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We explore the potential of conducting an experiment on the Chinese Space Station (CSS) to constrain beyond-the-standard-model (BSM) long-range spin- and velocity-dependent interactions, which are mediated by the exchange of an ultralight $\left(m_{z^{\prime}}<10^{-10}\text{eV}\right)$ or massless intermediate vector boson. We demonstrate that the proposed experiment on the CSS offers several advantages compared to ground-based experiments. The high speed can enhance the sensitivity to velocity-dependent interactions. The periodicity allows efficient extraction of signals from background noises, thereby strengthening the experiment's accuracy. Combining these advantages, one can improve the existing bounds on such interactions by up to five orders of magnitude. With advancements in sensor technology, we anticipate a further enhancement of four orders of magnitude, resulting in a total potential improvement of up to nine orders of magnitude., Comment: 6 pages, 4 figures; comments welcome

Controllable Depth-of-Field (DoF) imaging commonly produces amazing visual effects based on heavy and expensive high-end lenses. However, confronted with the increasing demand for mobile scenarios, it is desirable to achieve a lightweight solution with Minimalist Optical Systems (MOS). This work centers around two major limitations of MOS, i.e., the severe optical aberrations and uncontrollable DoF, for achieving single-lens controllable DoF imaging via computational methods. A Depth-aware Controllable DoF Imaging (DCDI) framework is proposed equipped with All-in-Focus (AiF) aberration correction and monocular depth estimation, where the recovered image and corresponding depth map are utilized to produce imaging results under diverse DoFs of any high-end lens via patch-wise convolution. To address the depth-varying optical degradation, we introduce a Depth-aware Degradation-adaptive Training (DA2T) scheme. At the dataset level, a Depth-aware Aberration MOS (DAMOS) dataset is established based on the simulation of Point Spread Functions (PSFs) under different object distances. Additionally, we design two plug-and-play depth-aware mechanisms to embed depth information into the aberration image recovery for better tackling depth-aware degradation. Furthermore, we propose a storage-efficient Omni-Lens-Field model to represent the 4D PSF library of various lenses. With the predicted depth map, recovered image, and depth-aware PSF map inferred by Omni-Lens-Field, single-lens controllable DoF imaging is achieved. Comprehensive experimental results demonstrate that the proposed framework enhances the recovery performance, and attains impressive single-lens controllable DoF imaging results, providing a seminal baseline for this field. The source code and the established dataset will be publicly available at https://github.com/XiaolongQian/DCDI., Comment: The source code and the established dataset will be publicly available at https://github.com/XiaolongQian/DCDI

Revealing the essence of dark matter (DM) and dark energy is essential for understanding our universe. Ultralight (rest energy $<$10 eV) bosonic particles, including pseudoscalar axions and axion-like particles (ALPs) have emerged among leading candidates to explain the composition of DM and searching for them has become an important part of precision-measurement science. Ultrahigh-sensitivity alkali-noble-gas based comagnetometers and magnetometers are being used as powerful haloscopes, i.e., devices designed to search for DM present in the galactic halo. A broad variety of such devices include clock-comparison comagnetometers, self-compensating comagnetometers, hybrid-spin-resonance magnetometer, spin-exchange-relaxation-free magnetometers, nuclear magnetic-resonance magnetometers, Floquet magnetometers, masers, as well as devices like the cosmic axion spin-precession experiment (CASPEr) using liquid $^{129}$Xe, prepolarized via spin-exchange optical pumping with rubidium atoms. The combination of alkali metal and noble gas allows one to take the best advantage of the complementary properties of the two spin systems. This review summarizes the operational principles, experimental setups and the successful explorations of new physics using these haloscopes. Additionally, some limiting factors are pointed out for further improvement.

Systolic arrays are a prominent choice for deep neural network (DNN) accelerators because they offer parallelism and efficient data reuse. Improving the reliability of DNN accelerators is crucial as hardware faults can degrade the accuracy of DNN inferencing. Systolic arrays make use of a large number of processing elements (PEs) for parallel processing, but when one PE is faulty, the error propagates and affects the outcomes of downstream PEs. Due to the large number of PEs, the cost associated with implementing hardware-based runtime monitoring of every single PE is infeasible. We present a solution to optimize the placement of hardware monitors within systolic arrays. We first prove that $2N-1$ monitors are needed to localize a single faulty PE and we also derive the monitor placement. We show that a second placement optimization problem, which minimizes the set of candidate faulty PEs for a given number of monitors, is NP-hard. Therefore, we propose a heuristic approach to balance the reliability and hardware resource utilization in DNN accelerators when number of monitors is limited. Experimental evaluation shows that to localize a single faulty PE, an area overhead of only 0.33% is incurred for a $256\times 256$ systolic array., Comment: Technical fallacies appear in this paper

