Search

Robust surface states are a defining feature of topological photonic and acoustic systems. Although the supercell technique is commonly used for studying their surface effects, it falls short in sorting the states at two boundaries, differentiating surface states from bulk states, and demanding large computational resources. To overcome the limitations, we employ surface Green's functions combined with two efficient algorithms to obtain the ideal surface states for a semi-infinite system. They are further extended to more complex scenarios, such as coated structures, heterostructures, and sandwiched structures. Photonic Chern insulators, valley photonic crystals, and acoustic topological insulators are taken as examples for demonstration and verification. The surface spectra obtained from our approach can be directly used for comparison with experimental observables. Our approach enriches the efficient computational tools for topological photonics and acoustics., Comment: 11 pages, 7 figures

We conduct a reverberation mapping (RM) campaign to spectroscopically monitor a sample of selected bright active galactic nuclei with large anticipated broad-line region (BLR) sizes adequate for spectroastrometric observations by the GRAVITY instrument on the Very Large Telescope Interferometer. We report the first results for five objects, IC 4329A, Mrk 335, Mrk 509, Mrk 1239, and PDS 456, among which Mrk 1239 and PDS 456 are for the first time spectroscopically monitored. We obtain multi-year monitoring data and perform multi-component spectral decomposition to extract the broad H$\beta$ profiles. We detect significant time lags between the H$\beta$ and continuum variations, generally obeying the previously established BLR size-luminosity relation. Velocity-resolved H$\beta$ time lags illustrate diverse, possibly evolving BLR kinematics. We further measure the H$\beta$ line widths from mean and rms spectra and the resulting virial products show good consistency among different seasons. Adopting a unity virial factor and the full width at half maximum of the broad H$\beta$ line from the mean spectrum as the measure of velocity, the obtained black hole mass averaged over seasons is $\log M_\bullet/M_\odot=8.02_{-0.14}^{+0.09}$, $6.92_{-0.12}^{+0.12}$, $8.01_{-0.25}^{+0.16}$, $7.44_{-0.14}^{+0.13}$, and $8.59_{-0.11}^{+0.07}$ for the five objects, respectively. The black hole mass estimations using other line width measures are also reported (up to the virial factors). For objects with previous RM campaigns, our mass estimates are in agreement with earlier results. In a companion paper, we will employ BLR dynamical modeling to directly infer the black hole mass and thereby determine the virial factors., Comment: 32 pages, 6 tables, 20 figures. To appear in ApJ

LLMs driven products were increasingly prevalent in our daily lives, With a natural language based interaction style, people may potentially leak their personal private information. Thus, privacy policy and user agreement played an important role in regulating and alerting people. However, there lacked the work examining the reading of LLM's privacy policy. Thus, we conducted the first user study to let participants read the privacy policy and user agreement with two different styles (a cursory and detailed style). We found users lack important information upon cursory reading and even detailed reading. Besides, their privacy concerns was not solved even upon detailed reading. We provided four design implications based on the findings.

The current safeguard mechanisms for large language models (LLMs) are indeed susceptible to jailbreak attacks, making them inherently fragile. Even the process of fine-tuning on apparently benign data for downstream tasks can jeopardize safety. One potential solution is to conduct safety fine-tuning subsequent to downstream fine-tuning. However, there's a risk of catastrophic forgetting during safety fine-tuning, where LLMs may regain safety measures but lose the task-specific knowledge acquired during downstream fine-tuning. In this paper, we introduce a safety realignment framework through subspace-oriented model fusion (SOMF), aiming to combine the safeguard capabilities of initially aligned model and the current fine-tuned model into a realigned model. Our approach begins by disentangling all task vectors from the weights of each fine-tuned model. We then identify safety-related regions within these vectors by subspace masking techniques. Finally, we explore the fusion of the initial safely aligned LLM with all task vectors based on the identified safety subspace. We validate that our safety realignment framework satisfies the safety requirements of a single fine-tuned model as well as multiple models during their fusion. Our findings confirm that SOMF preserves safety without notably compromising performance on downstream tasks, including instruction following in Chinese, English, and Hindi, as well as problem-solving capabilities in Code and Math.

