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Non-Hermitian (NH) systems exhibit intricate spectral topology arising from complex-valued eigenenergies, with positive/negative imaginary parts representing gain/loss. Unlike the orthogonal eigenstates of Hermitian systems, NH systems feature left and right eigenstates that form a biorthogonal basis and can differ significantly, showcasing pronounced skewness between them. These characteristics give rise to unique properties absent in Hermitian systems, such as the NH skin effect and ultra spectral sensitivity. However, conventional experimental techniques are inadequate for directly measuring the complex-valued spectra and left and right eigenstates -- key elements for enhancing our knowledge of NH physics. This challenge is particularly acute in higher-dimensional NH systems, where the spectra and eigenstates are highly sensitive to macroscopic shapes, lattice geometry, and boundary conditions, posing greater experimental demands compared to one-dimensional systems. Here, we present a Green's function-based method that enables the direct measurement and characterization of both complex-valued energy spectra and the left and right eigenstates in arbitrary NH lattices. Using active acoustic crystals as the experimental platform, we observe spectral transitions and eigenstate skewness in two-dimensional NH lattices under both nonreciprocal and reciprocal conditions, with varied geometries and boundary conditions. Our approach renders complex spectral topology and left eigenstates experimentally accessible and practically meaningful, providing new insights into these quantities. The results not only confirm recent theoretical predictions of higher-dimensional NH systems but also establish a universal and versatile framework for investigating complex spectral properties and NH dynamics across a wide range of physical platforms.

Prenatal ultrasound evaluates fetal growth and detects congenital abnormalities during pregnancy, but the examination of ultrasound images by radiologists requires expertise and sophisticated equipment, which would otherwise fail to improve the rate of identifying specific types of fetal central nervous system (CNS) abnormalities and result in unnecessary patient examinations. We construct a deep learning model to improve the overall accuracy of the diagnosis of fetal cranial anomalies to aid prenatal diagnosis. In our collected multi-center dataset of fetal craniocerebral anomalies covering four typical anomalies of the fetal central nervous system (CNS): anencephaly, encephalocele (including meningocele), holoprosencephaly, and rachischisis, patient-level prediction accuracy reaches 94.5%, with an AUROC value of 99.3%. In the subgroup analyzes, our model is applicable to the entire gestational period, with good identification of fetal anomaly types for any gestational period. Heatmaps superimposed on the ultrasound images not only provide a visual interpretation for the algorithm but also provide an intuitive visual aid to the physician by highlighting key areas that need to be reviewed, helping the physician to quickly identify and validate key areas. Finally, the retrospective reader study demonstrates that by combining the automatic prediction of the DL system with the professional judgment of the radiologist, the diagnostic accuracy and efficiency can be effectively improved and the misdiagnosis rate can be reduced, which has an important clinical application prospect.

The evolution of colour vision is captivating, as it reveals the adaptive strategies of extinct species while simultaneously inspiring innovations in modern imaging technology. In this study, we present a simplified model of visual transduction in the retina, introducing a novel opsin layer. We quantify evolutionary pressures by measuring machine vision recognition accuracy on colour images shaped by specific opsins. Building on this, we develop an evolutionary conservation optimisation algorithm to reconstruct the spectral sensitivity of opsins, enabling mutation-driven adaptations to to more effectively spot fruits or predators. This model condenses millions of years of evolution within seconds on GPU, providing an experimental framework to test long-standing hypotheses in evolutionary biology , such as vision of early mammals, primate trichromacy from gene duplication, retention of colour blindness, blue-shift of fish rod and multiple rod opsins with bioluminescence. Moreover, the model enables speculative explorations of hypothetical species, such as organisms with eyes adapted to the conditions on Mars. Our findings suggest a minimalist yet effective approach to task-specific camera filter design, optimising the spectral response function to meet application-driven demands. The code will be made publicly available upon acceptance., Comment: 15 pages, 6 figures

We study the dynamics of the focusing nonlinear Hartree equation with a Kato potential $$ i\partial_t u +\Delta u - Vu = -(|\cdot|^{-\gamma} \ast |u|^2)u, \quad x \in \mathbb{R}^d $$ under some assumptions on the potential $V$. We prove the blow up versus global existence dichotomy for solutions beyond the threshold, based on the method from Duyckaerts-Roudenko [6]. Furthermore, our result compensates for the one of in [13] below that threshold.