As the complexity of System-on-Chip (SoC) designs continues to increase, ensuring thorough verification becomes a significant challenge for system integrators. The complexity of verification can result in undetected bugs. Unlike software or firmware bugs, hardware bugs are hard to fix after deployment and they require additional logic, i.e., patching logic integrated with the design in advance in order to patch. However, the absence of a standardized metric for defining "patchability" leaves system integrators relying on their understanding of each IP and security requirements to engineer ad hoc patching designs. In this paper, we propose a theoretical patchability quantification method to analyze designs at the Register Transfer Level (RTL) with provided patching options. Our quantification defines patchability as a combination of observability and controllability so that we can analyze and compare the patchability of IP variations. This quantification is a systematic approach to estimate each patching architecture's ability to patch at run-time and complements existing patching works. In experiments, we compare several design options of the same patching architecture and discuss their differences in terms of theoretical patchability and how many potential weaknesses can be mitigated.

Experiments aimed at detecting ultralight dark matter typically rely on resonant effects, which are sensitive to the dark matter mass that matches the resonance frequency. In this study, we investigate the nucleon couplings of ultralight axion dark matter using a magnetometer operating in a nuclear magnetic resonance (NMR) mode. Our approach involves the use of a $^{21}$Ne spin-based sensor, which features the lowest nuclear magnetic moment among noble-gas spins. This configuration allows us to achieve an ultrahigh sensitivity of 0.73 fT/Hz$^{1/2}$ at around 5 Hz, corresponding to energy resolution of approximately 1.5$\times 10^{-23}\,\rm{eV/Hz^{1/2}}$. Our analysis reveals that under certain conditions it is beneficial to scan the frequency with steps significantly larger than the resonance width. The analytical results are in agreement with experimental data and the scan strategy is potentially applicable to other resonant searches. Further, our study establishes stringent constraints on axion-like particles (ALP) in the 4.5--15.5 Hz Compton-frequency range coupling to neutrons and protons, improving on prior work by several-fold. Within a band around 4.6--6.6 Hz and around 7.5 Hz, our laboratory findings surpass astrophysical limits derived from neutron-star cooling. Hence, we demonstrate an accelerated resonance search for ultralight dark matter, achieving an approximately 30-fold increase in scanning step while maintaining competitive sensitivity., Comment: 13 pages, 11 figures, accepted by Communications Physics

Observational evidence suggests the existence of dark matter (DM), which comprises approximately $84.4\%$ of matter in the universe. Recent advances in tabletop quantum sensor technology have enabled searches for nongravitational interactions of DM. Our experiment named ChangE utilizes Coupled Hot Atom eNsembles to search for liGht dark mattEr and new physics. We identify a strongly-coupled hybrid spin-resonance (HSR) regime that enhances the bandwidth of $^{21}$Ne nuclear spin by three orders of magnitude while maintaining high sensitivity. In combination with a self-compensating mode (SC) for low frequencies, we present a comprehensive broadband search for axion-like dark matter with Compton frequencies in the range of $[0.01, 1000]$ Hz. We set new constraints on the DM interactions with neutrons and protons, accounting for the stochastic effect. For the axion-neutron coupling, our results reach a low value of $|g_{ann}|\le 3\times 10^{-10}$ in the frequency range $[0.02 , 4]$ Hz surpassing astrophysical limits and provide the strongest laboratory constraints in the $[10, 100]$ Hz range. For the axion-proton coupling, we offer the best terrestrial constraints for the frequency below 100 Hz., Comment: 14 pages, 5 figures

We use a formal correspondence between thermodynamics and inference, where the number of samples can be thought of as the inverse temperature, to study a quantity called ``learning capacity'' which is a measure of the effective dimensionality of a model. We show that the learning capacity is a useful notion of the complexity because (a) it correlates well with the test loss and it is a tiny fraction of the number of parameters for many deep networks trained on typical datasets, (b) it depends upon the number of samples used for training, (c) it is numerically consistent with notions of capacity obtained from PAC-Bayes generalization bounds, and (d) the test loss as a function of the learning capacity does not exhibit double descent. We show that the learning capacity saturates at very small and very large sample sizes; the threshold that characterizes the transition between these two regimes provides guidelines as to when one should procure more data and when one should search for a different architecture to improve performance. We show how the learning capacity can be used to provide a quantitative notion of capacity even for non-parametric models such as random forests and nearest neighbor classifiers.

