Search

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

Ultra-thin (UT) oxide semiconductors are promising candidates for back-end-of-line (BEOL) compatible transistors and monolithic three-dimensional integration. Experimentally, UT indium oxide (In$_2$O$_3$) field-effect transistors (FETs) with thicknesses down to 0.4 nm exhibits extremely high drain current (10000 $\mu$A/$\mu$m) and transconductance (4000 $\mu$S/$\mu$m). Here, we employ the ab initio quantum transport simulation to investigate the performance limit of sub-5-nm gate length (Lg) UT In$_2$O$_3$ FET. Based on the International Technology Roadmap for Semiconductors (ITRS) criteria for high-performance (HP) devices, the scaling limit of UT In$_2$O$_3$ FETs can reach 2 nm in terms of on-state current, delay time, and power dissipation. The wide bandgap nature of UT In$_2$O$_3$ (3.15 eV) renders it a suitable candidate for ITRS low-power (LP) electronics with Lg down to 3 nm. Both the HP and LP UT In$_2$O$_3$ FETs exhibit superior energy-delay products as compared to other common 2D semiconductors such as monolayer MoS2 and MoTe2. Our study unveils the immense promise of UT In$_2$O$_3$ for both HP and LP device applications., Comment: 16 pages, 7 figures

We present optical, infrared, ultraviolet, and radio observations of SN 2022xkq, an underluminous fast-declining type Ia supernova (SN Ia) in NGC 1784 ($\mathrm{D}\approx31$ Mpc), from $<1$ to 180 days after explosion. The high-cadence observations of SN 2022xkq, a photometrically transitional and spectroscopically 91bg-like SN Ia, cover the first days and weeks following explosion which are critical to distinguishing between explosion scenarios. The early light curve of SN 2022xkq has a red early color and exhibits a flux excess which is more prominent in redder bands; this is the first time such a feature has been seen in a transitional/91bg-like SN Ia. We also present 92 optical and 19 near-infrared (NIR) spectra, beginning 0.4 days after explosion in the optical and 2.6 days after explosion in the NIR. SN 2022xkq exhibits a long-lived C I 1.0693 $\mu$m feature which persists until 5 days post-maximum. We also detect C II $\lambda$6580 in the pre-maximum optical spectra. These lines are evidence for unburnt carbon that is difficult to reconcile with the double detonation of a sub-Chandrasekhar mass white dwarf. No existing explosion model can fully explain the photometric and spectroscopic dataset of SN 2022xkq, but the considerable breadth of the observations is ideal for furthering our understanding of the processes which produce faint SNe Ia., Comment: 38 pages, 16 figures, accepted for publication in ApJ, the figure 15 input models and synthetic spectra are now available at https://zenodo.org/record/8379254

We present optical and near-infrared observations of SN~2022crv, a stripped envelope supernova in NGC~3054, discovered within 12 hrs of explosion by the Distance Less Than 40 Mpc Survey. We suggest SN~2022crv is a transitional object on the continuum between SNe Ib and SNe IIb. A high-velocity hydrogen feature ($\sim$$-$20,000 -- $-$16,000 $\rm km\,s^{-1}$) was conspicuous in SN~2022crv at early phases, and then quickly disappeared around maximum light. By comparing with hydrodynamic modeling, we find that a hydrogen envelope of $\sim 10^{-3}$ \msun{} can reproduce the behaviour of the hydrogen feature observed in SN~2022crv. The early light curve of SN~2022crv did not show envelope cooling emission, implying that SN~2022crv had a compact progenitor with extremely low amount of hydrogen. The analysis of the nebular spectra shows that SN~2022crv is consistent with the explosion of a He star with a final mass of $\sim$4.5 -- 5.6 \msun{} that has evolved from a $\sim$16 -- 22 \msun{} zero-age main sequence star in a binary system with about 1.0 -- 1.7 \msun{} of oxygen finally synthesized in the core. The high metallicity at the supernova site indicates that the progenitor experienced a strong stellar wind mass loss. In order to retain a small amount of residual hydrogen at such a high metallicity, the initial orbital separation of the binary system is likely larger than $\sim$1000~$\rm R_{\odot}$. The near-infrared spectra of SN~2022crv show a unique absorption feature on the blue side of He I line at $\sim$1.005~$\mu$m. This is the first time that such a feature has been observed in a Type Ib/IIb, and could be due to \ion{Sr}{2}. Further detailed modelling on SN~2022crv can shed light on the progenitor and the origin of the mysterious absorption feature in the near infrared., Comment: 33 pages, 23 figures, submitted to ApJ

