Camera calibration is very important for some vision tasks, like rendering 3D scenes, environment reconstruction, and self-localization, etc. In this paper, we propose a framework of multi-scale transformers for camera calibration. With the input of a single image, the multi-scale features output from the model’s backbone are utilized to estimate camera parameters. At the same time, we show that the way of coarse-to-fine is effective to locate global structures and detailed features in the image, by studying the attention response of horizon line estimation. Moreover, deep supervision is proven to get more precise results and accelerated training. Our method outperforms all the state-of-the-art methods by objective and subjective experiments on Google Street View dataset and Pano360.

Compared to gait recognition, Gait Attribute Recognition (GAR) is a seldom-investigated problem. However, since gait attribute recognition can provide richer and finer semantic descriptions, it is an indispensable part of building intelligent gait analysis systems. Nonetheless, the types of attributes considered in the existing datasets are very limited. This paper contributes a new benchmark dataset for gait attribute recognition named Multi-Attribute Gait (MA-Gait). Our MA-Gait contains 95 subjects recorded from 12 camera views, resulting in more than 13000 sequences, with 16 attributes labeled, including six attributes that have never been considered in the literature. Moreover, we propose a Multi-Scale Motion Encoder (MSME) to extract robust motion features, and an Attribute-Guided Feature Selection Module (AGFSM) to adaptively capture the most discriminative attribute features from static appearance features and dynamic motion features for different attributes. Our method achieves the best GAR accuracy on the new dataset. Comprehensive experiments show the effectiveness of the proposed method through both quantitative and qualitative evaluations.

This paper presents a learning based planner for computing optimized 3D printing toolpaths on prescribed graphs, the challenges of which include the varying graph structures on different models and the large scale of nodes & edges on a graph. We adopt an on-the-fly strategy to tackle these challenges, formulating the planner as a Deep Q-Network (DQN) based optimizer to decide the next 'best' node to visit. We construct the state spaces by the Local Search Graph (LSG) centered at different nodes on a graph, which is encoded by a carefully designed algorithm so that LSGs in similar configurations can be identified to re-use the earlier learned DQN priors for accelerating the computation of toolpath planning. Our method can cover different 3D printing applications by defining their corresponding reward functions. Toolpath planning problems in wire-frame printing, continuous fiber printing, and metallic printing are selected to demonstrate its generality. The performance of our planner has been verified by testing the resultant toolpaths in physical experiments. By using our planner, wire-frame models with up to 4.2k struts can be successfully printed, up to 93.3% of sharp turns on continuous fiber toolpaths can be avoided, and the thermal distortion in metallic printing can be reduced by 24.9%.

In medical situations, head-mounted displays have gained significant attention for providing physicians with immediate access to patient information. Traditional vital signs monitoring needs physical contact to patient and separates display to physicians. However, it is difficult to integrate the advanced computer-human interaction with contactless vital signs monitoring device. This paper discusses the design and implementation of HoloCV, a scalable mixed reality-based system that supports contactless vital signs monitoring and presents real-time pre-diagnostic results of patient to the visual field of physicians. HoloCV is supported by a distributed architecture divided into three interrelated applications responsible for advanced computer-human interaction, radar and thermal infrared device integration, computation and communication infrastructure, respectively. To monitor vital signs contactlessly and continuously, the HoloCV utilizes Impulse-Radio Ultra-WideBand(IR-UWB) radar and thermal infrared camera to achieve monitoring respiration rate, heart rate, blood pressure, and temperature. By running the YOLOv7-Tiny model directly on Microsoft Hololens2 head-mounted displays, HoloCV can achieve real-time object detection and present real-time visualizations of vital signs data on the detected targets. The overall weight of the system is less than or equal to 5 kilograms. It has a detection range of 0.5m to 3m, and can continuously operate for more than 1 hour with a standby time of 5 hours. The working environment temperature ranges from 0°C to 46°C. The heart rate measurement range is from 50 to 150 BPM, the respiratory rate measurement range is from 10 to 50 BPM. The temperature measurement range is from 32 degrees Celsius to 42 degrees Celsius, and the blood pressure measurement range is from 50 to 180 mmHg. With its capability for real-time and non-contact monitoring of vital signs, this HoloCV system differentiates itself from traditional contact-based devices, providing physicians with a convenient means to assess patients' conditions and administer necessary treatments without physical contact. The codes of the proposed HoloCV are made publicly available.