Compared to traditional electrodynamic loudspeakers, the parametric array loudspeaker (PAL) offers exceptional directivity for audio applications but suffers from significant nonlinear distortions due to its inherent intricate demodulation process. The Volterra filter-based approaches have been widely used to reduce these distortions, but the effectiveness is limited by its inverse filter's capability. Specifically, its pth-order inverse filter can only compensate for nonlinearities up to the pth order, while the higher-order nonlinearities it introduces continue to generate lower-order harmonics. In contrast, this paper introduces the modern deep learning methods for the first time to address nonlinear identification and compensation for PAL systems. Specifically, a feedforward variant of the WaveNet neural network, recognized for its success in audio nonlinear system modeling, is utilized to identify and compensate for distortions in a double sideband amplitude modulation-based PAL system. Experimental measurements from 250 Hz to 8 kHz demonstrate that our proposed approach significantly reduces both total harmonic distortion and intermodulation distortion of audio sound generated by PALs, achieving average reductions to 4.55% and 2.47%, respectively. This performance is notably superior to results obtained using the current state-of-the-art Volterra filter-based methods. Our work opens new possibilities for improving the sound reproduction performance of PALs., Comment: 5 pages, 7 figures

In this paper, we study the blow up and scattering result of the solution to the focusing nonlinear Hartree equation with potential $$i\partial_t u +\Delta u - Vu = - (|\cdot|^{-3} \ast |u|^2)u, \qquad (t, x) \in \mathbb{R} \times \mathbb{R}^5 $$ in the energy space ${H}^1(\mathbb{R}^5)$ below the mass-energy threshold. The potential $V$ we considered is an extension of Kato potential in some sense. We extend the results of Meng [26] to nonlinear Hartree equation with potential $V$ under some conditions. By establishing a Virial-Morawetz estimate and a scattering criteria, we obtain the scattering theory based on the method from Dodson-Murphy [11].

Stripped-envelope supernovae (SESNe) represent a significant fraction of core-collapse supernovae, arising from massive stars that have shed their hydrogen and, in some cases, helium envelopes. The origins and explosion mechanisms of SESNe remain a topic of active investigation. In this work, we employ radiative-transfer simulations to model the light curves and spectra of a set of explosions of single, solar-metallicity, massive Wolf-Rayet (WR) stars with ejecta masses ranging from 4 to 11 Msun, that were computed from a turbulence-aided and neutrino-driven explosion mechanism. We analyze these synthetic observables to explore the impact of varying ejecta mass and helium content on observable features. We find that the light curve shape of these progenitors with high ejecta masses is consistent with observed SESNe with broad light curves but not the peak luminosities. The commonly used analytic formula based on rising bolometric light curves overestimates the ejecta mass of these high-initial-mass progenitor explosions by a factor up to 2.6. In contrast, the calibrated method by Haynie et al., which relies on late-time decay tails, reduces uncertainties to an average of 20% within the calibrated ejecta mass range.Spectroscopically, the He I 1.083 um line remains prominent even in models with as little as 0.02 Msun of helium. However, the strength of the optical He I lines is not directly proportional to the helium mass but instead depends on a complex interplay of factors such as 56Ni distribution, composition, and radiation field. Thus, producing realistic helium features requires detailed radiative transfer simulations for each new hydrodynamic model., Comment: 17 pages, 8 figures, accepted to APJ

Decoding the directional focus of an attended speaker from listeners' electroencephalogram (EEG) signals is essential for developing brain-computer interfaces to improve the quality of life for individuals with hearing impairment. Previous works have concentrated on binary directional focus decoding, i.e., determining whether the attended speaker is on the left or right side of the listener. However, a more precise decoding of the exact direction of the attended speaker is necessary for effective speech processing. Additionally, audio spatial information has not been effectively leveraged, resulting in suboptimal decoding results. In this paper, it is found that on the recently presented dataset with 14-class directional focus, models relying exclusively on EEG inputs exhibit significantly lower accuracy when decoding the directional focus in both leave-one-subject-out and leave-one-trial-out scenarios. By integrating audio spatial spectra with EEG features, the decoding accuracy can be effectively improved. The CNN, LSM-CNN, and Deformer models are employed to decode the directional focus from listeners' EEG signals and audio spatial spectra. The proposed Sp-EEG-Deformer model achieves notable 14-class decoding accuracies of 55.35% and 57.19% in leave-one-subject-out and leave-one-trial-out scenarios with a decision window of 1 second, respectively. Experiment results indicate increased decoding accuracy as the number of alternative directions reduces. These findings suggest the efficacy of our proposed dual modal directional focus decoding strategy., Comment: Submitted to IEEE TNSRE

