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The Closeby Habitable Exoplanet Survey (CHES) constitutes a mission intricately designed to systematically survey approximately 100 solar-type stars located within the immediate proximity of the solar system, specifically within a range of 10 parsecs. The core objective of this mission is the detection and characterization of potentially habitable Earth-like planets or super-Earths within the habitable zone of these stars. The CHES mission obtains high-precision astrometric measurements of planets orbiting the target stars by observing angular distance variations between the target star and reference stars. As a result, we surveyed the relevant parameters of both target and reference stars in detail, conducting a thorough analysis and calculation of the required observation accuracy, the number of observations, and the priority assigned to each target star. Observational emphasis will be concentrated on targets considered of higher priority, ensuring the effectiveness of their observation capabilities. Through this approach, we formulate a five-year observation strategy that will cover all the target stars within a six-month timeframe. The strategy not only fulfills the required observing capability but also exhibit high efficiency simultaneously, providing an executable program for future mission. Over the span of the mission's five-year duration, a cumulative observation time of 29,220 hours will be available. Approximately 86 percent of this, totaling 25,120 hours, is allocated for the observation of target stars. This allocation leaves approximately 4,100 hours for extended scientific observation programs. We have also performed simulated observations based on this strategy and verified its observational capability for exoplanets., Comment: 20 pages, 12 figures, accepted for publication in AJ

To date, the dayside thermal structure of ultrahot Jupiters (UHJs) is generally considered to be inverted, but their nightside thermal structure has been less explored. Here we explore the impact of nightside thermal emission on high-resolution infrared transmission spectroscopy, which should not be neglected, especially for UHJs. We present a general equation for the high-resolution transmission spectrum that includes planetary nightside thermal emission. This provides a new way to infer the thermal structure of the planetary nightside with high-resolution transmission spectroscopy. Using the cross-correlation technique, we find evidence for the presence of an H$_2$O emission signature on the UHJ WASP-33b during the transit, indicating an inverted temperature structure on its nightside. Such a result suggests a stronger heat transport through the circulation than currently expected. An alternative explanation is that the rotating visible hemisphere during transit leads to the potential contribution of the limb and dayside atmospheres to the detected emission signature. In the future, the combination of high-resolution full-phase curve spectroscopic observations and general circulation models will hopefully solve this puzzle and provide a complete picture of the three-dimensional nature of the chemistry, circulation, and thermal structure of UHJs., Comment: 17 pages, 9 figures, 1 table. Accepted for publication in ApJL

In this report, we present our solutions to the EgoVis Challenges in CVPR 2024, including five tracks in the Ego4D challenge and three tracks in the EPIC-Kitchens challenge. Building upon the video-language two-tower model and leveraging our meticulously organized egocentric video data, we introduce a novel foundation model called EgoVideo. This model is specifically designed to cater to the unique characteristics of egocentric videos and provides strong support for our competition submissions. In the Ego4D challenges, we tackle various tasks including Natural Language Queries, Step Grounding, Moment Queries, Short-term Object Interaction Anticipation, and Long-term Action Anticipation. In addition, we also participate in the EPIC-Kitchens challenge, where we engage in the Action Recognition, Multiple Instance Retrieval, and Domain Adaptation for Action Recognition tracks. By adapting EgoVideo to these diverse tasks, we showcase its versatility and effectiveness in different egocentric video analysis scenarios, demonstrating the powerful representation ability of EgoVideo as an egocentric foundation model. Our codebase and pretrained models are publicly available at https://github.com/OpenGVLab/EgoVideo., Comment: Champion solutions in the EgoVis CVPR 2024 workshop