A recent experiment has reported a nitrogen-doped lutetium hydride acheving a remarkable Tc of 294 K at just 1 GPa, significantly reducing the required pressure for obtaining room temperature superconductivity. However, subsequent experimental and theoretical investigations have encountered difficulties in replicating these results, leaving the structure of this Lu-H-N compound shrouded in uncertainty. Here, we propose a stable structure for Lu$_4$H$_7$N employing first-principles calculations. Our calculations reveal that Lu$_4$H$_7$N has a Tc of 1.044 K, which can be substantially enhanced to 11.721 K at 150 GPa, due to the increasing electron-phonon coupling (EPC). Notably, we delve into the nontrivial Z2 band topology of Lu$_4$H$_7$N, featuring discernible surface states near the Fermi level, and we explore its spin Hall conductivity characteristics. Furthermore, we find that the electron doping can enhance the EPC strength and Tc of Lu$_4$H$_7$N, such as the Lu$_4$H$_7$O structure we predict simulating electron doping for Lu$_4$H$_7$N with an impressive Tc of 3.837 K. This work demonstrates the coexistence of superconducting and topological properties in a Lu-H-N system compound, which holds the promise of guiding the search for novel topological superconducting materials.

Photo stimulated luminescent materials are one of the most attractive alternatives for next generation optical information storage technologies. However, there are still some challenges in regulating appropriate energy levels in luminescent materials for optical information storage. Herein, a green emission nanophosphor Zn2SiO4: Cr3+, Mn2+ with the trap depth of 1.05 eV, fulfilling the requirements for optical information storage, was fabricated for the first time through the solution combustion method and subsequent heat treatment at 1000 degree centigrade for 2h. The crystal structure, micromorphology, photoluminescence (PL), photoluminescence excitation (PLE), and afterglow properties of Zn2SiO4: xCr3+, yMn2+ were studied systematically. By applying the strategy of trap depth engineering, high trap density with proper trap depth was observed when Cr3+ ions were introduced into Zn2SiO4: Mn2+. Thermoluminescence (TL) glow curve analysis through the initial rise (IR) method was conducted to gain some insight into the information of traps. As proof of application, information storage was experimentally achieved by choosing 275 nm illumination for information writing and 980 nm NIR excitation for information reading. The results indicate that Zn2SiO4: Cr3+, Mn2+ phosphor holds promise for potential applications in the field of optical information storage.

We report first-time reverberation mapping results for 14 AGNs from the ongoing Monitoring AGNs with H$\beta$ Asymmetry campaign (MAHA). These results utilize optical spectra obtained with the Long Slit Spectrograph on the Wyoming Infrared 2.3m Telescope between 2017 November-2023 May. MAHA combines long-duration monitoring with high cadence. We report results from multiple observing seasons for 9 of the 14 objects. These results include H$\beta$ time lags, supermassive black hole masses, and velocity-resolved time lags. The velocity-resolved lags allow us to investigate the kinematics of the broad-line region., Comment: 35 pages, 19 figures, accepted for publication in ApJ Supplement

Despite previous DETR-like methods having performed successfully in generic object detection, tiny object detection is still a challenging task for them since the positional information of object queries is not customized for detecting tiny objects, whose scale is extraordinarily smaller than general objects. Also, DETR-like methods using a fixed number of queries make them unsuitable for aerial datasets, which only contain tiny objects, and the numbers of instances are imbalanced between different images. Thus, we present a simple yet effective model, named DQ-DETR, which consists of three different components: categorical counting module, counting-guided feature enhancement, and dynamic query selection to solve the above-mentioned problems. DQ-DETR uses the prediction and density maps from the categorical counting module to dynamically adjust the number of object queries and improve the positional information of queries. Our model DQ-DETR outperforms previous CNN-based and DETR-like methods, achieving state-of-the-art mAP 30.2% on the AI-TOD-V2 dataset, which mostly consists of tiny objects. Our code will be available at https://github.com/Katie0723/DQ-DETR., Comment: Accepted by ECCV 2024. Our code will be available at https://github.com/Katie0723/DQ-DETR

We propose the piecemeal quantum telescope through bit-by-bit iteration using different baselines. It improves precision exponentially with number of baselines, and it works robustly under large observation errors such as statistical errors, channel noise, operational errors and so on. For example, under the noisy channel of random phase drifts, our method can provide an exponential precision provided that the detected data is not entirely noisy. Being fault tolerant to statistical error, it requests only a small number of incident single-photons in detecting the star angle with exponential precision. As a result, it requests to detect only a few hundreds of photons from the target star for a precision breaking classical limit by 4 to 5 magnitude orders. This demonstrates a super efficiency., Comment: 6 figures

Vascular activities offer valuable signatures for psychological monitoring applications. We present CogniDot, an affordable, miniature skin sensor placed on the temporal area on the head that senses cognitive loads with a single-pixel color sensor. With its energy-efficient design, bio-compatible adhesive, and compact size (22mm diameter, 8.5mm thickness), it is ideal for long-term monitoring of mind status. We showed in detail the hardware design of our sensor. The user study results with 12 participants show that CogniDot can accurately differentiate between three levels of cognitive loads with a within-user accuracy of 97%. We also discuss its potential for broader applications.