Human understanding of language is robust to different word choices as far as they represent similar semantic concepts. To what extent does our human intuition transfer to language models, which represent all subwords as distinct embeddings? In this work, we take an initial step on measuring the role of shared semantics among subwords in the encoder-only multilingual language models (mLMs). To this end, we form "semantic tokens" by merging the semantically similar subwords and their embeddings, and evaluate the updated mLMs on 5 heterogeneous multilingual downstream tasks. Results show that the general shared semantics could get the models a long way in making the predictions on mLMs with different tokenizers and model sizes. Inspections on the grouped subwords show that they exhibit a wide range of semantic similarities, including synonyms and translations across many languages and scripts. Lastly, we found the zero-shot results with semantic tokens are on par or even better than the original models on certain classification tasks, suggesting that the shared subword-level semantics may serve as the anchors for cross-lingual transferring., Comment: 8 pages, 9 figures

Unlike biomarkers in biofluids, airborne biomarkers are dilute and difficult to trace. Detecting diverse airborne biomarkers with sufficient sensitivity typically relies on bulky and expensive equipment like mass spectrometers that remain inaccessible to the general population. Here, we introduce Airborne Biomarker Localization Engine (ABLE), a simple, affordable, and portable platform that can detect both volatile, non-volatile, molecular, and particulate biomarkers in about 15 minutes. ABLE significantly improves gas detection limits by converting dilute gases into droplets by water condensation, producing concentrated aqueous samples that are easy to be tested. Fundamental studies of multiphase condensation revealed unexpected stability in condensate-trapped biomarkers, making ABLE a reliable, accessible, and high-performance system for open-air-based biosensing applications such as non-contact infant healthcare, pathogen detection in public space, and food safety., Comment: 17 pages, 5 figures. An additional 67-page supplementary materials document containing a detailed description of methods, 15 additional discussions, 30 figures, and 3 tables, will be made available after the manuscript is published after peer-review process

Ultrathin oxide semiconductors with sub-1-nm thickness are promising building blocks for ultrascaled field-effect transistor (FET) applications due to their resilience against short-channel effects, high air stability, and potential for low-energy device operation. However, the n-type dominance of ultrathin oxide FET has hindered their integration into complementary metal-oxide-semiconductor (CMOS) technology, which requires both n-and p-type devices. Here we develop an ab initio device-driven computational screening workflow to identify sub-1-nm thickness oxide semiconductors for sub-5-nm FET applications. We demonstrate that ultrathin CaO2, CaO, and SrO are compatible with p-type device operations under both high-performance (HP) and low-power (LP) requirements specified by the International Technology Roadmap of Semiconductors (ITRS), thereby expanding the limited family of p-type oxide semiconductors. Notably, CaO and SrO emerge as the first-of-kind sub-1-nm thickness oxide semiconductors capable of simultaneously meeting the ITRS HP and LP criteria for both n-and p-type devices. CaO and SrO FETs outperform many existing low-dimensional semiconductors, exhibiting scalability below 5-nm gate length. Our findings offer a pioneering effort in the ab initio, device-driven screening of sub-1-nm thickness oxide semiconductors, significantly broadening the material candidate pool for future CMOS technology nodes., Comment: 23 pages, 5 figures, 1 table

The non-Hermitian skin effect (NHSE) is a distinctive topological phenomenon observed in nonHermitian systems. Recently, there has been considerable interest in exploring higher-order NHSE occurrences in two and three dimensions. In such systems, topological edge states collapse into a corner while bulk states remain delocalized. Through a Hermitian-non-Hermitian correspondence, this study predicts and experimentally observes the higher-order NHSE in an acoustic Kagome lattice possessing nonreciprocal hoppings. By rotating the frequency spectrum and employing complexfrequency excitation techniques, we observe the localization of acoustic energy towards a corner of the lattice in the topologically nontrivial phase, even when the source is located far from that corner. In contrast, the acoustic energy spreads out when excited at the frequencies hosting the bulk states. These observations are unequivocal evidence of the higher-order NHSE.

