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We present an independent validation and comprehensive re-calibration of S-PLUS Ultra-Short Survey (USS) DR1 12-band photometry using about 30,000--70,000 standard stars from the BEst STar (BEST) database. We identify spatial variation of zero-point offsets, up to 30--40\,mmag for blue filters ($u$, $J0378$, $J0395$) and 10\,mmag for others, predominantly due to the higher uncertainties of the technique employed in the original USS calibration. Moreover, we detect large- and medium-scale CCD position-dependent systematic errors, up to 50\,mmag, primarily caused by different aperture and flat-field corrections. We then re-calibrate the USS DR1 photometry by correcting the systematic shifts for each tile using second-order two-dimensional polynomial fitting combined with a numerical stellar flat-field correction method. The re-calibrated results from the XPSP and the SCR standards are consistent within 6\,mmag in the USS zero-points, demonstrating both the typical precision of re-calibrated USS photometry and a sixfold improvement in USS zero-point precision. Further validation using SDSS and Pan-STARRS1, as well as LAMOST DR10 and Gaia photometry, also confirms this precision for the re-calibrated USS photometry. Our results clearly demonstrate the capability and the efficiency of the BEST database in improving calibration precision to the milli-magnitude level for wide-field photometric surveys. The re-calibrated USS DR1 photometry is publicly available: doi: 10.12149/101503 (https://nadc.china-vo.org/res/r101504/)., Comment: 28 pages, 25 figures, ApJS accepted, see main results in Figures 4 and 7

We apply the stellar locus method to synthetic $(BP-RP)_{XPSP}$ and $(BP-G)_{XPSP}$ colors derived from corrected Gaia BP/RP (XP) spectra to obtain precise estimates of metallicity for about 100 million stars in the Milky Way (34 million giants in the color range $0.6 < (BP-RP)_0 < 1.75$ and 65 million dwarfs in the color range $0.2 < (BP-RP)_0 < 1.5$). The sub milli-magnitude precision of the derived synthetic stellar colors enables estimates of metallicity for stars as low as [Fe/H] $\sim -4$. Multiple validation tests indicate that the typical metallicity precision is between 0.05 -- 0.1 dex for both dwarfs and giants at [Fe/H] = 0 as faint as G $\sim$ 16, and decreases to 0.15 -- 0.25 dex at [Fe/H] = $-$2.0. For $-4.0 < $[Fe/H]$< -3.0$, the typical metallicity precision decreases to on the order of 0.4 -- 0.5 dex, based on the results from the reference sample. Our achieved precision is comparable to or better than previous efforts using the entire XP spectra, and about three times better than our previous work based on Gaia EDR3 colors. This opens up new opportunities for investigations of stellar populations, the formation and chemical evolution of the Milky Way, the chemistry of stars and star clusters, and the identification of candidate stars for subsequent high-resolution spectroscopic follow-up. The catalog is publicly available at https://doi.org/10.12149/101548., Comment: 25 pages, 21 figures, ApJS in Press

Gaia DR3 photometry and BP/RP spectra have been widely used as reference in photometric calibrations. In this work, we check the spatial uniformity of Gaia DR3 photometry and BP/RP spectra by comparing the BP, RP and G bands photometry with the synthetic ones from the BP/RP spectra. The discrepancies have a small dispersion of 1.07, 0.55 and 1.02 mmag for the BP, RP, and G bands, respectively. However, the discrepancies exhibit obvious spatial patterns, which are clearly associated with the Gaia's scanning law. The patterns observed in the BP and G bands are similar, with discrepancies between photometry and spectra being more pronounced in these bands compared to the RP band. A further independent test with the Dark Energy Survey DR2 photometry reveals that the spatial patterns are more likely attributed to the Gaia DR3 BP/RP spectra, particularly in the BP band. On one hand, our results confirm the high spatial uniformity of Gaia data at the mmag level. On the other hand, our results suggest that the spatial uniformity of Gaia DR3 BP/RP spectra is not as good as that of Gaia DR3 photometry, and could be further improved in the future., Comment: 7pages, 2 figures, ApJ accepted