La$_{3}$Ni$_{2}$O$_{7}$ has garnered widespread interest recently due to its high-temperature superconductivity under pressure, accompanied by charge density wave (CDW) ordering and metal-insulator (MI) transitions in the phase diagram. Here, we reveal with comprehensive calculations that La$_{3}$Ni$_{2}$O$_{7}$ possesses an antiferromagnetic ground state under both low and high pressures, with the strong Fermi surface nesting contributed by the flat band that leads to phonon softening and electronic instabilities. Several stable CDW orders with oxygen octahedral distortions are identified, which can trigger the MI transitions. The estimated CDW transition temperature ($\approx$120 K) at ambient pressure agrees nicely with experimental results. In the presence of apical oxygen vacancies, we identify two different phases, say, half distortion and full distortion phases, respectively, and their competition can lead to a pressure-induced MI transition, in good agreement with experimental observations. In addition, we find that the electron-phonon coupling is too small to contribute to superconductivity. These results appear to indicate an unconventional superconducting pairing mechanism mediated by antiferromagnetic fluctuations. A phase diagram that is consistent with the experimental results is given. The present results not only explain the origins of experimentally observed CDW and MI transitions, but also provide insight for deeply understanding the properties like superconductivity, CDW and the role of oxygen vacancies in pressurized La$_{3}$Ni$_{2}$O$_{7}$.

Impressive results have been achieved in natural language processing (NLP) tasks through the training of large language models (LLMs). However, these models occasionally produce toxic content such as insults, threats, and profanity in response to certain prompts, thereby constraining their practical utility. To tackle this issue, various finetuning-based and decoding-based approaches have been utilized to mitigate toxicity. However, these methods typically necessitate additional costs such as high-quality training data or auxiliary models. In this paper, we propose fine-grained detoxification via instance-level prefixes (FGDILP) to mitigate toxic text without additional cost. Specifically, FGDILP contrasts the contextualized representation in attention space using a positive prefix-prepended prompt against multiple negative prefix-prepended prompts at the instance level. This allows for constructing fine-grained subtoxicity vectors, which enables collaborative detoxification by fusing them to correct the normal generation process when provided with a raw prompt. We validate that FGDILP enables controlled text generation with regard to toxicity at both the utterance and context levels. Our method surpasses prompt-based baselines in detoxification, although at a slight cost to generation fluency and diversity.

The emergence of virtual avatars provides innovative opportunities for remote conferencing, education, and more. Our study investigates how the realism of avatars, used by native English speakers, impacts the anxiety levels of English as a Second Language (ESL) speakers during interactions. ESL participants engaged in conversations with native English speakers represented through cartoonish avatars, realistic-like avatars, or actual video streams. We measured both the ESL speakers' self-reported anxiety and their physiological indicators of anxiety. Our findings show that interactions with native speakers using cartoonish avatars or direct video lead to reduced anxiety levels among ESL participants. However, interactions with avatars that closely resemble humans heightened these anxieties. These insights are critically important for the design and application of virtual avatars, especially in addressing cross-cultural communication barriers and enhancing user experience., Comment: 13 pages, 7 figures, 8 tables

We investigate the two-channel nonreciprocal scattering of a coherent atomic ensemble under the linear spatial Kramers-Kronig modulation, which has potential applications in chiral phase modulation and broadband coherent perfect/asymmetric absorber that yet is typically unavailable in conventional continuous atomic media. In the regime of electromagnetically induced transparency, we observe the direction-dependent (chiral) phase modulation, which may enrich the burgeoning chiral quantum optics and can be used for implementing photonic filters, unidirectional amplifiers, and coherent asymmetric absorbers. By simplifying the stringent generation condition of coherent perfect absorption (CPA), we demonstrate the possibility of realizing two-channel CPA with broadband and sharp edges. Our proposal may be used to design and integrate some all-optical functional devices at extremely low power levels for quantum information processing and optical communication networks.