Quantum-mechanics-based transport simulation is of importance for the design of ultra-short channel field-effect transistors (FETs) with its capability of understanding the physical mechanism, while facing the primary challenge of the high computational intensity. Traditional machine learning is expected to accelerate the optimization of FET design, yet its application in this field is limited by the lack of both high-fidelity datasets and the integration of physical knowledge. Here, we introduced a physics-integrated neural network framework to predict the transport curves of sub-5-nm gate-all-around (GAA) FETs using an in-house developed high-fidelity database. The transport curves in the database are collected from literature and our first-principles calculations. Beyond silicon, we included indium arsenide, indium phosphide, and selenium nanowires with different structural phases as the FET channel materials. Then, we built a physical-knowledge-integrated hyper vector neural network (PHVNN), in which five new physical features were added into the inputs for prediction transport characteristics, achieving a sufficiently low mean absolute error of 0.39. In particular, ~98% of the current prediction residuals are within one order of magnitude. Using PHVNN, we efficiently screened out the symmetric p-type GAA FETs that possess the same figures of merit with the n-type ones, which are crucial for the fabrication of homogeneous CMOS circuits. Finally, our automatic differentiation analysis provides interpretable insights into the PHVNN, which highlights the important contributions of our new input parameters and improves the reliability of PHVNN. Our approach provides an effective method for rapidly screening appropriate GAA FETs with the prospect of accelerating the design process of next-generation electronic devices., Comment: 13 pages, 4 figures

Wide bandgap oxide semiconductors are very promising channel candidates for next-generation electronics due to their large-area manufacturing, high-quality dielectrics, low contact resistance, and low leakage current. However, the absence of ultra-short gate length (Lg) p-type transistors has restricted their application in future complementary metal-oxide-semiconductor (CMOS) integration. Inspired by the successfully grown high-hole mobility bilayer (BL) beta tellurium dioxide (\b{eta}-TeO2), we investigate the performance of sub-5-nm-Lg BL \b{eta}-TeO2 field-effect transistors (FETs) by utilizing first-principles quantum transport simulation. The distinctive anisotropy of BL \b{eta}-TeO2 yields different transport properties. In the y-direction, both the sub-5-nm-Lg n-type and p-type BL \b{eta}-TeO2 FETs can fulfill the International Technology Roadmap for Semiconductors (ITRS) criteria for high-performance (HP) devices, which are superior to the reported oxide FETs (only n-type). Remarkably, we for the first time demonstrate the existence of the NMOS and PMOS symmetry in sub-5-nm-Lg oxide semiconductor FETs. As to the x-direction, the n-type BL \b{eta}-TeO2 FETs satisfy both the ITRS HP and low-power (LP) requirements with Lg down to 3 nm. Consequently, our work shed light on the tremendous prospects of BL \b{eta}-TeO2 for CMOS application.

Graphene nanoribbon (GNR) emerges as an exceptionally promising channel candidate due to its tunable sizable bandgap (0-3 eV), ultrahigh carrier mobility (up to 4600 cm^(2) V^(-1) s^(-1)), and excellent device performance (current on-off ratio of 10^(7)). However, the asymmetry of reported n-type and p-type GNR field-effect transistors (FETs) at ultrashort gate length (Lg) has become an obstacle to future complementary metal-oxide-semiconductor (CMOS) integration. Here, we conduct ab initio quantum transport simulations to investigate the transport properties of sub-5-nm Lg 7 armchair-edge GNR (7 AGNR) FETs. The on-state current, delay time, and power dissipation of the n-type and p-type 7 AGNR FETs fulfill the International Technology Roadmap for Semiconductors targets for high-performance devices when Lg is reduced to 3 nm. Remarkably, the 7 AGNR FETs exhibit superior n-type and p-type symmetry to the 7-9-7 AGNR FETs due to the more symmetrical electron/hole effective masses. Compared to the monolayer MoS2 and MoTe2 counterparts, the 7 AGNR FETs have better device performance, which could be further improved via gate engineering. Our results shed light on the immense potential of 7 AGNR in advancing CMOS electronics beyond silicon.