We present a comprehensive data set of supernova (SN) 2016adj located within the central dust lane of Centaurus A. SN 2016adj is significantly reddened and after correcting the peak apparent $B$-band magnitude ($m_B = 17.48\pm0.05$) for Milky Way reddening and our inferred host-galaxy reddening parameters (i.e., $R_{V}^{host} = 5.7\pm0.7$ and $A_{V}^{host} = 6.3\pm0.2$), we estimate it reached a peak absolute magnitude of $M_B \sim -18$. Detailed inspection of the optical/NIR spectroscopic time-series reveals a carbon-rich SN Ic and not a SN Ib/IIb as previously suggested in the literature. The NIR spectra shows prevalent carbon-monoxide formation occurring already by +41 days past $B$-band maximum, which is $\approx 11$ days earlier than previously reported in the literature for this object. Interestingly around two months past maximum, the NIR spectrum of SN~2016adj begins to exhibit H features, with a +97~d medium resolution spectrum revealing both Paschen and Bracket lines with absorption minima of $\sim 2000$ km/s, full-width-half-maximum emission velocities of $\sim 1000$ km/s, and emission line ratios consistent with a dense emission region. We speculate these attributes are due to circumstellar interaction (CSI) between the rapidly expanding SN ejecta and a H-rich shell of material formed during the pre-SN phase. A bolometric light curve is constructed and a semi-analytical model fit suggests the supernova synthesized 0.5 solar masses of $^{56}$Ni and ejected 4.2 solar masses of material, though these values should be approached with caution given the large uncertainties associated with the adopted reddening parameters, possible CSI contamination, and known light echo emission. Finally, inspection of Hubble Space Telescope archival data yielded no progenitor detection., Comment: Submitted to A&A, comments are welcome

Gate-all-around (GAA) nanowire (NW) field-effect transistor (FET) is a promising device architecture due to its superior gate controllability than that of the conventional FinFET architecture. The significantly higher electron mobility of indium phosphide (InP) NW than silicon NW makes it particularly well-suited for high-performance (HP) electronics applications. In this work, we perform an ab initio quantum transport simulation to investigate the performance limit of sub-5-nm gate length (Lg) GAA InP NW FETs. The GAA InP NW FETs with Lg of 4 nm can meet the International Technology Roadmap for Semiconductors (ITRS) requirements for HP devices from the perspective of on-state current, delay time, and power dissipation. We also investigate the impact of strain on 3-nm-Lg GAA InP NW FETs. The application of tensile strain results in a remarkable increase of over 60% in the on-state current. These results highlight the potential of GAA InP NW FETs for HP applications in the sub-5-nm Lg region., Comment: 22 pages, 7 figures

Nebular-phase observations of peculiar Type Ia supernovae (SNe Ia) provide important constraints on progenitor scenarios and explosion dynamics for both these rare SNe and the more common, cosmologically useful SNe Ia. We present observations from an extensive ground-based and space-based follow-up campaign to characterize SN 2022pul, a "super-Chandrasekhar" mass SN Ia (alternatively "03fg-like" SN), from before peak brightness to well into the nebular phase across optical to mid-infrared (MIR) wavelengths. The early rise of the light curve is atypical, exhibiting two distinct components, consistent with SN Ia ejecta interacting with dense carbon-oxygen rich circumstellar material (CSM). In the optical, SN 2022pul is most similar to SN 2012dn, having a low estimated peak luminosity ($M_{B}=-18.9$ mag) and high photospheric velocity relative to other 03fg-like SNe. In the nebular phase, SN 2022pul adds to the increasing diversity of the 03fg-like subclass. From 168 to 336 days after peak $B$-band brightness, SN 2022pul exhibits asymmetric and narrow emission from [O I] $\lambda\lambda 6300,\ 6364$ (${\rm FWHM} \approx 2{,}000$ km s$^{-1}$), strong, broad emission from [Ca II] $\lambda\lambda 7291,\ 7323$ (${\rm FWHM} \approx 7{,}300$ km s$^{-1}$), and a rapid Fe III to Fe II ionization change. Finally, we present the first-ever optical-to-mid-infrared (MIR) nebular spectrum of an 03fg-like SN Ia using data from JWST. In the MIR, strong lines of neon and argon, weak emission from stable nickel, and strong thermal dust emission (with $T \approx 500$ K), combined with prominent [O I] in the optical, suggest that SN 2022pul was produced by a white dwarf merger within carbon/oxygen-rich CSM., Comment: 23 pages, 11 figures, submitted to ApJ

We present an analysis of ground-based and JWST observations of SN~2022pul, a peculiar "03fg-like" (or "super-Chandrasekhar") Type Ia supernova (SN Ia), in the nebular phase at 338d post explosion. Our combined spectrum continuously covers 0.4--14 $\mu$m and includes the first mid-infrared spectrum of an 03fg-like SN Ia. Compared to normal SN Ia 2021aefx, SN 2022pul exhibits a lower mean ionization state, asymmetric emission-line profiles, stronger emission from the intermediate-mass elements (IMEs) argon and calcium, weaker emission from iron-group elements (IGEs), and the first unambiguous detection of neon in a SN Ia. Strong, broad, centrally peaked [Ne II] line at 12.81 $\mu$m was previously predicted as a hallmark of "violent merger'' SN Ia models, where dynamical interaction between two sub-$M_{ch}$ white dwarfs (WDs) causes disruption of the lower mass WD and detonation of the other. The violent merger scenario was already a leading hypothesis for 03fg-like SNe Ia; in SN 2022pul it can explain the large-scale ejecta asymmetries seen between the IMEs and IGEs and the central location of narrow oxygen and broad neon. We modify extant models to add clumping of the ejecta to better reproduce the optical iron emission, and add mass in the innermost region ($< 2000$ km s$^{-1}$) to account for the observed narrow [O I]~$\lambda\lambda6300$, 6364 emission. A violent WD-WD merger explains many of the observations of SN 2022pul, and our results favor this model interpretation for the subclass of 03fg-like SN Ia., Comment: 20 pages, 10 figures, published in ApJ