We present a direct measurement of extinction curves using corrected $Gaia$ XP spectra of the common sources in $Gaia$ DR3 and LAMOST DR7. Our analysis of approximately 370 thousand high-quality samples yielded a high-precision average extinction curve for the Milky Way. After incorporating infrared photometric data from 2MASS and WISE, the extinction curve spans wavelengths from 0.336 to 4.6 $\mu$m. We determine an average $R_{55}$ of $2.730 \pm 0.007$, corresponding to $R_V= 3.073 \pm 0.009$, and a near-infrared power-law index $\alpha$ of $1.935 \pm 0.037$. Our study confirmed some intermediate-scale structures within the optical range. Two new features were identified at 540 and 769 nm, and their intensities exhibited a correlation with extinction and $R_V$. This extinction curve can be used to investigate the characteristics of dust and enhance the extinction correction of Milky Way stars. A Python package for this extinction curve is available., Comment: 11 pages, 5 figures, accepted for publication in ApJ

The task that requires an agent to navigate to a given object through only visual observation is called visual object navigation (VON). The main bottlenecks of VON are strategies exploration and prior knowledge exploitation. Traditional strategies exploration ignores the differences of searching and navigating stages, using the same reward in two stages, which reduces navigation performance and training efficiency. Our study enables the agent to explore larger area in searching stage and seek the optimal path in navigating stage, improving the success rate of navigation. Traditional prior knowledge exploitation focused on learning and utilizing object association, which ignored the depth and obstacle information in the environment. This paper uses the RGB and depth information of the training scene to pretrain the feature extractor, which improves navigation efficiency. The obstacle information is memorized by the agent during the navigation, reducing the probability of collision and deadlock. Depth, obstacle and other prior knowledge are concatenated and input into the policy network, and navigation actions are output under the training of two-stage rewards. We evaluated our method on AI2-Thor and RoboTHOR and demonstrated that it significantly outperforms state-of-the-art (SOTA) methods on success rate and navigation efficiency.

Offshore wind farms have emerged as a popular renewable energy source that can generate substantial electric power with a low environmental impact. However, integrating these farms into the grid poses significant complexities. To address these issues, optimal-sized energy storage can provide potential solutions and help improve the reliability, efficiency, and flexibility of the grid. Nevertheless, limited studies have attempted to perform energy storage sizing while including design and operations (i.e., control co-design) for offshore wind farms. As a result, the present work develops a control co-design optimization formulation to optimize multiple objectives and identify Pareto optimal solutions. The graph-based optimization framework is proposed to address the complexity of the system, allowing the optimization problem to be decomposed for large power systems. The IEEE-9 bus system is treated as an onshore AC grid with two offshore wind farms connected via a multi-terminal DC grid for our use case. The developed methodology successfully identifies the Pareto front during the control co-design optimization, enabling decision-makers to select the best compromise solution for multiple objectives.

We search for an optimal filter design for the estimation of stellar metallicity, based on synthetic photometry from Gaia XP spectra convolved with a series of filter-transmission curves defined by different central wavelengths and bandwidths. Unlike previous designs based solely on maximizing metallicity sensitivity, we find that the optimal solution provides a balance between the sensitivity and uncertainty of the spectra. With this optimal filter design, the best precision of metallicity estimates for relatively bright ($G \sim 11.5$) stars is excellent, $\sigma_{\rm [Fe/H]} = 0.034$\,dex for FGK dwarf stars, superior to that obtained utilizing custom sensitivity-optimized filters (e.g., SkyMapper\,$v$). By selecting hundreds of high-probabability member stars of the open cluster M67, our analysis reveals that the intrinsic photometric-metallicity scatter of these cluster members is only 0.036\,dex, consistent with this level of precision. Our results clearly demonstrate that the internal precision of photometric-metallicity estimates can be extremely high, even providing the opportunity to perform chemical tagging for very large numbers of field stars in the Milky Way. This experiment shows that it is crucial to take into account uncertainty alongside the sensitivity when designing filters for measuring the stellar metallicity and other parameters., Comment: 9 pages, 5 figures; ApJL accepted, see main result in Figures 5