Large Language Models (LLMs) have shown great potential in intelligent visualization systems, especially for domain-specific applications. Integrating LLMs into visualization systems presents challenges, and we categorize these challenges into three alignments: domain problems with LLMs, visualization with LLMs, and interaction with LLMs. To achieve these alignments, we propose a framework and outline a workflow to guide the application of fine-tuned LLMs to enhance visual interactions for domain-specific tasks. These alignment challenges are critical in education because of the need for an intelligent visualization system to support beginners' self-regulated learning. Therefore, we apply the framework to education and introduce Tailor-Mind, an interactive visualization system designed to facilitate self-regulated learning for artificial intelligence beginners. Drawing on insights from a preliminary study, we identify self-regulated learning tasks and fine-tuning objectives to guide visualization design and tuning data construction. Our focus on aligning visualization with fine-tuned LLM makes Tailor-Mind more like a personalized tutor. Tailor-Mind also supports interactive recommendations to help beginners better achieve their learning goals. Model performance evaluations and user studies confirm that Tailor-Mind improves the self-regulated learning experience, effectively validating the proposed framework.

Recommender systems require the simultaneous optimization of multiple objectives to accurately model user interests, necessitating the application of multi-task learning methods. However, existing multi-task learning methods in recommendations overlook the specific characteristics of recommendation scenarios, falling short in achieving proper gradient balance. To address this challenge, we set the target of multi-task learning as attaining the appropriate magnitude balance and the global direction balance, and propose an innovative methodology named GradCraft in response. GradCraft dynamically adjusts gradient magnitudes to align with the maximum gradient norm, mitigating interference from gradient magnitudes for subsequent manipulation. It then employs projections to eliminate gradient conflicts in directions while considering all conflicting tasks simultaneously, theoretically guaranteeing the global resolution of direction conflicts. GradCraft ensures the concurrent achievement of appropriate magnitude balance and global direction balance, aligning with the inherent characteristics of recommendation scenarios. Both offline and online experiments attest to the efficacy of GradCraft in enhancing multi-task performance in recommendations. The source code for GradCraft can be accessed at https://github.com/baiyimeng/GradCraft., Comment: Accepted by KDD'24

The significance of modeling long-term user interests for CTR prediction tasks in large-scale recommendation systems is progressively gaining attention among researchers and practitioners. Existing work, such as SIM and TWIN, typically employs a two-stage approach to model long-term user behavior sequences for efficiency concerns. The first stage rapidly retrieves a subset of sequences related to the target item from a long sequence using a search-based mechanism namely the General Search Unit (GSU), while the second stage calculates the interest scores using the Exact Search Unit (ESU) on the retrieved results. Given the extensive length of user behavior sequences spanning the entire life cycle, potentially reaching up to 10^6 in scale, there is currently no effective solution for fully modeling such expansive user interests. To overcome this issue, we introduced TWIN-V2, an enhancement of TWIN, where a divide-and-conquer approach is applied to compress life-cycle behaviors and uncover more accurate and diverse user interests. Specifically, a hierarchical clustering method groups items with similar characteristics in life-cycle behaviors into a single cluster during the offline phase. By limiting the size of clusters, we can compress behavior sequences well beyond the magnitude of 10^5 to a length manageable for online inference in GSU retrieval. Cluster-aware target attention extracts comprehensive and multi-faceted long-term interests of users, thereby making the final recommendation results more accurate and diverse. Extensive offline experiments on a multi-billion-scale industrial dataset and online A/B tests have demonstrated the effectiveness of TWIN-V2. Under an efficient deployment framework, TWIN-V2 has been successfully deployed to the primary traffic that serves hundreds of millions of daily active users at Kuaishou., Comment: Accepted by CIKM 2024

We analyze the single-photon band structure and the transport of a single photon in a one-dimensional coupled-spinning-resonator chain. The time-reversal symmetry of the resonators chain is broken by the spinning of the resonators, instead of external or synthetic magnetic field. Two nonreciprocal single-photon band gaps can be obtained in the coupled-spinning-resonator chain, whose width depends on the angular velocity of the spinning resonator. Based on the nonreciprocal band gaps, we can implement a single photon circulator at multiple frequency windows, and the direction of photon cycling is opposite for different band gaps. In addition, reciprocal single-photon band structures can also be realized in the coupled-spinning-resonator chain when all resonators rotate in the same direction with equal angular velocity. Our work open a new route to achieve, manipulate, and switch nonreciprocal or reciprocal single-photon band structures, and provides new opportunities to realize novel single-photon devices., Comment: 8 pages, 3 figures