Distributed quantum computing provides a viable approach towards scalable quantum computation, which relies on nonlocal quantum gates to connect distant quantum nodes, to overcome the limitation of a single device. However, such an approach has only been realized within single nodes or between nodes separated by a few tens of meters, preventing the target of harnessing computing resources in large-scale quantum networks. Here, we demonstrate distributed quantum computing between two nodes spatially separated by 7.0 km, using stationary qubits based on multiplexed quantum memories, flying qubits at telecom wavelengths, and active feedforward control based on field-deployed fiber. Specifically, we illustrate quantum parallelism by implementing Deutsch-Jozsa algorithm and quantum phase estimation algorithm between the two remote nodes. These results represent the first demonstration of distributed quantum computing over metropolitan-scale distances and lay the foundation for the construction of large-scale quantum computing networks relying on existing fiber channels., Comment: 6 pages, 3 figures

The recently discovered ATi$_3$Bi$_5$ (A=Cs, Rb) exhibit intriguing quantum phenomena including superconductivity, electronic nematicity, and abundant topological states, which provide promising platforms for studying kagome superconductivity, band topology, and charge orders. In this work, we comprehensively study various properties of ATi$_3$Bi$_5$ including superconductivity under pressure and doping, band topology under pressure, thermal conductivity, heat capacity, electrical resistance, and spin Hall conductivity (SHC) using first-principles calculations. Calculated superconducting transition temperature ($\mathrm{ T_{c}}$) of CsTi$_3$Bi$_5$ and RbTi$_3$Bi$_5$ at ambient pressure are about 1.85 and 1.92K. When subject to pressure, $\mathrm{ T_{c}}$ of CsTi$_3$Bi$_5$ exhibits a special valley and dome shape, which arises from quasi-two-dimensional to three-dimensional isotropic compression within the context of an overall decreasing trend. Furthermore, $\mathrm{ T_{c}}$ of RbTi$_3$Bi$_5$ can be effectively enhanced up to 3.09K by tuning the kagome van Hove singularities (VHSs) and flat band through doping. Pressure can also induce abundant topological surface states at the Fermi energy ($\mathrm{E}_{\mathrm{F}}$) and tune VHSs across $\mathrm{E}_{\mathrm{F}}$. Additionally, our transport calculations are in excellent agreement with recent experiments, confirming the absence of charge density wave. Notably, SHC of CsTi$_3$Bi$_5$ can reach as large as 226$ \hbar\cdot (e\cdot \Omega \cdot cm) ^{-1} $ at $\mathrm{E}_{\mathrm{F}}$. Our work provides a timely and detailed analysis of the rich physical properties for ATi$_3$Bi$_5$, offering valuable insights for further explorations and understandings on these intriguing superconducting materials., Comment: 11 pages, 5 figures

The non-Hermitian skin effect (NHSE) undermines the conventional bulk-boundary correspondence (BBC) since it results in a distinct bulk spectrum in open-boundary systems compared to the periodic counterpart. Using the non-Hermitian (NH) Su-Schrieffer-Heeger (SSH) model as an example, we propose an intuitive approach, termed ``doubling and swapping" method, to restore the BBC. Explicitly, we construct a modified system by swapping the asymmetric intracell hoppings in every second primitive unit cell, such that it has double-sized unit cells compared to the NH SSH model and is free of NHSE. Importantly, the modified system and the NH SSH chain exhibit identical spectra under open boundary conditions (OBC). As a result, the modified system can serve as the valid bulk for defining topological invariants that correctly predicts edge states and topological phase transitions. The basic principle is applicable to many other systems such as the non-Hermitian Creutz ladder model. Furthermore, we extend the study to disordered systems in which the asymmetric hoppings are randomly swapped. We show that two types of winding numbers can be defined to account for the NHSE and topological edge states, respectively., Comment: 4 figures

Physical world adversarial attack is a highly practical and threatening attack, which fools real world deep learning systems by generating conspicuous and maliciously crafted real world artifacts. In physical world attacks, evaluating naturalness is highly emphasized since human can easily detect and remove unnatural attacks. However, current studies evaluate naturalness in a case-by-case fashion, which suffers from errors, bias and inconsistencies. In this paper, we take the first step to benchmark and assess visual naturalness of physical world attacks, taking autonomous driving scenario as the first attempt. First, to benchmark attack naturalness, we contribute the first Physical Attack Naturalness (PAN) dataset with human rating and gaze. PAN verifies several insights for the first time: naturalness is (disparately) affected by contextual features (i.e., environmental and semantic variations) and correlates with behavioral feature (i.e., gaze signal). Second, to automatically assess attack naturalness that aligns with human ratings, we further introduce Dual Prior Alignment (DPA) network, which aims to embed human knowledge into model reasoning process. Specifically, DPA imitates human reasoning in naturalness assessment by rating prior alignment and mimics human gaze behavior by attentive prior alignment. We hope our work fosters researches to improve and automatically assess naturalness of physical world attacks. Our code and dataset can be found at https://github.com/zhangsn-19/PAN.