Applying large language models (LLMs) for academic API usage shows promise in reducing researchers' academic information seeking efforts. However, current LLM API-using methods struggle with complex API coupling commonly encountered in academic queries. To address this, we introduce SoAy, a solution-based LLM API-using methodology for academic information seeking. It uses code with a solution as the reasoning method, where a solution is a pre-constructed API calling sequence. The addition of the solution reduces the difficulty for the model to understand the complex relationships between APIs. Code improves the efficiency of reasoning. To evaluate SoAy, we introduce SoAyBench, an evaluation benchmark accompanied by SoAyEval, built upon a cloned environment of APIs from AMiner. Experimental results demonstrate a 34.58-75.99\% performance improvement compared to state-of-the-art LLM API-based baselines. All datasets, codes, tuned models, and deployed online services are publicly accessible at https://github.com/RUCKBReasoning/SoAy., Comment: 22 pages, 13 figures

In this paper, we present the mathematical formulation of an OpenStreetMaps (OSM) based tool that compares the costs and emissions of long-haul medium and heavy-duty (M&HD) electric and diesel freight trucks, and determines the spatial distribution of added energy demand due to M&HD EVs. The optimization utilizes a combination of information on routes from OSM, utility rate design data across the United States, and freight volume data, to determine these values. In order to deal with the computational complexity of this problem, we formulate the problem as a convex optimization problem that is scalable to a large geographic area. In our analysis, we further evaluate various scenarios of utility rate design (energy charges) and EV penetration rate across different geographic regions and their impact on the operating cost and emissions of the freight trucks. Our approach determines the net emissions reduction benefits of freight electrification by considering the primary energy source in different regions. Such analysis will provide insights to policy makers in designing utility rates for electric vehicle supply equipment (EVSE) operators depending upon the specific geographic region and to electric utilities in deciding infrastructure upgrades based on the spatial distribution of the added energy demand of M&HD EVs. To showcase the results, a case study for the U.S. state of Texas is conducted.

Vision-and-Language Navigation (VLN) is a challenging task where an agent is required to navigate to a natural language described location via vision observations. The navigation abilities of the agent can be enhanced by the relations between objects, which are usually learned using internal objects or external datasets. The relationships between internal objects are modeled employing graph convolutional network (GCN) in traditional studies. However, GCN tends to be shallow, limiting its modeling ability. To address this issue, we utilize a cross attention mechanism to learn the connections between objects over a trajectory, which takes temporal continuity into account, termed as Temporal Object Relations (TOR). The external datasets have a gap with the navigation environment, leading to inaccurate modeling of relations. To avoid this problem, we construct object connections based on observations from all viewpoints in the navigational environment, which ensures complete spatial coverage and eliminates the gap, called Spatial Object Relations (SOR). Additionally, we observe that agents may repeatedly visit the same location during navigation, significantly hindering their performance. For resolving this matter, we introduce the Turning Back Penalty (TBP) loss function, which penalizes the agent's repetitive visiting behavior, substantially reducing the navigational distance. Experimental results on the REVERIE, SOON, and R2R datasets demonstrate the effectiveness of the proposed method.