We introduce a holographic dark energy model that incorporates the first-order approximate Kaniadaski entropy, utilizing the Hubble horizon, $1/H$, as the infrared cutoff. We investigate the cosmological evolution within this framework. The model introduces an extra parameter relative to the $\Lambda$CDM model. It posits a Universe that is initially dominated by dark matter, which then evolves to a phase where dark energy becomes the predominant component, with this transition occurring at a redshift of approximately $z \sim 0.419$. The energy density of dark energy is ultimately expected to become constant, thereby circumventing the potential issue of a "big rip". Employing the most recent Type Ia supernova and Hubble parameter data, we constrain the model's parameters and find a Hubble constant of $H_0=72.8$ km/s/Mpc, thereby resolving the Hubble tension issue. The estimated age of the Universe, based on the best-fit parameter values, is $14.2$ Gyr. Furthermore, we predict the number of strong gravitational lenses and conduct statefinder and $Om$ diagnostic analyses to validate and characterize the model., Comment: 9 pages, 5 figures

Text-to-SQL is a technology that converts natural language queries into the structured query language SQL. A novel research approach that has recently gained attention focuses on methods based on the complexity of SQL queries, achieving notable performance improvements. However, existing methods entail significant storage and training costs, which hampers their practical application. To address this issue, this paper introduces a method for Text-to-SQL based on Refined Schema and Hardness Prompt. By filtering out low-relevance schema information with a refined schema and identifying query hardness through a Language Model (LM) to form prompts, this method reduces storage and training costs while maintaining performance. It's worth mentioning that this method is applicable to any sequence-to-sequence (seq2seq) LM. Our experiments on the Spider dataset, specifically with large-scale LMs, achieved an exceptional Execution accuracy (EX) of 82.6%, demonstrating the effectiveness and greater suitability of our method for real-world applications., Comment: 4 pages, 2 figures, 2024 6th International Conference on Electronic Engineering and Informatics (EEI 2024)

To incentivize flexible resources such as Battery Energy Storage Systems (BESSs) to offer Frequency Control Ancillary Services (FCAS), Australia's National Electricity Market (NEM) has implemented changes in recent years towards shorter-term bidding rules and faster service requirements. However, firstly, existing bidding optimization methods often overlook or oversimplify the key aspects of FCAS market procedures, resulting in an inaccurate depiction of the market bidding process. Thus, the BESS bidding problem is modeled based on the actual bidding records and the latest market specifications and then formulated as a deep reinforcement learning (DRL) problem. Secondly, the erratic decisions of the DRL agent caused by imperfectly predicted market information increases the risk of profit loss. Hence, a Conditional Value at Risk (CVaR)-based DRL algorithm is developed to enhance the risk resilience of bidding strategies. Thirdly, well-trained DRL models still face performance decline in uncommon scenarios during online operations. Therefore, a Large Language Models (LLMs)-assisted artificial intelligence (AI)-agent interactive decision-making framework is proposed to improve the strategy timeliness, reliability and interpretability in uncertain new scenarios, where conditional hybrid decision and self-reflection mechanisms are designed to address LLMs' hallucination challenge. The experiment results demonstrate that our proposed framework has higher bidding profitability compared to the baseline methods by effectively mitigating the profit loss caused by various uncertainties.

Distribution system state estimation (DSSE) plays a crucial role in the real-time monitoring, control, and operation of distribution networks. Besides intensive computational requirements, conventional DSSE methods need high-quality measurements to obtain accurate states, whereas missing values often occur due to sensor failures or communication delays. To address these challenging issues, a forecast-then-estimate framework of edge learning is proposed for DSSE, leveraging large language models (LLMs) to forecast missing measurements and provide pseudo-measurements. Firstly, natural language-based prompts and measurement sequences are integrated by the proposed LLM to learn patterns from historical data and provide accurate forecasting results. Secondly, a convolutional layer-based neural network model is introduced to improve the robustness of state estimation under missing measurement. Thirdly, to alleviate the overfitting of the deep learning-based DSSE, it is reformulated as a multi-task learning framework containing shared and task-specific layers. The uncertainty weighting algorithm is applied to find the optimal weights to balance different tasks. The numerical simulation on the Simbench case is used to demonstrate the effectiveness of the proposed forecast-then-estimate framework.