Parametric array loudspeakers (PALs) are known for producing highly directional audio beams, a feat more challenging to achieve with conventional electro-dynamic loudspeakers (EDLs). Due to their intrinsic physical mechanisms, PALs hold promising potential for spatial audio applications such as virtual reality (VR). However, the feasibility of using an array of PALs for sound zone control (SZC) has remained unexplored, mainly due to the complexity of the nonlinear demodulation process inherent in PALs. Leveraging recent advancements in PAL modeling, this work proposes an optimization algorithm to achieve the acoustic contrast control (ACC) between two target areas using a PAL array. The performance and robustness of the proposed ACC-based SZC using PAL arrays are investigated through simulations, and the results are compared with those obtained using EDL arrays. The results show that the PAL array outperforms the EDL array in SZC performance and robustness at higher frequencies and lower signal-to-noise ratio, while being comparable under other conditions. This work paves the way for high-contrast acoustic control using PAL arrays.

We study a system composed by a three-level giant atom (3GA), a waveguide initially in the vacuum state, and a single-mode cavity. The 3GA-cavity system is in a strong-coupling regime, and the distance between the coupling points is comparable to the coherent length of a spontaneously emitted photon. The dynamics of the 3GA and its radiative field in the waveguide for long time are analyzed. Besides the steady value, we also found an oscillatory character of the excited state population, a signature of oscillating bound states which is generated by the superposition of two bound states in the continuum. The radiative field propagates in the cavity-like geometry formed by the coupling points. When one bound state is emergent, a sine-like interference pattern is visible for the emitted field intensity in spacetime. An oscillatory character in time and a beat in space for the emitted field intensity are observed when two bound states are emergent in a subspace. The wavelengths and the periods are controlled by the number of the photons in the cavity.

Despite significant progress made in the last decade, deep neural network (DNN) based speech enhancement (SE) still faces the challenge of notable degradation in the quality of recovered speech under low signal-to-noise ratio (SNR) conditions. In this letter, we propose an SNR-progressive speech enhancement model with harmonic compensation for low-SNR SE. Reliable pitch estimation is obtained from the intermediate output, which has the benefit of retaining more speech components than the coarse estimate while possessing a significant higher SNR than the input noisy speech. An effective harmonic compensation mechanism is introduced for better harmonic recovery. Extensive ex-periments demonstrate the advantage of our proposed model. A multi-modal speech extraction system based on the proposed backbone model ranks first in the ICASSP 2024 MISP Challenge: https://mispchallenge.github.io/mispchallenge2023/index.html.

In this work, we study the critical long-range percolation on $\mathbb{Z}$, where an edge connects $i$ and $j$ independently with probability $1-\exp\{-\beta |i-j|^{-2}\}$ for some fixed $\beta>0$. Viewing this as a random electric network where each edge has a unit conductance, we show that with high probability the effective resistances from the origin 0 to $[-N, N]^c$ and from the interval $[-N,N]$ to $[-2N,2N]^c$ (conditioned on no edge joining $[-N,N]$ and $[-2N,2N]^c$) both have a polynomial lower bound in $N$. Our bound holds for all $\beta>0$ and thus rules out a potential phase transition (around $\beta = 1$) which seemed to be a reasonable possibility., Comment: 26 pages, 10 figures

Sub-1-nm gate length $MoS_2$ transistors have been experimentally fabricated, but their device performance limit remains elusive. Herein, we explore the performance limits of the sub-1-nm gate length monolayer (ML) $MoS_2$ transistors through ab initio quantum transport simulations. Our simulation results demonstrate that, through appropriate doping and dielectric engineering, the sub-1-nm devices can meet the requirement of extended 'ITRS'(International Technology Roadmap for Semiconductors) $L_g$=0.34 nm. Following device optimization, we achieve impressive maximum on-state current densities of 409 $\mu A / \mu m$ for n-type and 800 $\mu A / \mu m$ for p-type high-performance (HP) devices, while n-type and p-type low-power (LP) devices exhibit maximum on-state current densities of 75 $\mu A / \mu m$ and 187 $\mu A / \mu m$, respectively. We employed the Wentzel-Kramer-Brillouin (WKB) approximation to explain the physical mechanisms of underlap and spacer region optimization on transistor performance. The underlap and spacer regions primarily influence the transport properties of sub-1-nm transistors by respectively altering the width and body factor of the potential barriers. Compared to ML $MoS_2$ transistors with a 1 nm gate length, our sub-1-nm gate length HP and LP ML $MoS_2$ transistors exhibit lower energy-delay products. Hence the sub-1-nm gate length transistors have immense potential for driving the next generation of electronics.