The upcoming Chinese Space Station Telescope (CSST) slitless spectroscopic survey poses a challenge of flux calibration, which requires a large number of flux-standard stars. In this work, we design an uncertainty-aware residual attention network, the UaRA-net, to derive the CSST SEDs with a resolution of R = 200 over the wavelength range of 2500-10000 \AA using LAMOST normalized spectra with a resolution of R = 2000 over the wavelength range of 4000-7000 \AA. With the special structure and training strategy, the proposed model can not only provide accurate predictions of SEDs but also their corresponding errors. The precision of the predicted SEDs depends on effective temperature (Teff), wavelength, and the LAMOST spectral signal-to-noise ratios (SNRs), particularly in the GU band. For stars with Teff = 6000 K, the typical SED precisions in the GU band are 4.2%, 2.1%, and 1.5% at SNR values of 20, 40, and 80, respectively. As Teff increases to 8000 K, the precision increases to 1.2%, 0.6%, and 0.5%, respectively. The precision is higher at redder wavelengths. In the GI band, the typical SED precisions for stars with Teff = 6000 K increase to 0.3%, 0.1%, and 0.1% at SNR values of 20, 40, and 80, respectively. We further verify our model using the empirical spectra of the MILES and find good performance. The proposed method will open up new possibilities for optimal utilization of slitless spectra of the CSST and other surveys., Comment: 20 pages, 15 figures, accepted by ApJS

By combining spectra from the CALSPEC and NGSL, as well as spectroscopic data from the LAMOST Data Release 7 (DR7), we have analyzed and corrected the systematic errors of the Gaia DR3 BP/RP (XP) spectra. The errors depend on the normalized spectral energy distribution (simplified by two independent ``colors'') and $G$ magnitude. Our corrections are applicable in the range of approximately $-0.5

Energy storage and electrification of buildings hold great potential for future decarbonized energy systems. However, there are several technical and economic barriers that prevent large-scale adoption and integration of energy storage in buildings. These barriers include integration with building control systems, high capital costs, and the necessity to identify and quantify value streams for different stakeholders. To overcome these obstacles, it is crucial to develop advanced sizing and dispatch methods to assist planning and operational decision-making for integrating energy storage in buildings. This work develops a simple and flexible optimal sizing and dispatch framework for thermal energy storage (TES) and battery energy storage (BES) systems in large-scale office buildings. The optimal sizes of TES, BES, as well as other building assets are determined in a joint manner instead of sequentially to avoid sub-optimal solutions. The solution is determined considering both capital costs in optimal sizing and operational benefits in optimal dispatch. With the optimally sized systems, we implemented real-time operation using the model-based control (MPC), facilitating the effective and efficient management of energy resources. Comprehensive assessments are performed using simulation studies to quantify potential energy and economic benefits by different utility tariffs and climate locations, to improve our understanding of the techno-economic performance of different TES and BES systems, and to identify barriers to adopting energy storage for buildings. Finally, the proposed framework will provide guidance to a broad range of stakeholders to properly design energy storage in buildings and maximize potential benefits, thereby advancing affordable building energy storage deployment and helping accelerate the transition towards a cleaner and more equitable energy economy.

We employ the corrected Gaia Early Data Release 3 (EDR3) photometric data and spectroscopic data from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) DR7 to assemble a sample of approximately 0.25 million FGK dwarf photometric standard stars for the 12 J-PLUS filters using the Stellar Color Regression (SCR) method. We then independently validated the J-PLUS DR3 photometry, and uncovered significant systematic errors: up to 15 mmag in the results of Stellar Locus (SL) method, and up to 10 mmag mainly caused by magnitude-, color-, and extinction-dependent errors of the Gaia XP spectra with the Gaia BP/RP (XP) Synthetic Photometry (XPSP) method. We have also further developed the XPSP method using the corrected Gaia XP spectra by Huang et al. (2023) and applied it to the J-PLUS DR3 photometry. This resulted in an agreement of 1-5 mmag with the SCR method, and a two-fold improvement in the J-PLUS zero-point precision. Finally, the zero-point calibration for around 91% of the tiles within the LAMOST observation footprint is determined through the SCR method, with the remaining approximately 9% of tiles outside this footprint relying on the improved XPSP method. The re-calibrated J-PLUS DR3 photometric data establishes a solid data foundation for conducting research that depends on high-precision photometric calibration., Comment: ApJS accepted; 21 papes; 20 figures, see main results in Figures 5 and 12