Black phosphorus nitrogen (BP-N) is an attractive high-energy-density material. However, high-pressure synthesized BP-N will decompose at low-pressure and cannot be quenched to ambient conditions. Finding a method to stabilize it at 0 GPa is of great significance for its practical applications. However, unlike cg-N, LP-N, and HLP-N, it is always a metastable phase at high-pressure up to 260 GPa, and decomposes into chains at 23 GPa. Here, based on the first-principles simulations, we find that P atom doping can effectively reduce the synthesis pressure of BP-N and maintain its stability at 0 GPa. Uniform distribution of P atom dopants within the layer helps maintain the structural stability of BP-N layer at 0 GPa, while interlayer electrostatic interaction induced by N-P dipoles enhances its dynamic stability by eliminating interlayer slipping. Furthermore, pressure is conducive to enhancing the stability of BP-N and its doped forms by suppressing N-chain dissociation. For the configuration with 12.5% doping concentration, a gravimetric energy density of 8.07 kJ/g can be realized, which is nearly two times higher than TNT., Comment: 27 pages, 6 figures

The spectral signatures of optical absorbers, when combined with those of infrared molecules, play a critical role in constraining the cloud properties of exoplanet atmospheres. We aim to use optical transmission spectroscopy to confirm the tentative color signature previously observed by multiband photometry in the atmosphere of hot Jupiter HAT-P-55b. We observed a transit of HAT-P-55b with the OSIRIS spectrograph on the Gran Telescopio Canarias (GTC). We created two sets of spectroscopic light curves using the conventional band-integrated method and the newly proposed pixel-based method to derive the transmission spectrum. We performed Bayesian spectral retrieval analyses on the transmission spectrum to interpret the observed atmospheric properties. The transmission spectra derived from the two methods are consistent, both spectrally resolving the tentative color signature observed by MuSCAT2. The retrievals on the combined OSIRIS and MuSCAT2 transmission spectrum yield the detection of Na at 5.5$\sigma$ and the tentative detection of MgH at 3.4$\sigma$. The current optical-only wavelength coverage cannot constrain the absolute abundances of the atmospheric species. Space-based observations covering the molecular infrared bands or ground-based high-resolution spectroscopy are needed to further constrain the atmospheric properties of HAT-P-55b., Comment: 10 pages, 6 figures, accepted for publication in A&A

The Closeby Habitable Exoplanet Survey (CHES) is dedicated to the astrometric exploration for habitable-zone Earth-like planets orbiting solar-type stars in close proximity, achieving unprecedented micro-arcsecond precision. Given the elevated precision, thorough consideration of photocenter jitters induced by stellar activity becomes imperative. This study endeavors to model the stellar activity of solar-type stars, compute astrometric noise, and delineate the detection limits of habitable planets within the astrometric domain. Simulations were conducted for identified primary targets of CHES, involving the generation of simulated observed data for astrometry and photometry, accounting for the impact of stellar activity. Estimation of activity levels in our samples was achieved through chromospheric activity indices, revealing that over 90% of stars exhibited photocenter jitters below 1 $\mu\mathrm{as}$. Notably, certain proximate stars, such as $\alpha$ Cen A and B, displayed more discernible noise arising from stellar activity. Subsequent tests were performed to evaluate detection performance, unveiling that stellar activity tends to have a less pronounced impact on planetary detectability for the majority of stars. Approximately 95% of targets demonstrated a detection efficiency exceeding 80%. However, for several cold stars, e.g., HD 32450 and HD 21531, with the habitable zones close to the stars, a reduction in detection efficiency was observed. These findings offer invaluable insights into the intricate interplay between stellar activity and astrometric precision, significantly advancing our understanding in the search for habitable planets., Comment: 18 pages, 10 figures, accepted for publication in AJ

Existing CNN-based speech separation models face local receptive field limitations and cannot effectively capture long time dependencies. Although LSTM and Transformer-based speech separation models can avoid this problem, their high complexity makes them face the challenge of computational resources and inference efficiency when dealing with long audio. To address this challenge, we introduce an innovative speech separation method called SPMamba. This model builds upon the robust TF-GridNet architecture, replacing its traditional BLSTM modules with bidirectional Mamba modules. These modules effectively model the spatiotemporal relationships between the time and frequency dimensions, allowing SPMamba to capture long-range dependencies with linear computational complexity. Specifically, the bidirectional processing within the Mamba modules enables the model to utilize both past and future contextual information, thereby enhancing separation performance. Extensive experiments conducted on public datasets, including WSJ0-2Mix, WHAM!, and Libri2Mix, as well as the newly constructed Echo2Mix dataset, demonstrated that SPMamba significantly outperformed existing state-of-the-art models, achieving superior results while also reducing computational complexity. These findings highlighted the effectiveness of SPMamba in tackling the intricate challenges of speech separation in complex environments., Comment: Technical Report. Code is available at https://github.com/JusperLee/SPMamba

