 pages

Virtual garment simulation has become increasingly important with applications in garment design and virtual try-on. However, reproducing garments faithfully remains a cumbersome process. We propose an end-to-end method for estimating parameters of shell material models corresponding to real fabrics with minimal priors. Our method determines yarn model properties from information directly obtained from real fabrics, unlike methods that require expensive specialized capture systems. We use an extended homogenization method to match yarn-level and shell-level hyperelastic energies with respect to a range of surface deformations represented by the first and second fundamental forms, including bending along the diagonal to warp and weft directions. We optimize the parameters of a shell deformation model involving uncoupled bending and membrane energies. This allows the simulated model to exhibit nonlinearity and anisotropy seen in real cloth. Finally, we validate our results with quantitative and visual comparisons against real world fabrics through stretch tests and drape experiments. Our homogenized shell models not only capture the characteristics of underlying yarn patterns, but also exhibit distinct behaviors for different yarn materials.

We present JWST spectral and photometric observations of the Type IIP supernova (SN) 2022acko at ~50 days past explosion. These data are the first JWST spectral observations of a core-collapse SN. We identify ~30 different H I features, other features associated with products produced from the CNO cycle, and s-process elements such as Sc II and Ba II. By combining the JWST spectra with ground-based optical and NIR spectra, we construct a full Spectral Energy Distribution from 0.4 to 25 microns and find that the JWST spectra are fully consistent with the simultaneous JWST photometry. The data lack signatures of CO formation and we estimate a limit on the CO mass of < 10^{-8} solar mass. We demonstrate how the CO fundamental band limits can be used to probe underlying physics during stellar evolution, explosion, and the environment. The observations indicate little mixing between the H envelope and C/O core in the ejecta and show no evidence of dust. The data presented here set a critical baseline for future JWST observations, where possible molecular and dust formation may be seen.

The incorporation of LiDAR technology into some high-end smartphones has unlocked numerous possibilities across various applications, including photography, image restoration, augmented reality, and more. In this paper, we introduce a novel direction that harnesses LiDAR depth maps to enhance the compression of the corresponding RGB camera images. To the best of our knowledge, this represents the initial exploration in this particular research direction. Specifically, we propose a Transformer-based learned image compression system capable of achieving variable-rate compression using a single model while utilizing the LiDAR depth map as supplementary information for both the encoding and decoding processes. Experimental results demonstrate that integrating LiDAR yields an average PSNR gain of 0.83 dB and an average bitrate reduction of 16% as compared to its absence.

In strongly coupled gauge theories that serve as completions of composite Higgs models, the fermionic bound states formed by fermions (hyperquarks) transforming in different representations, called chimera baryons, could serve as top partners, by embedding of the Standard Model appropriately. We report our results on the spectrum of chimera baryons in the Sp(4) gauge theory with hyperquarks transforming in fundamental and two-index antisymmetric representations. For this study, we adopt the quenched approximation. We investigate the mass hierarchy between the lightest chimera baryons with different quantum numbers, as a function of the lattice parameters. Inspired by baryon chiral effective field theory, and the Akaike Information Criterion, we perform a first extrapolation to the continuum and massless-hyperquark limit., Comment: 8 pages, 2 figures, contribution to the proceedings of the 40th International Symposium on Lattice Field Theory (LATTICE2023), July 31st - August 4th, 2023, Fermi National Accelerator Laboratory, Batavia, Illinois, USA

We report the All-Sky Automated Survey for SuperNovae discovery of the tidal disruption event (TDE) ASASSN-23bd (AT 2023clx) in NGC 3799, a LINER galaxy with no evidence of strong AGN activity over the past decade. With a redshift of $z = 0.01107$ and a peak UV/optical luminosity of $(5.4\pm0.4)\times10^{42}$ erg s$^{-1}$, ASASSN-23bd is the lowest-redshift and least-luminous TDE discovered to date. Spectroscopically, ASASSN-23bd shows H$\alpha$ and He I emission throughout its spectral time series, and the UV spectrum shows nitrogen lines without the strong carbon and magnesium lines typically seen for AGN. Fits to the rising ASAS-SN light curve show that ASASSN-23bd started to brighten on MJD 59988$^{+1}_{-1}$, $\sim$9 days before discovery, with a nearly linear rise in flux, peaking in the $g$ band on MJD $60000^{+3}_{-3}$. Scaling relations and TDE light curve modelling find a black hole mass of $\sim$10$^6$ $M_\odot$, which is on the lower end of supermassive black hole masses. ASASSN-23bd is a dim X-ray source, with an upper limit of $L_{0.3-10\,\mathrm{keV}} < 1.0\times10^{40}$ erg s$^{-1}$ from stacking all \emph{Swift} observations prior to MJD 60061, but with soft ($\sim 0.1$ keV) thermal emission with a luminosity of $L_{0.3-2 \,\mathrm{keV}}\sim4\times10^{39}$ erg s$^{-1}$ in \emph{XMM-Newton} observations on MJD 60095. The rapid $(t < 15$ days) light curve rise, low UV/optical luminosity, and a luminosity decline over 40 days of $\Delta L_{40}\approx-0.7$ make ASASSN-23bd one of the dimmest TDEs to date and a member of the growing ``Low Luminosity and Fast'' class of TDEs., Comment: 17 pages, 13 figures, submitted to MNRAS