We present a comprehensive re-calibration of medium- and broad-band photometry from the Southern Photometric Local Universe Survey (S-PLUS) by leveraging two approaches: an improved Gaia XP Synthetic Photometry (XPSP) method with corrected Gaia XP spectra, the Stellar Color Regression (SCR) method with corrected Gaia EDR3 photometric data and spectroscopic data from LAMOST DR7. Through the use of millions of stars as standards per band, we demonstrate the existence of position-dependent systematic errors, up to 23 mmag for the Main Survey region, in the S-PLUS DR4 photometric data. A comparison between the XPSP and SCR methods reveals minor differences in zero-point offsets, typically within the range of 1 to 6 mmag, indicating the accuracy of the re-calibration, and a two- to three-fold improvement in the zero-point precision. During this process, we also verified and corrected for the systematic errors related to CCD position. The corrected S-PLUS DR4 photometric data will provide a solid data foundation for conducting scientific research that relies on high-calibration precision. Our results underscore the power of the XPSP method in combination with the SCR method, showcasing their effectiveness in enhancing calibration precision for wide-field surveys when combined with Gaia photometry and XP spectra, to be applied for other S-PLUS sub-surveys., Comment: 15 pages, 15 figures, submitted, see main results in Figures 4 and 8

In this work, we perform the re-calibration of PS1 photometry by correcting for position-dependent systematic errors using the spectroscopy-based Stellar Color Regression method (SCR), the photometry-based SCR method (SCR$'$), and the Gaia XP synthetic photometry method (XPSP). We confirm the significant large-scale and small-scale spatial variation of magnitude offsets for all the $grizy$ filters. We show that the PS1 photometric calibration precisions in the $grizy$ filters are around 5--7\,mmag when averaged over 14$'$ regions. We note a much larger calibration error up to 0.04 mag in the Galactic plane, which is probably caused by the systematic errors of the PS1 magnitudes in crowded fields. The results of the three methods are consistent with each other within 1--2\,mmag or better for all the filters. We provide two-dimensional maps and a python package ({\url{https://doi.org/10.12149/101283}}) to correct for position-dependent magnitude offsets of PS1, which can be used for high-precision investigations and as a reference to calibrate other surveys., Comment: 14 pages, 18 figures, ApJS accepted, see main results in Figures 11 and 12

Gaia DR3 mission has identified and provided about 440,000 binary systems with orbital solutions, offering a valuable resource for searching binaries including a compact component. By combining the Gaia DR3 data with radial velocities (RVs) from the LAMOST spectroscopic survey, we identify three wide binaries possibly containing a compact object. For two of these sources with a main-sequence companion, no obvious excess is observed in the blue/red band of the Gaia DR3 XP spectra, and the LAMOST medium-resolution spectra exhibit clear single-lined features. The absence of an additional component from spectral disentangling analysis further suggests the presence of compact objects within these systems. On the other hand, the visible star of the third source is a stripped giant star. In contrast to most binaries including stripped stars, no emission line is detected in the optical spectra. The unseen star could potentially be a massive white dwarf or neutron star, but the possibility of an F-type dwarf star scenario cannot be ruled out. An examination of about ten binaries containing white dwarfs or neutron stars using both kinematic and chemical methods suggest most of these systems are located in the thin disk of the Milky Way., Comment: 20 pages, 13 figures, accepted by APJ