Being able to map the activities of others into one's own point of view is one fundamental human skill even from a very early age. Taking a step toward understanding this human ability, we introduce EgoExoLearn, a large-scale dataset that emulates the human demonstration following process, in which individuals record egocentric videos as they execute tasks guided by demonstration videos. Focusing on the potential applications in daily assistance and professional support, EgoExoLearn contains egocentric and demonstration video data spanning 120 hours captured in daily life scenarios and specialized laboratories. Along with the videos we record high-quality gaze data and provide detailed multimodal annotations, formulating a playground for modeling the human ability to bridge asynchronous procedural actions from different viewpoints. To this end, we present benchmarks such as cross-view association, cross-view action planning, and cross-view referenced skill assessment, along with detailed analysis. We expect EgoExoLearn can serve as an important resource for bridging the actions across views, thus paving the way for creating AI agents capable of seamlessly learning by observing humans in the real world. Code and data can be found at: https://github.com/OpenGVLab/EgoExoLearn, Comment: CVPR 2024

We introduce InternVideo2, a new family of video foundation models (ViFM) that achieve the state-of-the-art results in video recognition, video-text tasks, and video-centric dialogue. Our core design is a progressive training approach that unifies the masked video modeling, crossmodal contrastive learning, and next token prediction, scaling up the video encoder size to 6B parameters. At the data level, we prioritize spatiotemporal consistency by semantically segmenting videos and generating video-audio-speech captions. This improves the alignment between video and text. Through extensive experiments, we validate our designs and demonstrate superior performance on over 60 video and audio tasks. Notably, our model outperforms others on various video-related dialogue and long video understanding benchmarks, highlighting its ability to reason and comprehend longer contexts. Code and models are available at https://github.com/OpenGVLab/InternVideo/tree/main/InternVideo2/., Comment: a technical report about video understanding (accepted to ECCV2024)

Understanding videos is one of the fundamental directions in computer vision research, with extensive efforts dedicated to exploring various architectures such as RNN, 3D CNN, and Transformers. The newly proposed architecture of state space model, e.g., Mamba, shows promising traits to extend its success in long sequence modeling to video modeling. To assess whether Mamba can be a viable alternative to Transformers in the video understanding domain, in this work, we conduct a comprehensive set of studies, probing different roles Mamba can play in modeling videos, while investigating diverse tasks where Mamba could exhibit superiority. We categorize Mamba into four roles for modeling videos, deriving a Video Mamba Suite composed of 14 models/modules, and evaluating them on 12 video understanding tasks. Our extensive experiments reveal the strong potential of Mamba on both video-only and video-language tasks while showing promising efficiency-performance trade-offs. We hope this work could provide valuable data points and insights for future research on video understanding. Code is public: https://github.com/OpenGVLab/video-mamba-suite., Comment: Technical Report

Cubic gauche nitrogen (cg-N) has received wide attention due to its high energy density and environmental friendliness. However, existing synthesis methods for cg-N predominantly rely on the high-pressure techniques, or the utilization of nanoconfined effects using highly toxic and sensitive sodium azide as precursor, which significantly restrict the practical application of cg-N as high energy density materials (HDEM). Here, based on the first-principles simulations, we find that the adsorption of potassium on the cg-N surface exhibits superior stabilization compared to sodium. Then, we chose the safer potassium azide as raw material for synthesizing cg-N. Through plasma-enhanced chemical vapor deposition treatment, the free-standing cg-N was successfully synthesized without the need of high-pressure and nanoconfined effects. Importantly, it demonstrates excellent thermal stability up to 760 K, and then a rapid and intense thermal decomposition occurs, exhibiting typical behaviors of HDEM thermal decomposition. Our work has significantly promoted the practical application of cg-N as HDEM.