The digital era has seen a marked increase in financial fraud. edge ML emerged as a promising solution for smartphone payment services fraud detection, enabling the deployment of ML models directly on edge devices. This approach enables a more personalized real-time fraud detection. However, a significant gap in current research is the lack of a robust system for monitoring data distribution shifts in these distributed edge ML applications. Our work bridges this gap by introducing a novel open-source framework designed for continuous monitoring of data distribution shifts on a network of edge devices. Our system includes an innovative calculation of the Kolmogorov-Smirnov (KS) test over a distributed network of edge devices, enabling efficient and accurate monitoring of users behavior shifts. We comprehensively evaluate the proposed framework employing both real-world and synthetic financial transaction datasets and demonstrate the framework's effectiveness.

Communication among healthcare professionals (HCPs) is crucial for the quality of patient treatment. Surrounding each patient's treatment, communication among HCPs can be examined as temporal networks, constructed from Electronic Health Record (EHR) access logs. This paper introduces a visual analytics system designed to study the effectiveness and efficiency of temporal communication networks mediated by the EHR system. We present a method that associates network measures with patient survival outcomes and devises effectiveness metrics based on these associations. To analyze communication efficiency, we extract the latencies and frequencies of EHR accesses. Our visual analytics system is designed to assist in inspecting and understanding the composed communication effectiveness metrics and to enable the exploration of communication efficiency by encoding latencies and frequencies in an information flow diagram. We demonstrate and evaluate our system through multiple case studies and an expert review.

The Cosmic Gems arc is among the brightest and highly magnified galaxies observed at redshift $z\sim10.2$. However, it is an intrinsically UV faint galaxy, in the range of those now thought to drive the reionization of the Universe. Hitherto the smallest features resolved in a galaxy at a comparable redshift are between a few hundreds and a few tens of parsecs. Here we report JWST observations of the Cosmic Gems. The light of the galaxy is resolved into five star clusters located in a region smaller than 70 parsec. They exhibit minimal dust attenuation and low metallicity, ages younger than 50 Myr and intrinsic masses of $\sim10^6$ M$_{\odot}$. Their lensing-corrected sizes are approximately 1 pc, resulting in stellar surface densities near $10^5$~M$_{\odot}$/pc$^2$, three orders of magnitude higher than typical young star clusters in the local universe. Despite the uncertainties inherent to the lensing model, they are consistent with being gravitationally bound stellar systems, i.e., proto-globular clusters. We conclude that star cluster formation and feedback likely contributed to shape the properties of galaxies during the epoch of reionization. [Abridged], Comment: Accepted for publication

We revisit the problem of the particle dynamics subject to a geometric holonomic constraint of codimension 1 in spatial dimensions d =2 and 3. In the absence of dissipation, we show that by solving the Lagrangian multiplier in a general fashion, the external potential independent part, the net normal force, of the equation of motion corresponds to precisely to the curvature of the trajectory on the constraint space multiplied by twice the kinetic energy. The tangent the trajectory is the instantaneous velocity. In d = 3, this term equals the second fundamental form II of the constraint surface evaluated on the unit tangent vector in the direction of velocity. Using these result we establish the relation between constrained particle dynamics with geodesic equations and derive intriguing kinematic implications using theorems from fundamental differential geometry., Comment: 7 pages, 1 figure