In this paper, a total of approximately 2.6 million dwarfs were constructed as standard stars, with an accuracy of about 0.01-0.02 mag for each band, by combining spectroscopic data from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope Data Release 7, photometric data from the corrected Gaia Early Data Release 3, and photometric metallicities. Using the spectroscopy based stellar color regression method (SCR method) and the photometric-based SCR method (SCR' method), we performed the relative calibration of the Nanshan One-meter Wide-field Telescope imaging data. Based on the corrected Pan-STARRS DR1 photometry, the absolute calibration was also performed. In the photometric calibration process, we analyzed the dependence of the calibration zero points on different images (observation time), different gates of the CCD detector, and different CCD positions. We found that the stellar flat and the relative gain between different gates depend on time. The amplitude of gain variation in three channels is approximately 0.5%-0.7% relative to the other channel, with a maximum value of 4%. In addition, significant spatial variations of the stellar flat fitting residual are found and corrected. Using repeated sources in the adjacent images, we checked and discovered internal consistency of about 1-2 mmag in all the filters. Using the PS1 magnitudes synthesized by Gaia DR3 BP/RP spectra by the synthetic photometry method, we found that the photometric calibration uniformity is about 1-2 mmag for all the bands, at a spatial resolution of 1.3 degree. A detailed comparison between the spectroscopy-based SCR and photometric-based SCR method magnitude offsets was performed, and we achieved an internal consistency precision of about 2 mmag or better with resolutions of 1.3 degree for all the filters. Which is mainly from the position-dependent errors of the E(B-V) used in SCR' method., Comment: 15 pages in Chinese language, 8 figures, Chinese Science Bulletin accepted and published online (https://www.sciengine.com/CSB/doi/10.1360/TB-2023-0052), see main results in Figures 6, 7 and 8

This paper proposes a policy learning algorithm based on the Koopman operator theory and policy gradient approach, which seeks to approximate an unknown dynamical system and search for optimal policy simultaneously, using the observations gathered through interaction with the environment. The proposed algorithm has two innovations: first, it introduces the so-called deep Koopman representation into the policy gradient to achieve a linear approximation of the unknown dynamical system, all with the purpose of improving data efficiency; second, the accumulated errors for long-term tasks induced by approximating system dynamics are avoided by applying Bellman's principle of optimality. Furthermore, a theoretical analysis is provided to prove the asymptotic convergence of the proposed algorithm and characterize the corresponding sampling complexity. These conclusions are also supported by simulations on several challenging benchmark environments.

This paper presents a data-driven approach to approximate the dynamics of a nonlinear time-varying system (NTVS) by a linear time-varying system (LTVS), which is resulted from the Koopman operator and deep neural networks. Analysis of the approximation error between states of the NTVS and the resulting LTVS is presented. Simulations on a representative NTVS show that the proposed method achieves small approximation errors, even when the system changes rapidly. Furthermore, simulations in an example of quadcopters demonstrate the computational efficiency of the proposed approach.

Uniform and accurate photometric calibration plays an important role in the current and next-generation wide-field imaging surveys. Herein, we review the modern photometric calibration methods, including the classic standard star method, "hardware/observation-driven" methods (such as the Ubercalibration, Hypercalibration, and Forward Global Calibration Methods), and "software/physics-driven" methods (e.g., the Stellar Locus Regression, Stellar Locus, and Stellar Color Regression Methods). Further, we discuss their advantages, limitations, and future developments toward millimagnitude precision calibration., Comment: Scientia Sinica Physica, Mechanica & Astronomica accepted, in Chinese language

By combining spectroscopic data from the LAMOST DR7, SDSS DR12, and corrected photometric data from the Gaia EDR3, we apply the Stellar Color Regression (SCR; Yuan et al. 2015a) method to recalibrate the SDSS Stripe 82 standard stars catalog of Ivezi\'c et al. (2007). With a total number of about 30,000 spectroscopically targeted stars, we have mapped out the relatively large and strongly correlated photometric zero-point errors present in the catalog, $\sim$2.5 per cent in the $u$ band and $\sim$ 1 per cent in the $griz$ bands. Our study also confirms some small but significant magnitude dependence errors in the $z$ band for some charge-coupled devices. Various tests show that we have achieved an internal precision of about 5 mmag in the $u$ band and about 2 mmag in the $griz$ bands, which is about 5 times better than previous results. We also apply the method to the latest version of the catalog (V4.2; Thanjavur et al. 2021), and find modest systematic calibration errors up to $\sim$ 1 per cent along the R.A. direction and smaller errors along the Dec. direction. The results demonstrate the power of the SCR method when combining spectroscopic data and Gaia photometry in breaking the 1 percent precision barrier of ground-based photometric surveys. Our work paves the way for the re-calibration of the whole SDSS photometric survey and has important implications for the calibration of future surveys. Future implementations and improvements of the SCR method under different situations are also discussed., Comment: 21 pages, 20 figures, 9 tables, ApJS in press