We validate the Transiting Exoplanet Survey Satellite (TESS) object of interest TOI-2266.01 (TIC 348911) as a small transiting planet (most likely a super-Earth) orbiting a faint M5 dwarf ($V=16.54$) on a 2.33~d orbit. The validation is based on an approach where multicolour transit light curves are used to robustly estimate the upper limit of the transiting object's radius. Our analysis uses SPOC-pipeline TESS light curves from Sectors 24, 25, 51, and 52, simultaneous multicolour transit photometry observed with MuSCAT2, MuSCAT3, and HiPERCAM, and additional transit photometry observed with the LCOGT telescopes. TOI-2266 b is found to be a planet with a radius of $1.54\pm\0.09\,R_\oplus$, which locates it at the edge of the transition zone between rocky planets, water-rich planets, and sub-Neptunes (the so-called M~dwarf radius valley). The planet is amenable to ground-based radial velocity mass measurement with red-sensitive spectrographs installed in large telescopes, such as MAROON-X and Keck Planet Finder (KPF), which makes it a valuable addition to a relatively small population of planets that can be used to probe the physics of the transition zone. Further, the planet's orbital period of 2.33 days places it inside a `keystone planet' wedge in the period-radius plane where competing planet formation scenarios make conflicting predictions on how the radius valley depends on the orbital period. This makes the planet also a welcome addition to the small population of planets that can be used to test small-planet formation scenarios around M~dwarfs., Comment: Accepted to A&A

Accurate physical parameters of exoplanet systems are essential for further exploration of planetary internal structure, atmospheres, and formation history. We aim to use simultaneous multicolour transit photometry to improve the estimation of transit parameters, to search for transit timing variations (TTVs), and to establish which of our targets should be prioritised for follow-up transmission spectroscopy. We performed time series photometric observations of 12 transits for the hot Jupiters HAT-P-19b, HAT-P-51b, HAT-P-55b, and HAT-P-65b using the simultaneous four-colour camera MuSCAT2 on the Telescopio Carlos S\'anchez. We collected 56 additional transit light curves from TESS photometry. To derive transit parameters, we modelled the MuSCAT2 light curves with Gaussian processes to account for correlated noise. To derive physical parameters, we performed EXOFASTv2 global fits to the available transit and radial velocity data sets, together with the Gaia DR3 parallax, isochrones, and spectral energy distributions. To assess the potential for atmospheric characterisation, we compared the multicolour transit depths with a flat line and a clear atmosphere model. We consistently refined the transit and physical parameters. We improved the orbital period and ephemeris estimates, and found no evidence for TTVs or orbital decay. The MuSCAT2 broadband transmission spectra of HAT-P-19b and HAT-P-65b are consistent with previously published low-resolution transmission spectra. We also found that, except for HAT-P-65b, the assumption of a planetary atmosphere can improve the fit to the MuSCAT2 data. In particular, we identified HAT-P-55b as a priority target among these four planets for further atmospheric studies using transmission spectroscopy., Comment: Corrected a typo in T0 of HAT-P-65b (the correct value is 2458139.35067)

Understanding human actions from videos of first-person view poses significant challenges. Most prior approaches explore representation learning on egocentric videos only, while overlooking the potential benefit of exploiting existing large-scale third-person videos. In this paper, (1) we develop EgoInstructor, a retrieval-augmented multimodal captioning model that automatically retrieves semantically relevant third-person instructional videos to enhance the video captioning of egocentric videos. (2) For training the cross-view retrieval module, we devise an automatic pipeline to discover ego-exo video pairs from distinct large-scale egocentric and exocentric datasets. (3) We train the cross-view retrieval module with a novel EgoExoNCE loss that pulls egocentric and exocentric video features closer by aligning them to shared text features that describe similar actions. (4) Through extensive experiments, our cross-view retrieval module demonstrates superior performance across seven benchmarks. Regarding egocentric video captioning, EgoInstructor exhibits significant improvements by leveraging third-person videos as references. Project page is available at: https://jazzcharles.github.io/Egoinstructor/, Comment: CVPR 2024. Project page is available at: https://jazzcharles.github.io/Egoinstructor/