We present an analysis of Type Ia Supernovae (SNe~Ia) from both the Carnegie Supernova Project~I (CSP-I) and II (CSP-II), and extend the Hubble diagram from the optical to the near-infrared wavelengths ($uBgVriYJH$). We calculate the Hubble constant, $H_0$, using various distance calibrators: Cepheids, Tip of the Red Giant Branch (TRGB), and Surface Brightness Fluctuations (SBF). Combining all methods of calibrations, we derive $\rm H_0=71.76 \pm 0.58 \ (stat) \pm 1.19 \ (sys) \ km \ s^{-1} \ Mpc^{-1}$ from $B$-band, and $\rm H_0=73.22 \pm 0.68 \ (stat) \pm 1.28 \ (sys) \ km \ s^{-1} \ Mpc^{-1}$ from $H$-band. By assigning equal weight to the Cepheid, TRGB, and SBF calibrators, we derive the systematic errors required for consistency in the first rung of the distance ladder, resulting in a systematic error of $1.2\sim 1.3 \rm \ km \ s^{-1} \ Mpc^{-1}$ in $H_0$. As a result, relative to the statistics-only uncertainty, the tension between the late-time $H_0$ we derive by combining the various distance calibrators and the early-time $H_0$ from the Cosmic Microwave Background is reduced. The highest precision in SN~Ia luminosity is found in the $Y$ band ($0.12\pm0.01$ mag), as defined by the intrinsic scatter ($\sigma_{int}$). We revisit SN~Ia Hubble residual-host mass correlations and recover previous results that these correlations do not change significantly between the optical and the near-infrared wavelengths. Finally, SNe~Ia that explode beyond 10 kpc from their host centers exhibit smaller dispersion in their luminosity, confirming our earlier findings. Reduced effect of dust in the outskirt of hosts may be responsible for this effect., Comment: Revised calculations are made. Will be resubmitted to ApJ

In this work, we study the random metric for the critical long-range percolation on $\mathbb{Z}^d$. A recent work by B\"aumler [3] implies the subsequential scaling limit, and our main contribution is to prove that the subsequential limit is uniquely characterized by a natural list of axioms. Our proof method is hugely inspired by recent works of Gwynne and Miller [42], and Ding and Gwynne [25] on the uniqueness of Liouville quantum gravity metrics., Comment: 100 pages,17 figures

With the proliferation of short video applications, the significance of short video recommendations has vastly increased. Unlike other recommendation scenarios, short video recommendation systems heavily rely on feedback from watch time. Existing approaches simply treat watch time as a direct label, failing to effectively harness its extensive semantics and introduce bias, thereby limiting the potential for modeling user interests based on watch time. To overcome this challenge, we propose a framework named Debiased Multiple-semantics-extracting Labeling(DML). DML constructs labels that encompass various semantics by utilizing quantiles derived from the distribution of watch time, prioritizing relative order rather than absolute label values. This approach facilitates easier model learning while aligning with the ranking objective of recommendations. Furthermore, we introduce a method inspired by causal adjustment to refine label definitions, thereby directly mitigating bias at the label level. We substantiate the effectiveness of our DML framework through both online and offline experiments. Extensive results demonstrate that our DML could effectively leverage watch time to discover users' real interests, enhancing their engagement in our application., Comment: 8 pages, 4 figures

The sustainable development of next-generation device technology is paramount in the face of climate change and the looming energy crisis. Tremendous efforts have been made in the discovery and design of nanomaterials that achieve device-level sustainability, where high performance and low operational energy cost are prioritized. However, many of such materials are composed of elements that are under threat of depletion and pose elevated risks to the environment. The role of material-level sustainability in computational screening efforts remains an open question thus far. Here we develop a general van der Waals materials screening framework imbued with sustainability-motivated search criteria. Using ultrawide bandgap (UWBG) materials as a backdrop -- an emerging materials class with great prospects in dielectric, power electronics, and ultraviolet device applications, we demonstrate how this screening framework results in 25 sustainable UWBG layered materials comprising only of low-risks elements. Our findings constitute a critical first-step towards reinventing a more sustainable electronics landscape beyond silicon, with the framework established in this work serving as a harbinger of sustainable 2D materials discovery., Comment: 17 pages, 8 figures