Most deep network methods for compressive sensing reconstruction suffer from the black-box characteristic of DNN. In this paper, a deep neural network with interpretable motion estimation named CSMCNet is proposed. The network is able to realize high-quality reconstruction of video compressive sensing by unfolding the iterative steps of optimization based algorithms. A DNN based, multi-hypothesis motion estimation module is designed to improve the reconstruction quality, and a residual module is employed to further narrow down the gap between re-construction results and original signal in our proposed method. Besides, we propose an interpolation module with corresponding training strategy to realize scalable CS reconstruction, which is capable of using the same model to decode various compression ratios. Experiments show that a PSNR of 29.34dB can be achieved at 2% CS ratio (compressed by 98%), which is superior than other state-of-the-art methods. Moreover, the interpolation module is proved to be effective, with significant cost saving and acceptable performance losses., Comment: 12 pages, 10 pages, 5 tables

This paper evaluates new security threats due to the processor frontend in modern Intel processors. The root causes of the security threats are the multiple paths in the processor frontend that the micro-operations can take: through the Micro-Instruction Translation Engine (MITE), through the Decode Stream Buffer (DSB), also called the Micro-operation Cache, or through the Loop Stream Detector (LSD). Each path has its own unique timing and power signatures, which lead to the side- and covert-channel attacks presented in this work. Especially, the switching between the different paths leads to observable timing or power differences which, as this work demonstrates, could be exploited by attackers. Because of the different paths, the switching, and way the components are shared in the frontend between hardware threads, two separate threads are able to be mutually influenced and timing or power can reveal activity on the other thread. The security threats are not limited to multi-threading, and this work further demonstrates new ways for leaking execution information about SGX enclaves or a new in-domain Spectre variant in single-thread setting. Finally, this work demonstrates a new method for fingerprinting the microcode patches of the processor by analyzing the behavior of different paths in the frontend. The findings of this work highlight the security threats associated with the processor frontend and the need for deployment of defenses for the modern processor frontend., Comment: 13 pages

Although it has been surpassed by many subsequent coding standards, JPEG occupies a large share of the storage load of the current data hosting service. To reduce the storage costs, DropBox proposed a lossless secondary compression algorithm, Lepton, to further improve the compression rate of JPEG images. However, the bloated probability models defined by Lepton severely restrict its throughput and energy efficiency. To solve this problem, we construct an efficient access probability-based hash function for the probability models, and then propose a hardware-friendly memory optimization method by combining the proposed hash function and the N-way Set-Associative unit. After that, we design a highly parameterized hardware structure for the probability models and finally implement a power and area efficient Lepton hardware encoder. To the best of our knowledge, this is the first hardware implementation of Lepton. The synthesis result shows that the proposed hardware structure reduces the total area of the probability models by 70.97%. Compared with DropBox's software solution, the throughput and the energy efficiency of the proposed Lepton hardware encoder are increased by 55.25 and 4899 times respectively. In terms of manufacturing cost, the proposed Lepton hardware encoder is also significantly lower than the general-purpose CPU used by DropBox.

In this paper, a deep neural network with interpretable motion compensation called CS-MCNet is proposed to realize high-quality and real-time decoding of video compressive sensing. Firstly, explicit multi-hypothesis motion compensation is applied in our network to extract correlation information of adjacent frames(as shown in Fig. 1), which improves the recover performance. And then, a residual module further narrows down the gap between reconstruction result and original signal. The overall architecture is interpretable by using algorithm unrolling, which brings the benefits of being able to transfer prior knowledge about the conventional algorithms. As a result, a PSNR of 22dB can be achieved at 64x compression ratio, which is about 4% to 9% better than state-of-the-art methods. In addition, due to the feed-forward architecture, the reconstruction can be processed by our network in real time and up to three orders of magnitude faster than traditional iterative methods., Comment: 15pages, ACCV2020 accepted paper