The exponential growth of large language models (LLMs) has opened up numerous possibilities for multimodal AGI systems. However, the progress in vision and vision-language foundation models, which are also critical elements of multi-modal AGI, has not kept pace with LLMs. In this work, we design a large-scale vision-language foundation model (InternVL), which scales up the vision foundation model to 6 billion parameters and progressively aligns it with the LLM, using web-scale image-text data from various sources. This model can be broadly applied to and achieve state-of-the-art performance on 32 generic visual-linguistic benchmarks including visual perception tasks such as image-level or pixel-level recognition, vision-language tasks such as zero-shot image/video classification, zero-shot image/video-text retrieval, and link with LLMs to create multi-modal dialogue systems. It has powerful visual capabilities and can be a good alternative to the ViT-22B. We hope that our research could contribute to the development of multi-modal large models. Code and models are available at https://github.com/OpenGVLab/InternVL., Comment: 25 pages, 5 figures, 28 tables

The fundamental goal of the Text-to-SQL task is to translate natural language question into SQL query. Current research primarily emphasizes the information coupling between natural language questions and schemas, and significant progress has been made in this area. The natural language questions as the primary task requirements source determines the hardness of correspond SQL queries, the correlation between the two always be ignored. However, when the correlation between questions and queries was decoupled, it may simplify the task. In this paper, we introduce an innovative framework for Text-to-SQL based on decoupling SQL query hardness parsing. This framework decouples the Text-to-SQL task based on query hardness by analyzing questions and schemas, simplifying the multi-hardness task into a single-hardness challenge. This greatly reduces the parsing pressure on the language model. We evaluate our proposed framework and achieve a new state-of-the-art performance of fine-turning methods on Spider dev., Comment: 10 pages, 3 figures

We report an attempt to detect molecular and atomic species in the atmosphere of the ultra-hot Jupiter WASP-33b using the high-resolution echelle spectrograph NEID with a wavelength coverage of 380$-$930 nm. By analyzing the transmission spectrum of WASP-33b using the line-by-line technique and the cross-correlation technique, we confirm previous detection of H$\alpha$, H$\beta$, H$\gamma$, and Ca II infrared triplets. We find no evidence for a significant day-to-night wind in WASP-33b, taking into account the effects of stellar pulsations using a relatively novel GP method and poorly constrained systemic velocity measurements. We also detect the previously reported pre-transit absorption signal, which may be a pulsation mode induced by the planet. Combined with previous CARMENES and HARPS-N observations, we report the non-detection of TiO, Ti I, and V I in the transmission spectrum, while they were already detected in the dayside atmosphere of WASP-33b. This implies a difference in the chemical compositions and abundances between the dayside and terminator atmospheres of WASP-33b, and certainly requires further improvements in the sensitivity of the detection methods., Comment: 18 pages, 8 figures, accepted for publication in AJ

With the rapid development of Multi-modal Large Language Models (MLLMs), a number of diagnostic benchmarks have recently emerged to evaluate the comprehension capabilities of these models. However, most benchmarks predominantly assess spatial understanding in the static image tasks, while overlooking temporal understanding in the dynamic video tasks. To alleviate this issue, we introduce a comprehensive Multi-modal Video understanding Benchmark, namely MVBench, which covers 20 challenging video tasks that cannot be effectively solved with a single frame. Specifically, we first introduce a novel static-to-dynamic method to define these temporal-related tasks. By transforming various static tasks into dynamic ones, we enable the systematic generation of video tasks that require a broad spectrum of temporal skills, ranging from perception to cognition. Then, guided by the task definition, we automatically convert public video annotations into multiple-choice QA to evaluate each task. On one hand, such a distinct paradigm allows us to build MVBench efficiently, without much manual intervention. On the other hand, it guarantees evaluation fairness with ground-truth video annotations, avoiding the biased scoring of LLMs. Moreover, we further develop a robust video MLLM baseline, i.e., VideoChat2, by progressive multi-modal training with diverse instruction-tuning data. The extensive results on our MVBench reveal that, the existing MLLMs are far from satisfactory in temporal understanding, while our VideoChat2 largely surpasses these leading models by over 15% on MVBench. All models and data are available at https://github.com/OpenGVLab/Ask-Anything., Comment: CVPR 2024 highlight: updated version with Mistral and better performances for MVBench/NExT-QA/STAR/TVQA/EgoSchema/IntentQA

