Miracle-dz/UniFAR-FTUs huggingface dataset:
DeepH-2: Enhancing deep-learning electronic structure via an equivariant local-coordinate transformer

Deep-learning electronic structure calculations show great potential for revolutionizing the landscape of computational materials research. However, current neural-network architectures are not deemed suitable for widespread general-purpose application. Here we introduce a framework of equivariant local-coordinate transformer, designed to enhance the deep-learning density functional theory Hamiltonian referred to as DeepH-2. Unlike previous models such as DeepH and DeepH-E3, DeepH-2 seamlessly integrates the simplicity of local-coordinate transformations and the mathematical elegance of equivariant neural networks, effectively overcoming their respective disadvantages. Based on our comprehensive experiments, DeepH-2 demonstrates superiority over its predecessors in both efficiency and accuracy, showcasing state-of-the-art performance. This advancement opens up opportunities for exploring universal neural network models or even large materials models.
Fourier Prompt Tuning for Modality-Incomplete Scene Segmentation

Integrating information from multiple modalities enhances the robustness of scene perception systems in autonomous vehicles, providing a more comprehensive and reliable sensory framework. However, the modality incompleteness in multi-modal segmentation remains under-explored. In this work, we establish a task called Modality-Incomplete Scene Segmentation (MISS), which encompasses both system-level modality absence and sensor-level modality errors. To avoid the predominant modality reliance in multi-modal fusion, we introduce a Missing-aware Modal Switch (MMS) strategy to proactively manage missing modalities during training. Utilizing bit-level batch-wise sampling enhances the model's performance in both complete and incomplete testing scenarios. Furthermore, we introduce the Fourier Prompt Tuning (FPT) method to incorporate representative spectral information into a limited number of learnable prompts that maintain robustness against all MISS scenarios. Akin to fine-tuning effects but with fewer tunable parameters (1.1%). Extensive experiments prove the efficacy of our proposed approach, showcasing an improvement of 5.84% mIoU over the prior state-of-the-art parameter-efficient methods in modality missing. The source code will be publicly available at https://github.com/RuipingL/MISS.
A New Approach to Harnessing Side Information in Multi-Server Private Information Retrieval

This paper presents new solutions for Private Information Retrieval (PIR) with side information. This problem is motivated by PIR settings in which a client has side information about the data held by the servers and would like to leverage this information in order to improve the download rate. The problem of PIR with side information has been the subject of several recent studies that presented achievability schemes as well as converses for both multi-server and single-server settings. However, the solutions for the multi-server settings adapted from the solutions for the single-server setting in a rather straightforward manner, relying on the concept of super-messages. Such solutions require an exponential degree of sub-packetization (in terms of the number of messages). This paper makes the following contributions. First, we revisit the PIR problem with side information and present a new approach to leverage side information in the context of PIR. The key idea of our approach is a randomized algorithm to determine the linear combinations of the sub-packets that need to be recovered from each server. In addition, our approach takes advantage of the fact that the identity of the side information messages does not need to be kept private, and, as a result, the information retrieval scheme does not need to be symmetric. Second, we present schemes for PIR with side information that achieve a higher rate than previously proposed solutions and require a significantly lower degree of sub-packetization (linear in the number of servers). Our scheme not only achieves the highest known download rate for the problem at hand but also invalidates a previously claimed converse bound on the maximum achievable download rate.
Psychometric Properties of the Apathy Scale in Advanced Parkinson's Disease

Objectives To assess the psychometric attributes of the Apathy Scale- (AS-) Spanish version in patients with advanced Parkinson's disease (APD). Materials and Methods Over 6 months, 61 patients participated in a clinical study of levodopa-carbidopa intestinal gel (LCIG) and were evaluated using the AS and other clinical tools. Various psychometric attributes of the AS were assessed. Results Patients (60.7% men) were aged 68.02 ± 7.43 years, with 12.57 ± 5.97 years from PD diagnosis. Median HY of patients in “on state” was 2 (range, 1–4), and mean levodopa equivalent daily dose was 1455.98 ± 456.00 mg. Overall, the parameters of feasibility/acceptability were satisfactory, except for a moderate-to-high floor effect in AS items but not in its total score (both 3.3%). Cronbach's alpha was 0.78, while item homogeneity coefficient was 0.21. Almost all items (11/14) reached acceptable item-total corrected correlations (rS = 0.16–0.50). AS total score was moderately correlated with Beck Depression Inventory (0.34) and with Non-Motor Symptoms Scale domains 2 (sleep/fatigue, 0.35), 3 (mood/apathy, 0.56), and 5 (attention/memory, 0.41). There were no significant differences between AS total scores by established groups of sex, time from diagnosis, HY, and Clinical Global Impression-Severity Scale. Following LCIG treatment, there was no significant change in the AS total score. The relative change was 5.56%, the standard error of the difference was 4.17, and Cohen's d effect was 0.10. Conclusions The AS showed satisfactory feasibility, acceptability, scaling assumptions, internal consistency, and convergent validity. Responsiveness parameters were poor, probably due to the characteristics of the clinical study from which these data came. This trial is registered with NCT02289729.
Weaver: Foundation Models for Creative Writing

