A multidimensional model for green building assessment: a case study of a highest-rated project in Chongqing

Green building is an inevitable trend in the construction industry which deeply affects the social development of the economy, environment and a series of industries. There is practical significance for the multidimensionally balanced development of green buildings. A model for multi-objective assessment of green building is devel-oped under three dimensions: Objective, Professional and Time (OPT) according to the green building definition. The OPT coordinate system was built up based on the scoring centroid system of both the China Green Building Labelling scheme (GBL) and the Singapore Green Mark (GM) by the introduction of the Coefficient of Varia-tion and Moment of Inertia. Both these frameworks are restructured based on a case study of a practical project in Chongqing which had achieved the highest GBL and GM awards. Results show that GBL distributes its scores more evenly while GM concentrates on energy saving with greater diversity in land supply and building oper-ations (normalized coefficients of variation of 0.435 and 0.350). The project’s com-pliance coefficients are 1.27 and 0.31 under GBL and GM respectively indicating its higher degree of compliance with the GM framework. The developed model provides multitarget-oriented guidelines for green building design, assessment and stand-arddevelopment

A simplified thermoregulation model of the human body in warm conditions

Thermoregulation models of the human body have been widely used in thermal comfort studies. The existing models are complicated and not fully verified for application in China. This paper presents a simplified thermoregulation model which has been statistically validated by the predicted and measured mean skin temperature in warm environments, including 21 typical conditions with 400 Chinese subjects. This model comprises three parts: i) the physical model; ii) the controlled system; and iii) the controlling system, and considers three key questions formerly ignored by the existing models including: a) the evaporation efficiency of regulatory sweat; b) the proportional relation of total skin blood flow and total heat loss by regulatory sweating against body surface area; and c) discrepancies in the mean skin temperatures by gender. The developed model has been validated to be within the 95% confidence interval of the population mean skin temperature in three cases

Disks around massive young stellar objects: are they common?

We present K-band polarimetric images of several massive young stellar objects at resolutions $\sim$ 0.1-0.5 arcsec. The polarization vectors around these sources are nearly centro-symmetric, indicating they are dominating the illumination of each field. Three out of the four sources show elongated low-polarization structures passing through the centers, suggesting the presence of polarization disks. These structures and their surrounding reflection nebulae make up bipolar outflow/disk systems, supporting the collapse/accretion scenario as their low-mass siblings. In particular, S140 IRS1 show well defined outflow cavity walls and a polarization disk which matches the direction of previously observed equatorial disk wind, thus confirming the polarization disk is actually the circumstellar disk. To date, a dozen massive protostellar objects show evidence for the existence of disks; our work add additional samples around MYSOs equivalent to early B-type stars.Comment: 9 pages, including 2 figures, 1 table, to appear on ApJ

Towards Frame Rate Agnostic Multi-Object Tracking

Multi-Object Tracking (MOT) is one of the most fundamental computer vision tasks which contributes to a variety of video analysis applications. Despite the recent promising progress, current MOT research is still limited to a fixed sampling frame rate of the input stream. In fact, we empirically find that the accuracy of all recent state-of-the-art trackers drops dramatically when the input frame rate changes. For a more intelligent tracking solution, we shift the attention of our research work to the problem of Frame Rate Agnostic MOT (FraMOT). In this paper, we propose a Frame Rate Agnostic MOT framework with Periodic training Scheme (FAPS) to tackle the FraMOT problem for the first time. Specifically, we propose a Frame Rate Agnostic Association Module (FAAM) that infers and encodes the frame rate information to aid identity matching across multi-frame-rate inputs, improving the capability of the learned model in handling complex motion-appearance relations in FraMOT. Besides, the association gap between training and inference is enlarged in FraMOT because those post-processing steps not included in training make a larger difference in lower frame rate scenarios. To address it, we propose Periodic Training Scheme (PTS) to reflect all post-processing steps in training via tracking pattern matching and fusion. Along with the proposed approaches, we make the first attempt to establish an evaluation method for this new task of FraMOT in two different modes, i.e., known frame rate and unknown frame rate, aiming to handle a more complex situation. The quantitative experiments on the challenging MOT datasets (FraMOT version) have clearly demonstrated that the proposed approaches can handle different frame rates better and thus improve the robustness against complicated scenarios.Comment: 21 pages; Author versio

Program Translation via Code Distillation

Software version migration and program translation are an important and costly part of the lifecycle of large codebases. Traditional machine translation relies on parallel corpora for supervised translation, which is not feasible for program translation due to a dearth of aligned data. Recent unsupervised neural machine translation techniques have overcome data limitations by included techniques such as back translation and low level compiler intermediate representations (IR). These methods face significant challenges due to the noise in code snippet alignment and the diversity of IRs respectively. In this paper we propose a novel model called Code Distillation (CoDist) whereby we capture the semantic and structural equivalence of code in a language agnostic intermediate representation. Distilled code serves as a translation pivot for any programming language, leading by construction to parallel corpora which scale to all available source code by simply applying the distillation compiler. We demonstrate that our approach achieves state-of-the-art performance on CodeXGLUE and TransCoder GeeksForGeeks translation benchmarks, with an average absolute increase of 12.7% on the TransCoder GeeksforGeeks translation benchmark compare to TransCoder-ST

SUT: Active Defects Probing for Transcompiler Models

Automatic Program translation has enormous application value and hence has been attracting significant interest from AI researchers. However, we observe that current program translation models still make elementary syntax errors, particularly, when the target language does not have syntax elements in the source language. Metrics like BLUE, CodeBLUE and computation accuracy may not expose these issues. In this paper we introduce a new metrics for programming language translation and these metrics address these basic syntax errors. We develop a novel active defects probing suite called Syntactic Unit Tests (SUT) which includes a highly interpretable evaluation harness for accuracy and test scoring. Experiments have shown that even powerful models like ChatGPT still make mistakes on these basic unit tests. Specifically, compared to previous program translation task evaluation dataset, its pass rate on our unit tests has decreased by 26.15%. Further our evaluation harness reveal syntactic element errors in which these models exhibit deficiencies