It is challenging to extract semantic meanings directly from audio signals in spoken language understanding (SLU), due to the lack of textual information. Popular end-to-end (E2E) SLU models utilize sequence-to-sequence automatic speech recognition (ASR) models to extract textual embeddings as input to infer semantics, which, however, require computationally expensive auto-regressive decoding. In this work, we leverage self-supervised acoustic encoders fine-tuned with Connectionist Temporal Classification (CTC) to extract textual embeddings and use joint CTC and SLU losses for utterance-level SLU tasks. Experiments show that our model achieves 4% absolute improvement over the the state-of-the-art (SOTA) dialogue act classification model on the DSTC2 dataset and 1.3% absolute improvement over the SOTA SLU model on the SLURP dataset., Comment: ICASSP 2023

Personalization in multi-turn dialogs has been a long standing challenge for end-to-end automatic speech recognition (E2E ASR) models. Recent work on contextual adapters has tackled rare word recognition using user catalogs. This adaptation, however, does not incorporate an important cue, the dialog act, which is available in a multi-turn dialog scenario. In this work, we propose a dialog act guided contextual adapter network. Specifically, it leverages dialog acts to select the most relevant user catalogs and creates queries based on both -- the audio as well as the semantic relationship between the carrier phrase and user catalogs to better guide the contextual biasing. On industrial voice assistant datasets, our model outperforms both the baselines - dialog act encoder-only model, and the contextual adaptation, leading to the most improvement over the no-context model: 58% average relative word error rate reduction (WERR) in the multi-turn dialog scenario, in comparison to the prior-art contextual adapter, which has achieved 39% WERR over the no-context model., Comment: Accepted at ICASSP 2023

The application of sodium laser guide star is the key difference between modern adaptive optics system and traditional adaptive optics system. Especially in system like multi-conjugate adaptive optics, sodium laser guide star asterism which is formed by several laser guide stars in certain pattern is required to probe more atmospheric turbulence in different directions. To achieve this, a sodium laser guide star asterism launching platform is required. In this paper, we will introduce the sodium laser guide star asterism launching platform built and tested on the 1.8m telescope of the Gaomeigu Observatory. The platform has two functions: one is to compare the performance of sodium laser guide stars generated by different lasers at the same place; the other is to generate sodium laser guide star asterism with adjustable shape. The field test results at the beginning of 2021 verify the important role of the platform, which is also the first time to realize sodium laser guide star asterism in China.

Achieving high energy resolution in spin systems is important for fundamental physics research and precision measurements, with alkali-noble-gas comagnetometers being among the best available sensors. We found a new relaxation mechanism in such devices, the gradient of the Fermi-contact-interaction field that dominates the relaxation of hyperpolarized nuclear spins. We report on precise control over spin distribution, demonstrating a tenfold increase of nuclear spin hyperpolarization and transverse coherence time with optimal hybrid optical pumping. Operating in the self-compensation regime, our $^{21}$Ne-Rb-K comagnetometer achieves an ultrahigh inertial rotation sensitivity of $3\times10^{-8}$\,rad/s/Hz$^{1/2}$ in the frequency range from 0.2 to 1.0 Hz, which is equivalent to the energy resolution of $3.1\times 10^{-23}$\,eV/Hz$^{1/2}$. We propose to use this comagnetometer to search for exotic spin-dependent interactions involving proton and neutron spins. The projected sensitivity surpasses the previous experimental and astrophysical limits by more than four orders of magnitude.

To help prevent hardware security vulnerabilities from propagating to later design stages where fixes are costly, it is crucial to identify security concerns as early as possible, such as in RTL designs. In this work, we investigate the practical implications and feasibility of producing a set of security-specific scanners that operate on Verilog source files. The scanners indicate parts of code that might contain one of a set of MITRE's common weakness enumerations (CWEs). We explore the CWE database to characterize the scope and attributes of the CWEs and identify those that are amenable to static analysis. We prototype scanners and evaluate them on 11 open source designs - 4 system-on-chips (SoC) and 7 processor cores - and explore the nature of identified weaknesses. Our analysis reported 53 potential weaknesses in the OpenPiton SoC used in Hack@DAC-21, 11 of which we confirmed as security concerns.