This work introduces Weaver, our first family of large language models (LLMs) dedicated to content creation. Weaver is pre-trained on a carefully selected corpus that focuses on improving the writing capabilities of large language models. We then fine-tune Weaver for creative and professional writing purposes and align it to the preference of professional writers using a suit of novel methods for instruction data synthesis and LLM alignment, making it able to produce more human-like texts and follow more diverse instructions for content creation. The Weaver family consists of models of Weaver Mini (1.8B), Weaver Base (6B), Weaver Pro (14B), and Weaver Ultra (34B) sizes, suitable for different applications and can be dynamically dispatched by a routing agent according to query complexity to balance response quality and computation cost. Evaluation on a carefully curated benchmark for assessing the writing capabilities of LLMs shows Weaver models of all sizes outperform generalist LLMs several times larger than them. Notably, our most-capable Weaver Ultra model surpasses GPT-4, a state-of-the-art generalist LLM, on various writing scenarios, demonstrating the advantage of training specialized LLMs for writing purposes. Moreover, Weaver natively supports retrieval-augmented generation (RAG) and function calling (tool usage). We present various use cases of these abilities for improving AI-assisted writing systems, including integration of external knowledge bases, tools, or APIs, and providing personalized writing assistance. Furthermore, we discuss and summarize a guideline and best practices for pre-training and fine-tuning domain-specific LLMs.
ILBiT: Imitation Learning for Robot Using Position and Torque Information based on Bilateral Control with Transformer

Autonomous manipulation in robot arms is a complex and evolving field of study in robotics. This paper introduces an innovative approach to this challenge by focusing on imitation learning (IL). Unlike traditional imitation methods, our approach uses IL based on bilateral control, allowing for more precise and adaptable robot movements. The conventional IL based on bilateral control method have relied on Long Short-Term Memory (LSTM) networks. In this paper, we present the IL for robot using position and torque information based on Bilateral control with Transformer (ILBiT). This proposed method employs the Transformer model, known for its robust performance in handling diverse datasets and its capability to surpass LSTM's limitations, especially in tasks requiring detailed force adjustments. A standout feature of ILBiT is its high-frequency operation at 100 Hz, which significantly improves the system's adaptability and response to varying environments and objects of different hardness levels. The effectiveness of the Transformer-based ILBiT method can be seen through comprehensive real-world experiments.
A Framework of Data Assimilation for Wind Flow Fields by Physics-informed Neural Networks

Various types of measurement techniques, such as Light Detection and Ranging devices, anemometers, and wind vanes, are extensively utilized in wind energy to characterize the inflow. However, these methods typically gather data at limited points within local wind fields, capturing only a fraction of the wind field's characteristics at wind turbine sites, thus hindering detailed wind field analysis. This study introduces a framework using Physics-informed Neural Networks (PINNs) to assimilate diverse sensor data types. This includes line-of-sight wind speed, velocity magnitude and direction, velocity components, and pressure. Moreover, the Reynolds Averaged Navier-Stokes equations are integrated as physical constraints, ensuring that the neural networks accurately represent atmospheric flow dynamics. The framework accounts for the turbulent nature of atmospheric boundary layer flow by including turbulence eddy viscosity in the network outputs, enhancing the model's ability to learn and accurately depict large-scale flow structures. The reconstructed flow field and the effective wind speed are in good agreement with the actual data. Furthermore, a transfer learning strategy is employed for the online deployment of pre-trained PINN, which requires less time than that of the actual physical flow. This capability allows the framework to reconstruct wind flow fields in real time based on live data. In the demo cases, the maximum error between the effective wind speed reconstructed online and the actual value at the wind turbine site is only 3.7%. The proposed data assimilation framework provides a universal tool for reconstructing spatiotemporal wind flow fields using various measurement data. Additionally, it presents a viable approach for the online assimilation of real-time measurements. To facilitate the utilization of wind energy, our framework's source code is openly accessible.
Towards Quantum Simulation of Non-Markovian Open Quantum Dynamics: A Universal and Compact Theory