It has been an unanswered question how many dusty galaxies have been undetected from the state-of-the-art observational surveys. JWST enables us to detect faint IR galaxies that have prominent polycyclic aromatic hydrocarbon (PAH) features in the mid-IR wavelengths. PAH is a valuable tracer of star formation and dust properties in the mid-infrared wavelength. The JWST Cosmic Evolution Early Release Science (CEERS) fields provide us with wavelength coverage from 7.7 to 21 $\mu$m using six photometric bands of the mid-infrared instrument (MIRI). We have identified galaxies dominated by mid-IR emission from PAHs, termed PAH galaxies. From our multi-band photometry catalogue, we selected ten PAH galaxies displaying high flux ratios of $\log(S_{15}/S_{10}) > 0.8$. The SED fitting analysis indicates that these galaxies are star-forming galaxies with total IR luminosities of $10^{10}$ $\sim$ $10^{11.5}$ $L_{\odot}$ at z $\sim 1$. The morphology of PAH galaxies does not show any clear signatures of major merging or interaction within the MIRI resolution. The majority of them are on the star-formation main sequence at $z \sim 1$. Our result demonstrates that JWST can detect PAH emissions from normal star-forming galaxies at $z \sim 1$, in addition to ultra-luminous infrared galaxies (ULIRGs) or luminous infrared galaxies (LIRGs)., Comment: 12 pages, 20 figures, 4 tables. Accepted by MNRAS. A summary video is at https://www.youtube.com/watch?v=UtPaVTFM4f8&ab_channel=NTHUCosmology

This work evaluated several cutting-edge large-scale foundation models based on self-supervision or weak supervision, including SeamlessM4T, SeamlessM4T v2, and Whisper-large-v3, on three code-switched corpora. We found that self-supervised models can achieve performances close to the supervised model, indicating the effectiveness of multilingual self-supervised pre-training. We also observed that these models still have room for improvement as they kept making similar mistakes and had unsatisfactory performances on modeling intra-sentential code-switching. In addition, the validity of several variants of Whisper was explored, and we concluded that they remained effective in a code-switching scenario, and similar techniques for self-supervised models are worth studying to boost the performance of code-switched tasks., Comment: Submitted to ICASSP 2024 Self-supervision in Audio, Speech and Beyond workshop

The coherent control of interacting spins in semiconductor quantum dots is of strong interest for quantum information processing as well as for studying quantum magnetism from the bottom up. Here, we present a $2\times4$ germanium quantum dot array with full and controllable interactions between nearest-neighbor spins. As a demonstration of the level of control, we define four singlet-triplet qubits in this system and show two-axis single-qubit control of each qubit and SWAP-style two-qubit gates between all neighbouring qubit pairs, yielding average single-qubit gate fidelities of 99.49(8)-99.84(1)% and Bell state fidelities of 73(1)-90(1)%. Combining these operations, we experimentally implement a circuit designed to generate and distribute entanglement across the array. A remote Bell state with a fidelity of 75(2)% and concurrence of 22(4)% is achieved. These results highlight the potential of singlet-triplet qubits as a competing platform for quantum computing and indicate that scaling up the control of quantum dot spins in extended bilinear arrays can be feasible., Comment: In the updated version, data on single- and two-qubit gate fidelities, as well as two-qubit Bell state fidelities, have been added

Conditional coding has lately emerged as the mainstream approach to learned video compression. However, a recent study shows that it may perform worse than residual coding when the information bottleneck arises. Conditional residual coding was thus proposed, creating a new school of thought to improve on conditional coding. Notably, conditional residual coding relies heavily on the assumption that the residual frame has a lower entropy rate than that of the intra frame. Recognizing that this assumption is not always true due to dis-occlusion phenomena or unreliable motion estimates, we propose a masked conditional residual coding scheme. It learns a soft mask to form a hybrid of conditional coding and conditional residual coding in a pixel adaptive manner. We introduce a Transformer-based conditional autoencoder. Several strategies are investigated with regard to how to condition a Transformer-based autoencoder for inter-frame coding, a topic that is largely under-explored. Additionally, we propose a channel transform module (CTM) to decorrelate the image latents along the channel dimension, with the aim of using the simple hyperprior to approach similar compression performance to the channel-wise autoregressive model. Experimental results confirm the superiority of our masked conditional residual transformer (termed MaskCRT) to both conditional coding and conditional residual coding. On commonly used datasets, MaskCRT shows comparable BD-rate results to VTM-17.0 under the low delay P configuration in terms of PSNR-RGB and outperforms VTM-17.0 in terms of MS-SSIM-RGB. It also opens up a new research direction for advancing learned video compression., Comment: Accepted for Publication in IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)