Static analysis tools, although widely adopted in industry, suffer from a high false positive rate. This paper aims to refine the results of static analysis tools, by automatically searching for a vulnerable path from given defect report. To realize this goal, we develop SATRACER, a novel tool which integrates symbolic execution techniques with static analysis. SATRACER selectively skips those program parts which can be consistently updated by static analysis, thus drastically improving performance. We have applied SATRACER to a set of 21 real-world applications. Evaluation results show that SATRACER can successfully remove 71.4% false alarms reported by a commercial static analysis tool in 10 hours, and confirmed 29 real use-after-free bugs and 895 real null-pointer-dereference bugs.

The sharding-based protocols provide an effective solution on scaling decentralized blockchains. However, the performance of a sharding-based blockchain protocol can be greatly degraded by the existence of cross-shard transactions. This paper proposes an overlap-based sharding mechanism for sharding protocols, OverlapShard, to weaken the adverse impact of cross-shard transactions by mapping each node into multiple actual shards. The virtual shards composed of overlapping nodes can be used to process cross-shard transactions. The results of empirical analysis show that when a transaction contains 2 inputs and 2 outputs, the consensus overheads of OverlapShard account for 25% of that of the nonoverlapping mechanism, and OverlapShard's communication overheads are reduced by about 33.3 %. The evaluation results show that the average communication overhead of OverlapShard is reduced by 63.30%. And the average cross-shard transaction ratio of overlap-based sharding model with 3-layer virtual shard structure is 43.76% lower than that of the non-overlapping sharding model.

The three kinds of gases such as NO2, NO, and SO2produced by industrial combustion process are the main sources of atmospheric pollution. Almost all the available technologies of measuring three components in mixed gases are limited by detection range, operating temperature, and integration, etc. Here we report an array with three triple-electrode carbon nanotube sensors and its sensitivity to the three flue gas components. The three sensors operate at the same gas ionization mechanism and are comprised of a common long cathode, three extracting electrodes and three collecting electrodes, and are set by different electrode separations with$75\ \boldsymbol{\mu} \mathbf{m}, 100\ \boldsymbol{\mu} \mathbf{m}$, and$120\ \boldsymbol{\mu} \mathbf{m}$, respectively. We explored and obtained distinct single-valued sensitive characteristics to the three-component gases at given voltages applied on electrodes, which should be higher than a critical voltage to obtain single-valued sensitivities of the three sensors. The standard deviation of repeatability experiment is less than 0.133 nA, which indicates that the sensor has a good repeatability. The array displays a high level of integration and small size, and also has the potential to detect the concentrations of different components of gas mixtures.

BACKGROUND: One of the most promising aspects of metabolomics is metabolic modeling and simulation. Central to such applications is automated high-throughput identification and quantification of metabolites. NMR spectroscopy is a reproducible, nondestructive, and nonselective method that has served as the foundation of metabolomics studies. However, the automated high-throughput identification and quantification of metabolites in NMR spectroscopy is limited by severe spectral overlap. Although numerous software programs have been developed for resolving overlapping resonances, as well as for identifying and quantifying metabolites, most of these programs are frequency-domain methods, considerably influenced by phase shifts and baseline distortions, and effective only in small-scale studies. Almost all these programs require multiple spectra for each application, and do not automatically identify and quantify metabolites in batches. RESULTS: We created IQMNMR, an R package that integrates a relaxation algorithm, digital filter, and similarity search algorithm. It differs from existing software in that it is a time-domain method; it uses not only frequency to resolve overlapping resonances but also relaxation time constants; it requires only one NMR spectrum per application; is uninfluenced by phase shifts and baseline distortions; and most important, yields a batch of quantified metabolites. CONCLUSIONS: IQMNMR provides a solution that can automatically identify and quantify metabolites by one-dimensional proton NMR spectroscopy. Its time-domain nature, stability against phase shifts and baseline distortions, requirement for only one NMR spectrum, and capability to output a batch of quantified metabolites are of considerable significance to metabolic modeling and simulation.IQMNMR is available at http://cran.r-project.org/web/packages/IQMNMR/.