Non-Markovianity, the intricate dependence of an open quantum system on its temporal evolution history, holds tremendous implications across various scientific disciplines. However, accurately characterizing the complex non-Markovian effects has posed a formidable challenge for numerical simulations. Despite the promising potential of emerging quantum computing technologies, the pursuit of a universal theory enabling practical implementation of quantum computation algorithms remains ongoing. In this paper, we present a major advancement in bridging this critical gap: the development of dissipaton-embedded quantum master equation in second quantization (DQME-SQ). The DQME-SQ is an exact and compact theory, and its unique capabilities are demonstrated through digital quantum simulations of non-Markovian dissipative dynamics in both bosonic and fermionic environments. Our new theoretical developments pave the way for efficient exploration of complex open quantum systems.
The Influence of Presentation and Performance on User Satisfaction

The effectiveness of an IR system is gauged not just by its ability to retrieve relevant results but also by how it presents these results to users; an engaging presentation often correlates with increased user satisfaction. While existing research has delved into the link between user satisfaction, IR performance metrics, and presentation, these aspects have typically been investigated in isolation. Our research aims to bridge this gap by examining the relationship between query performance, presentation and user satisfaction. For our analysis, we conducted a between-subjects experiment comparing the effectiveness of various result card layouts for an ad-hoc news search interface. Drawing data from the TREC WaPo 2018 collection, we centered our study on four specific topics. Within each of these topics, we assessed six distinct queries with varying nDCG values. Our study involved 164 participants who were exposed to one of five distinct layouts containing result cards, such as"title'',"title+image'', or"title+image+summary''. Our findings indicate that while nDCG is a strong predictor of user satisfaction at the query level, there exists no linear relationship between the performance of the query, presentation of results and user satisfaction. However, when considering the total gain on the initial result page, we observed that presentation does play a significant role in user satisfaction (at the query level) for certain layouts with result cards such as, title+image or title+image+summary. Our results also suggest that the layout differences have complex and multifaceted impacts on satisfaction. We demonstrate the capacity to equalize user satisfaction levels between queries of varying performance by changing how results are presented. This emphasizes the necessity to harmonize both performance and presentation in IR systems, considering users' diverse preferences.
You Only Need One Step: Fast Super-Resolution with Stable Diffusion via Scale Distillation

In this paper, we introduce YONOS-SR, a novel stable diffusion-based approach for image super-resolution that yields state-of-the-art results using only a single DDIM step. We propose a novel scale distillation approach to train our SR model. Instead of directly training our SR model on the scale factor of interest, we start by training a teacher model on a smaller magnification scale, thereby making the SR problem simpler for the teacher. We then train a student model for a higher magnification scale, using the predictions of the teacher as a target during the training. This process is repeated iteratively until we reach the target scale factor of the final model. The rationale behind our scale distillation is that the teacher aids the student diffusion model training by i) providing a target adapted to the current noise level rather than using the same target coming from ground truth data for all noise levels and ii) providing an accurate target as the teacher has a simpler task to solve. We empirically show that the distilled model significantly outperforms the model trained for high scales directly, specifically with few steps during inference. Having a strong diffusion model that requires only one step allows us to freeze the U-Net and fine-tune the decoder on top of it. We show that the combination of spatially distilled U-Net and fine-tuned decoder outperforms state-of-the-art methods requiring 200 steps with only one single step.
The fate of optical excitons in FAPbI3 nanocube superlattices

Understanding the nature of the photoexcitation and ultrafast charge dynamics pathways in organic halide perovskite superlattices is key for their potential applications as tunable light emitters, photon harvesting materials and light-amplification systems. In this work we apply two-dimensional coherent electronic spectroscopy (2DES) to track in real time the formation of near-infrared optical excitons and their ultrafast decay into bi-excitons in CH(NH2)2PbI3 nanocube superlattices. On the picosecond timescale, the strong coupling between localized charges and specific lattice distortions leads to the progressive formation of long-lived localized trap states. The analysis of the temperature dependence of the excitonic intrinsic linewidth, as extracted by the anti-diagonal components of the 2D spectra, unveils a dramatic change of the excitonic coherence time across the cubic to tetragonal structural transition. Our results offer a new way to control and enhance the ultrafast coherent dynamics of photocarrier generation in hybrid halide perovskite synthetic solids.
Democratizing the Creation of Animatable Facial Avatars

