Direct Detection of Dark Photon Dark Matter with the James Webb Space Telescope

In this study, we propose an investigation into dark photon dark matter (DPDM) within the infrared frequency band, utilizing highly sensitive infrared light detectors commonly integrated into space telescopes, such as the James Webb Space Telescope (JWST). The presence of DPDM induces electron oscillations in the reflector of these detectors. Consequently, these oscillating electrons can emit monochromatic electromagnetic waves with a frequency almost equivalent to the mass of DPDM. By employing the stationary phase approximation, we can demonstrate that when the size of the reflector significantly exceeds the wavelength of the electromagnetic wave, the contribution to the electromagnetic wave field at a given position primarily stems from the surface unit perpendicular to the relative position vector. This simplification results in the reduction of electromagnetic wave calculations to ray optics. By applying this concept to JWST, our analysis of observational data demonstrates the potential to establish constraints on the kinetic mixing between the photon and dark photon within the range [10, 500] THz. Despite JWST not being optimized for DPDM searches, our findings reveal constraints comparable to those obtained from the XENON1T experiment in the laboratory, as well as astrophysical constraints from solar emission. Additionally, we explore strategies to optimize future experiments specifically designed for DPDM searches

RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Model

Inspired by the recent success of large language models (LLMs) like ChatGPT, researchers start to explore the adoption of LLMs for agile hardware design, such as generating design RTL based on natural-language instructions. However, in existing works, their target designs are all relatively simple and in a small scale, and proposed by the authors themselves, making a fair comparison among different LLM solutions challenging. In addition, many prior works only focus on the design correctness, without evaluating the design qualities of generated design RTL. In this work, we propose an open-source benchmark named RTLLM, for generating design RTL with natural language instructions. To systematically evaluate the auto-generated design RTL, we summarized three progressive goals, named syntax goal, functionality goal, and design quality goal. This benchmark can automatically provide a quantitative evaluation of any given LLM-based solution. Furthermore, we propose an easy-to-use yet surprisingly effective prompt engineering technique named self-planning, which proves to significantly boost the performance of GPT-3.5 in our proposed benchmark

A Preliminary Study on Innovative Absorption Systems that Utilize Low-Temperature Geothermal Energy for Air-Conditioning Buildings

Air conditioning (A/C) systems driven by renewable energy have been studied extensively during the past decade as promising alternatives to conventional electricity-driven vapor compression A/C to alleviate stress on the grid as well as reduce CO2 emissions. Among the possible renewable energy sources to drive A/C systems, low-temperature geothermal heat ( \u3c 150°C/300°F) is quite underdeveloped despite its abundance in the United States and the unique advantage of steady output regardless of the weather compared to other renewable energy sources. A major barrier to wider utilization is the typically long distances between geothermal sources and potential end uses. In order to overcome this barrier, an innovative two-step geothermal absorption (TSGA) system was studied. With this system, the low-temperature geothermal energy is stored and transported at ambient temperature with an energy density of 360 kJ of cooling energy per kg of shipped LiBr/H2O solution (about three times higher than hot water for typical space heating applications). Key design parameters of a 900 ton TSGA chiller have been determined based on computer simulations with ORNL’s SorpSim software. A case study for applying the TSGA system at a large office building in Houston, TX indicates that, for a 10-mile distance from the geothermal site to the building, the simple payback of the TSGA system is 11 years compared with a conventional electric-driven chiller. To further improve the density of the transported energy, thereby reducing transportation cost and improving payback, a new system using 3-phase-sorption technology is proposed. In this system crystallized salt solution is used to boost the transported energy density. A preliminary study of this new system shows that the enhanced energy density has potential to significantly improve payback

Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deployment

With the rapid growth of computing powers and recent advances in deep learning, we have witnessed impressive demonstrations of novel robot capabilities in research settings. Nonetheless, these learning systems exhibit brittle generalization and require excessive training data for practical tasks. To harness the capabilities of state-of-the-art robot learning models while embracing their imperfections, we present Sirius, a principled framework for humans and robots to collaborate through a division of work. In this framework, partially autonomous robots are tasked with handling a major portion of decision-making where they work reliably; meanwhile, human operators monitor the process and intervene in challenging situations. Such a human-robot team ensures safe deployments in complex tasks. Further, we introduce a new learning algorithm to improve the policy's performance on the data collected from the task executions. The core idea is re-weighing training samples with approximated human trust and optimizing the policies with weighted behavioral cloning. We evaluate Sirius in simulation and on real hardware, showing that Sirius consistently outperforms baselines over a collection of contact-rich manipulation tasks, achieving an 8% boost in simulation and 27% on real hardware than the state-of-the-art methods, with twice faster convergence and 85% memory size reduction. Videos and code are available at https://ut-austin-rpl.github.io/sirius

RTLCoder: Outperforming GPT-3.5 in Design RTL Generation with Our Open-Source Dataset and Lightweight Solution

The automatic generation of RTL code (e.g., Verilog) using natural language instructions and large language models (LLMs) has attracted significant research interest recently. However, most existing approaches heavily rely on commercial LLMs such as ChatGPT, while open-source LLMs tailored for this specific design generation task exhibit notably inferior performance. The absence of high-quality open-source solutions restricts the flexibility and data privacy of this emerging technique. In this study, we present a new customized LLM solution with a modest parameter count of only 7B, achieving better performance than GPT-3.5 on two representative benchmarks for RTL code generation. This remarkable balance between accuracy and efficiency is made possible by leveraging our new RTL code dataset and a customized LLM algorithm, both of which will be made fully open-source. Furthermore, we have successfully quantized our LLM to 4-bit with a total size of 4GB, enabling it to function on a single laptop with only slight performance degradation. This efficiency allows the RTL generator to serve as a local assistant for engineers, ensuring all design privacy concerns are addressed