Deep learning has had remarkable success at analyzing handheld imagery such as consumer photos due to the availability of large-scale human annotations (e.g., ImageNet). However, remote sensing data lacks such extensive annotation and thus potential for supervised learning. To address this, we propose a highly effective semi-supervised approach tailored specifically to remote sensing data. Our approach encompasses two key contributions. First, we adapt the FixMatch framework to remote sensing data by designing robust strong and weak augmentations suitable for this domain. Second, we develop an effective semi-supervised learning method by removing bias in imbalanced training data resulting from both actual labels and pseudo-labels predicted by the model. Our simple semi-supervised framework was validated by extensive experimentation. Using 30\% of labeled annotations, it delivers a 7.1\% accuracy gain over the supervised learning baseline and a 2.1\% gain over the supervised state-of-the-art CDS method on the remote sensing xView dataset., Comment: Accepted to CVPR 2023 MultiEarth Workshop

Optimal individualized treatment decision-making has improved health outcomes in recent years. The value function is commonly used to evaluate the goodness of an individualized treatment decision rule. Despite recent advances, comparing value functions between different treatment decision rules or constructing confidence intervals around value functions remains difficult. We propose a t-test based method applied to a test set that generates valid p-values to compare value functions between a given pair of treatment decision rules when some of the data are missing. We demonstrate the ease in use of this method and evaluate its performance via simulation studies and apply it to the China Health and Nutrition Survey data.

In-context learning (ICL) has revolutionized the capabilities of transformer models in NLP. In our project, we extend the understanding of the mechanisms underpinning ICL by exploring whether transformers can learn from sequential, non-textual function class data distributions. We introduce a novel sliding window sequential function class and employ toy-sized transformers with a GPT-2 architecture to conduct our experiments. Our analysis indicates that these models can indeed leverage ICL when trained on non-textual sequential function classes. Additionally, our experiments with randomized y-label sequences highlights that transformers retain some ICL capabilities even when the label associations are obfuscated. We provide evidence that transformers can reason with and understand sequentiality encoded within function classes, as reflected by the effective learning of our proposed tasks. Our results also show that the performance deteriorated with increasing randomness in the labels, though not to the extent one might expect, implying a potential robustness of learned sequentiality against label noise. Future research may want to look into how previous explanations of transformers, such as induction heads and task vectors, relate to sequentiality in ICL in these toy examples. Our investigation lays the groundwork for further research into how transformers process and perceive sequential data., Comment: 8 pages, 8 figures

This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of the Gemini family in cross-modal reasoning and language understanding will enable a wide variety of use cases. We discuss our approach toward post-training and deploying Gemini models responsibly to users through services including Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI.

X-ray circular dichroism, arising from the contrast in X-ray absorption between opposite photon helicities, serves as a spectroscopic tool to measure the magnetization of ferromagnetic materials and identify the handedness of chiral crystals. Antiferromagnets with crystallographic chirality typically lack X-ray magnetic circular dichroism because of time-reversal symmetry, yet exhibit weak X-ray natural circular dichroism. Here, we report the observation of giant natural circular dichroism in the Ni $L_3$-edge X-ray absorption of Ni$_3$TeO$_6$, a polar and chiral antiferromagnet with effective time-reversal symmetry. To unravel this intriguing phenomenon, we propose a phenomenological model that classifies the movement of photons in a chiral crystal within the same symmetry class as that of a magnetic field. The coupling of X-ray polarization with the induced magnetization yields giant X-ray natural circular dichroism, revealing the altermagnetism of Ni$_3$TeO$_6$. Our findings provide evidence for the interplay between magnetism and crystal chirality in natural optical activity. Additionally, we establish the first example of a new class of magnetic materials exhibiting circular dichroism with time-reversal symmetry., Comment: Accepted by Advanced Materials (2024.2.16) Revised title: Giant X-ray circular dichroism in a time-reversal invariant altermagnet Revised drafts: Main 14 pages, 4 figures, and SI 20 pages, 8 figures

This book was created as part of the SIRIUS B VERGE program to orient students to astrophysics as a broad field. The 2023-2024 VERGE program and the printing of this book is funded by the Women and Girls in Astronomy Program via the International Astronomical Union's North American Regional Office of Astronomy for Development and the Heising-Simons Foundation; as a result, this document is written by women in astronomy for girls who are looking to pursue the field. However, given its universal nature, the material covered in this guide is useful for anyone interested in pursuing astrophysics professionally., Comment: Introductory guide for students interested in pursuing astrophysics; to be submitted to BAAS