It is usually asserted that physical Hamiltonians for fermions must contain an even number of fermion operators. This is indeed true in electronic structure theory. However, when the Jordan-Wigner transformation is used to map physical spin Hamiltonians to Hamiltonians of spinless fermions, terms which contain an odd number of fermion operators may appear. The resulting fermionic Hamiltonian thus does not have number parity symmetry, and requires wave functions which do not have this symmetry either. In this work, we discuss the extension of standard Hartree-Fock-Bogoliubov (HFB) theory to the number-parity--nonconserving case. These ideas had appeared in the literature before, but, perhaps for lack of practical applications, had to the best of our knowledge never been employed. We here present a useful application for this more general HFB theory based on coherent states of the SO(2$M$ + 1) Lie group, where $M$ is the number of orbitals. We also show how using these unusual mean-field states can provide significant improvements when studying the Jordan-Wigner transformation of chemically relevant spin Hamiltonians., Comment: Resubmitted to J. Chem. Phys

Remote Direct Memory Access (RDMA) has been haunted by the need of pinning down memory regions. Pinning limits the memory utilization because it impedes on-demand paging and swapping. It also increases the initialization latency of large memory applications from seconds to minutes. To remove memory pining, existing approaches often require special hardware which supports page fault, and still have inferior performance. We propose NP-RDMA, which removes memory pinning during memory registration and enables dynamic page fault handling with commodity RDMA NICs. NP-RDMA does not require NICs to support page fault. Instead, by monitoring local memory paging and swapping with MMU-notifier, combining with IOMMU/SMMU-based address mapping, NP-RDMA efficiently detects and handles page fault in the software with near-zero additional latency to non-page-fault RDMA verbs. We implement an LD_PRELOAD library (with a modified kernel module), which is fully compatible with existing RDMA applications. Experiments show that NP-RDMA adds only 0.1{\sim}2 {\mu}s latency under non-page-fault scenarios. Moreover, NP-RDMA adds only 3.5{\sim}5.7 {\mu}s and 60 {\mu}s under minor or major page faults, respectively, which is 500x faster than ODP which uses advanced NICs that support page fault. With non-pinned memory, Spark initialization is 20x faster and the physical memory usage reduces by 86% with only 5.4% slowdown. Enterprise storage can expand to 5x capacity with SSDs while the average latency is only 10% higher. To the best of our knowledge, NP-RDMA is the first efficient and application-transparent software approach to remove memory pinning using commodity RDMA NICs.

HISPEC is a new, high-resolution near-infrared spectrograph being designed for the W.M. Keck II telescope. By offering single-shot, R=100,000 between 0.98 - 2.5 um, HISPEC will enable spectroscopy of transiting and non-transiting exoplanets in close orbits, direct high-contrast detection and spectroscopy of spatially separated substellar companions, and exoplanet dynamical mass and orbit measurements using precision radial velocity monitoring calibrated with a suite of state-of-the-art absolute and relative wavelength references. MODHIS is the counterpart to HISPEC for the Thirty Meter Telescope and is being developed in parallel with similar scientific goals. In this proceeding, we provide a brief overview of the current design of both instruments, and the requirements for the two spectrographs as guided by the scientific goals for each. We then outline the current science case for HISPEC and MODHIS, with focuses on the science enabled for exoplanet discovery and characterization. We also provide updated sensitivity curves for both instruments, in terms of both signal-to-noise ratio and predicted radial velocity precision., Comment: 25 pages, 9 figures. To appear in the Proceedings of SPIE: Techniques and Instrumentation for Detection of Exoplanets XI, vol. 12680 (2023)