Direct detection of dark photon dark matter using radio telescopes

Dark photons can be the ultralight dark matter candidate, interacting with Standard Model particles via kinetic mixing. We propose to search for ultralight dark photon dark matter (DPDM) through the local absorption at different radio telescopes. The local DPDM can induce harmonic oscillations of electrons inside the antenna of radio telescopes. It leads to a monochromatic radio signal and can be recorded by telescope receivers. Using the observation data from the FAST telescope, the upper limit on the kinetic mixing can already reach $10^{-12}$ for DPDM oscillation frequencies at $1-1.5$ GHz, which is stronger than the cosmic microwave background constraint by about one order of magnitude. Furthermore, large-scale interferometric arrays like LOFAR and SKA1 telescopes can achieve extraordinary sensitivities for direct DPDM search from 10 MHz to 10 GHz.Comment: 5 pages, 3 figures + appendix. Match the accepted version (PRL

Proteasome activator 28A: A clinical biomarker and pharmaceutical target in acute cerebral infarction therapy

Purpose: To determine the dynamic changes in serum levels of PA28α in patients with acute cerebral infarction (ACI), and to investigate its correlation with infarct size and neurological deficit of the disease. Methods: A total of 100 ACI patients and 100 healthy volunteers were recruited from The First Affiliated Hospital of Xinxiang Medical University as case and control groups, respectively. Their serum levels of PA28α were determined by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). The potential of PA28α in predicting the incidence of ACI was assessed by plotting ROC curves. Multivariate logistic regression analysis was conducted to investigate the risk factors of ACI. In addition, an ACI model in rats was established, and ACI rats were classified into 1, 3, 5, 7 and 14 day subgroups based on the duration post-ACI. Rats in the sham group served as control. Results: Serum level of PA28α was significantly higher in ACI patients than in controls. Moreover, the serum level of PA28α at admission was positively correlated to the NIHSS score and infarct volume of ACI patients. The level of PA28α in ACI rats gradually increased post-ACI, reaching a peak on day 7. The number of apoptotic brain cells in ACI rats gradually decreased after ACI. In addition, PA28α level was negatively correlated to the number of apoptotic brain cells in ACI rats (R2 = 0.5148, p < 0.001). Conclusion: The serum level of PA28α is elevated in ACI patients, and is positively correlated to infarct volume and neurological deficit of the disease. The dynamic change in brain cell apoptosis post-ACI is negatively correlated to the serum level of PA28α. These findings may provide theoretical basis for the diagnosis and treatment of ACI

A novel cuproptosis pattern and tumor immune microenvironment characterization in urothelial carcinoma of the bladder

BackgroundUrothelial carcinoma of the bladder (UCB) is the most prevalent malignant tumor of the urinary system worldwide, which has a significant recurrence rate despite multiple treatment options available. As a unique and novel copper-dependent programmed cell death mechanism, the comprehensive impact of cuproptosis on the tumor immune microenvironment, clinicopathological characteristics and the prognosis of patients remains largely unclear.MethodsA total of 568 UCB samples were thoroughly examined for cuproptosis patterns using data downloaded from TCGA and GEO, based on 10 cuproptosis-related genes reported previously. Then, the univariate COX regression analysis was performed on the genes that differed across the various patterns. To measure individual cuproptosis pattern, a cuproptosis score system was constructed using a principal component analysis algorithm. To validate the scoring system, immunohistochemical staining was performed on tumor tissues with different pathological grades, and experiments in vitro were conducted about the differentially expressed genes related to prognosis. Finally, the capacity of scoring system to predict the response to immunotherapy was verified by using data from IMvigor 210 cohort.ResultsFour unique cuproptosis clusters and two gene clusters were finally found by the investigation. The clinical features and prognosis of patients, as well as the mRNA transcriptome, pathway enrichment, and immune cell infiltration in TME, varied dramatically between various cuproptosis clusters and gene clusters. To identify individual cuproptosis patterns in UCB patients, we also established a cuproptosis scoring system. After validation with multiple methods, it was indicated that the score system could predict the prognosis of UCB patients and was significantly connected to clinical features such TNM category, tumor grade, molecular type and ultimate survival status. The clinical outcomes of UCB patients were predicted effectively according to the tumor mutation burden in conjunction with the scoring system. Furthermore, we found that the cuproptosis score had a significant correlation with the response to immunotherapy and the sensitivity to chemotherapy.ConclusionThis study revealed the potential impact of cuproptosis on the UCB tumor immune microenvironment and clinical pathological characteristics. The cuproptosis score system could effectively predict the prognosis of patients and the response to chemotherapy and immunotherapy

Spin Squeezing with Arbitrary Quadratic Collective-Spin Interaction

Spin squeezing is vitally important in quantum metrology and quantum information science. The noise reduction resulting from spin squeezing can surpass the standard quantum limit and even reach the Heisenberg Limit (HL) in some special circumstances. However, systems that can reach the HL are very limited. Here we study the spin squeezing in atomic systems with a generic form of quadratic collective-spin interaction, which can be described by the Lipkin-Meshkov-Glick(LMG) model. We find that the squeezing properties are determined by the initial states and the anisotropic parameters. Moreover, we propose a pulse rotation scheme to transform the model into two-axis twisting model with Heisenberg-limited spin squeezing. Our study paves the way for reaching HL in a broad variety of systems