In high-end visual effects pipelines, a customized (and expensive) light stage system is (typically) used to scan an actor in order to acquire both geometry and texture for various expressions. Aiming towards democratization, we propose a novel pipeline for obtaining geometry and texture as well as enough expression information to build a customized person-specific animation rig without using a light stage or any other high-end hardware (or manual cleanup). A key novel idea consists of warping real-world images to align with the geometry of a template avatar and subsequently projecting the warped image into the template avatar's texture; importantly, this allows us to leverage baked-in real-world lighting/texture information in order to create surrogate facial features (and bridge the domain gap) for the sake of geometry reconstruction. Not only can our method be used to obtain a neutral expression geometry and de-lit texture, but it can also be used to improve avatars after they have been imported into an animation system (noting that such imports tend to be lossy, while also hallucinating various features). Since a default animation rig will contain template expressions that do not correctly correspond to those of a particular individual, we use a Simon Says approach to capture various expressions and build a person-specific animation rig (that moves like they do). Our aforementioned warping/projection method has high enough efficacy to reconstruct geometry corresponding to each expressions.
WGAN-AFL: Seed Generation Augmented Fuzzer with Wasserstein-GAN

The importance of addressing security vulnerabilities is indisputable, with software becoming crucial in sectors such as national defense and finance. Consequently, The security issues caused by software vulnerabilities cannot be ignored. Fuzz testing is an automated software testing technology that can detect vulnerabilities in the software. However, most previous fuzzers encounter challenges that fuzzing performance is sensitive to initial input seeds. In the absence of high-quality initial input seeds, fuzzers may expend significant resources on program path exploration, leading to a substantial decrease in the efficiency of vulnerability detection. To address this issue, we propose WGAN-AFL. By collecting high-quality testcases, we train a generative adversarial network (GAN) to learn their features, thereby obtaining high-quality initial input seeds. To overcome drawbacks like mode collapse and training instability inherent in GANs, we utilize the Wasserstein GAN (WGAN) architecture for training, further enhancing the quality of the generated seeds. Experimental results demonstrate that WGAN-AFL significantly outperforms the original AFL in terms of code coverage, new paths, and vulnerability discovery, demonstrating the effective enhancement of seed quality by WGAN-AFL.
High detectivity terahertz radiation sensing using frequency-noise-optimized nanomechanical resonators

We achieve high detectivity terahertz sensing using a silicon nitride nanomechanical resonator functionalized with a metasurface absorber. High performances are achieved by striking a fine balance between the frequency stability of the resonator, and its responsivity to absorbed radiation. Using this approach, we demonstrate a detectivity $D^*=3.4\times10^9~\mathrm{cm\cdot\sqrt{Hz}/W}$ and a noise equivalent power $\mathrm{NEP}=36~\mathrm{pW/\sqrt{Hz}}$ that outperform the best room-temperature on-chip THz detectors (i.e., pyroelectrics). Our optical absorber consists of a 1-mm diameter metasurface, which currently enables a 0.5-3 THz detection range but can easily be scaled to other frequencies in the THz and infrared ranges. In addition to demonstrating high-performance terahertz sensing, our work unveils an important fundamental trade-off between high frequency stability and high responsivity in thermal-based nanomechanical radiation sensors.
A Cross-Language Investigation into Jailbreak Attacks in Large Language Models

Large Language Models (LLMs) have become increasingly popular for their advanced text generation capabilities across various domains. However, like any software, they face security challenges, including the risk of 'jailbreak' attacks that manipulate LLMs to produce prohibited content. A particularly underexplored area is the Multilingual Jailbreak attack, where malicious questions are translated into various languages to evade safety filters. Currently, there is a lack of comprehensive empirical studies addressing this specific threat. To address this research gap, we conducted an extensive empirical study on Multilingual Jailbreak attacks. We developed a novel semantic-preserving algorithm to create a multilingual jailbreak dataset and conducted an exhaustive evaluation on both widely-used open-source and commercial LLMs, including GPT-4 and LLaMa. Additionally, we performed interpretability analysis to uncover patterns in Multilingual Jailbreak attacks and implemented a fine-tuning mitigation method. Our findings reveal that our mitigation strategy significantly enhances model defense, reducing the attack success rate by 96.2%. This study provides valuable insights into understanding and mitigating Multilingual Jailbreak attacks.
Tracing quantum correlations back to collective interferences

In this paper, we investigate the possibility of explaining nonclassical correlations between two quantum systems in terms of quantum interferences between collective states of the two systems. We achieve this by mapping the relations between different measurement contexts in the product Hilbert space of a pair of two-level systems onto an analogous sequence of interferences between paths in a single-particle interferometer. The paradoxical relations between different measurement outcomes can then be traced to the distribution of probability currents in the interferometer. We show that the relation between probability currents and correlations can be represented by continuous conditional (quasi)probability currents through the interferometer, given by weak values; the violation of the noncontextual assumption is expressed by negative conditional currents in some of the paths. Since negative conditional currents correspond to the assignment of negative conditional probabilities to measurements results in different measurement contexts, the necessity of such negative probability currents represents a failure of noncontextual local realism. Our results help to explain the meaning of nonlocal correlations in quantum mechanics, and support Feynman's claim that interference is the origin of all quantum phenomena.