We present a method of extrapolating the spectroscopic behavior of Type Ia supernovae (SNe Ia) in the near-infrared (NIR) wavelength regime up to 2.30 $\mu$m using optical spectroscopy. Such a process is useful for accurately estimating K-corrections and other photometric quantities of SNe Ia in the NIR. Principal component analysis is performed on data consisting of Carnegie Supernova Project I & II optical and near-infrared FIRE spectra to produce models capable of making these extrapolations. This method differs from previous spectral template methods by not parameterizing models strictly by photometric light-curve properties of SNe Ia, allowing for more flexibility of the resulting extrapolated NIR flux. A difference of around -3.1% to -2.7% in the total integrated NIR flux between these extrapolations and the observations is seen here for most test cases including Branch core-normal and shallow-silicon subtypes. However, larger deviations from the observation are found for other tests, likely due to the limited high-velocity and broad-line SNe Ia in the training sample. Maximum-light principal components are shown to allow for spectroscopic predictions of the color-stretch light-curve parameter, $s_{BV}$, within approximately $\pm$0.1 units of the value measured with photometry. We also show these results compare well with NIR templates, although in most cases the templates are marginally more fitting to observations, illustrating a need for more concurrent optical+NIR spectroscopic observations to truly understand the diversity of SNe Ia in the NIR., Comment: 25 pages, 16 figures, ApJ, in press

Active headrests can reduce low-frequency noise around ears based on active noise control (ANC) system. Both the control system using fixed control filters and the remote microphone-based adaptive control system provide good noise reduction performance when the head is in the original position. However, their performance degrades significantly when the head is in motion. In this paper, a human ear-positioning system based on the depth camera is introduced to address this problem. The system uses RTMpose model to estimate the two-dimensional (2D) positions of the ears in the color frame, and then derives the corresponding three-dimensional (3D) coordinates in the depth frame with a depth camera. Experimental results show that the ear-positioning system can effectively track the movement of ears, and the broadband noise reduction performance of the active headrest combined with the system is significantly improved when the human head is translating or rotating.

Short video recommendations often face limitations due to the quality of user feedback, which may not accurately depict user interests. To tackle this challenge, a new task has emerged: generating more dependable labels from original feedback. Existing label generation methods rely on manual rules, demanding substantial human effort and potentially misaligning with the desired objectives of the platform. To transcend these constraints, we introduce LabelCraft, a novel automated label generation method explicitly optimizing pivotal operational metrics for platform success. By formulating label generation as a higher-level optimization problem above recommender model optimization, LabelCraft introduces a trainable labeling model for automatic label mechanism modeling. Through meta-learning techniques, LabelCraft effectively addresses the bi-level optimization hurdle posed by the recommender and labeling models, enabling the automatic acquisition of intricate label generation mechanisms. Extensive experiments on real-world datasets corroborate LabelCraft's excellence across varied operational metrics, encompassing usage time, user engagement, and retention. Codes are available at https://github.com/baiyimeng/LabelCraft., Comment: Accepted by WSDM'24

In this paper, we provide the sufficient and necessary conditions for the symmetry of the following stable L\'evy-type operator $\mathcal{L}$ on $\mathbb{R}$: $$\mathcal{L}=a(x){\Delta^{\alpha/2}}+b(x)\frac{\d}{\d x},$$ where $a,b$ are the continuous positive and differentiable functions, respectively. Under the assumption of symmetry, we further study the criteria for functional inequalities, including Poincar\'e inequalities, logarithmic Sobolev inequalities and Nash inequalities. Our proofs rely on the Orlicz space theory and the estimates of the Green functions.

Controllable single-photon routing plays an important role in quantum networks. We investigate single-photon scattering in two one-dimensional (1D) waveguides by a three-level emitter with a cascade configuration, which is a dipole coupled to an extra cavity. The tunneling path for the transmission of a single photon is switched by whether the extra cavity contains photons. For the setup, the Autler-Townes splitting is modulated by the extra cavity, in which the transmission valley (reflection range) width is tunable in terms of the number of photons in the extra cavity. Our investigation will be beneficial to single-photon routing in quantum networks using quantifiable photon numbers in an extra cavity., Comment: 7pages,5figures