The bridge / the stream / the home: interactive social housing typology in Wuxi

Wuxi is a city built on the regional river system. For thousands of years, the city layout developed along with the river channels. The rivers served people’s daily living, farming, and transportation needs. Social life among residents had also developed at the communal and transitional space along the rivers, mainly at the intersections where the bridges were. As Wuxi’s industry and commerce developed rapidly in the 20th century because of the convenient water transportation system, Wuxi’s urban area expanded widely in a short time. However, Wuxi’s overwhelming urban expansion happened too fast and lacked sophisticated urban planning consideration. This decision has isolated the urban zones and landscape zones, as well as cut off citizens’ daily access to natural areas. Meanwhile, there are both overdevelopment and underdevelopment situations in residential areas in the city. While there are plenty of enclosed, single-use zoning residential communities newly built with high rises, there are also old derelict neighborhoods abandoned in the old town at the core of Wuxi. As such two extreme situations existing at the same time in Wuxi, citizens’ living quality still has a large room for improvement that would recur the beautiful vision of living in a “natural city” with the “natural” traditional lifestyle. In response, this design proposal proposes a solution in the middle for a new residential area development typology. It is reconstituting areas of my research into new housing type. In this new scenario, the standard high-rise residential area’s spatial and structural layout is redesigned for mix-used purposes. Nature here is not just a landscape attraction for aesthetics, but also an incentive that stimulates and leads more social activities to happen in the residents’ contemporary daily life in the high rises, as the traditional lifestyle had. The site is one of the derelict and abandoned residential areas in the city core, with a total area of around 2 million square feet. Since the existing houses are heavily damaged and have no value for preservation, this design proposal tears down the entire area to build a new diverse residential neighborhood with a new social model for 3000 households

LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusion

As an emerging technology and a relatively affordable device, the 4D imaging radar has already been confirmed effective in performing 3D object detection in autonomous driving. Nevertheless, the sparsity and noisiness of 4D radar point clouds hinder further performance improvement, and in-depth studies about its fusion with other modalities are lacking. On the other hand, most of the camera-based perception methods transform the extracted image perspective view features into the bird's-eye view geometrically via "depth-based splatting" proposed in Lift-Splat-Shoot (LSS), and some researchers exploit other modals such as LiDARs or ordinary automotive radars for enhancement. Recently, a few works have applied the "sampling" strategy for image view transformation, showing that it outperforms "splatting" even without image depth prediction. However, the potential of "sampling" is not fully unleashed. In this paper, we investigate the "sampling" view transformation strategy on the camera and 4D imaging radar fusion-based 3D object detection. In the proposed model, LXL, predicted image depth distribution maps and radar 3D occupancy grids are utilized to aid image view transformation, called "radar occupancy-assisted depth-based sampling". Experiments on VoD and TJ4DRadSet datasets show that the proposed method outperforms existing 3D object detection methods by a significant margin without bells and whistles. Ablation studies demonstrate that our method performs the best among different enhancement settings

Stacking up electron-rich and electron-deficient monolayers to achieve extraordinary mid- to far-infrared excitonic absorption: Interlayer excitons in the C3B/C3N bilayer

Our ability to efficiently detect and generate far-infrared (i.e., terahertz) radiation is vital in areas spanning from biomedical imaging to interstellar spectroscopy. Despite decades of intense research, bridging the terahertz gap between electronics and optics remains a major challenge due to the lack of robust materials that can efficiently operate in this frequency range, and two-dimensional (2D) type-II heterostructures may be ideal candidates to fill this gap. Herein, using highly accurate many-body perturbation theory within the GW plus Bethe-Salpeter equation approach, we predict that a type-II heterostructure consisting of an electron rich C3N and an electron deficient C3B monolayers can give rise to extraordinary optical activities in the mid- to far-infrared range. C3N and C3B are two graphene-derived 2D materials that have attracted increasing research attention. Although both C3N and C3B monolayers are moderate gap 2D materials, and they only couple through the rather weak van der Waals interactions, the bilayer heterostructure surprisingly supports extremely bright, low-energy interlayer excitons with large binding energies of 0.2 ~ 0.4 eV, offering an ideal material with interlayer excitonic states for mid-to far-infrared applications at room temperature. We also investigate in detail the properties and formation mechanism of the inter- and intra-layer excitons.Comment: 15 pages, 6 figure

Are They All Good? Studying Practitioners' Expectations on the Readability of Log Messages

Developers write logging statements to generate logs that provide run-time information for various tasks. The readability of log messages in the logging statements (i.e., the descriptive text) is rather crucial to the value of the generated logs. Immature log messages may slow down or even obstruct the process of log analysis. Despite the importance of log messages, there is still a lack of standards on what constitutes good readability in log messages and how to write them. In this paper, we conduct a series of interviews with 17 industrial practitioners to investigate their expectations on the readability of log messages. Through the interviews, we derive three aspects related to the readability of log messages, including Structure, Information, and Wording, along with several specific practices to improve each aspect. We validate our findings through a series of online questionnaire surveys and receive positive feedback from the participants. We then manually investigate the readability of log messages in large-scale open source systems and find that a large portion (38.1%) of the log messages have inadequate readability. Motivated by such observation, we further explore the potential of automatically classifying the readability of log messages using deep learning and machine learning models. We find that both deep learning and machine learning models can effectively classify the readability of log messages with a balanced accuracy above 80.0% on average. Our study provides comprehensive guidelines for composing log messages to further improve practitioners' logging practices.Comment: Accepted as a research paper at the 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023

Deep Instance Segmentation with Automotive Radar Detection Points

Automotive radar provides reliable environmental perception in all-weather conditions with affordable cost, but it hardly supplies semantic and geometry information due to the sparsity of radar detection points. With the development of automotive radar technologies in recent years, instance segmentation becomes possible by using automotive radar. Its data contain contexts such as radar cross section and micro-Doppler effects, and sometimes can provide detection when the field of view is obscured. The outcome from instance segmentation could be potentially used as the input of trackers for tracking targets. The existing methods often utilize a clustering based classification framework, which fits the need of real-time processing but has limited performance due to minimum information provided by sparse radar detection points. In this paper, we propose an efficient method based on clustering of estimated semantic information to achieve instance segmentation for the sparse radar detection points. In addition, we show that the performance of the proposed approach can be further enhanced by incorporating the visual multi-layer perceptron. The effectiveness of the proposed method is verified by experimental results on the popular RadarScenes dataset, achieving 89.53% mCov and 86.97% mAP0.5, which is the best comparing to other approaches in the literature. More significantly, the proposed algorithm consumes memory around 1MB, and the inference time is less than 40ms. These two criteria ensure the practicality of the proposed method in real-world system

Dynamic spin-lattice coupling and nematic fluctuations in NaFeAs

We use inelastic neutron scattering to study acoustic phonons and spin excitations in single crystals of NaFeAs, a parent compound of iron pnictide superconductors. NaFeAs exhibits a tetragonal-to-orthorhombic structural transition at $T_s\approx 58$ K and a collinear antiferromagnetic (AF) order at $T_N\approx 45$ K. While longitudinal and out-of-plane transverse acoustic phonons behave as expected, the in-plane transverse acoustic phonons reveal considerable softening on cooling to $T_s$, and then harden on approaching $T_N$ before saturating below $T_N$. In addition, we find that spin-spin correlation lengths of low-energy magnetic excitations within the FeAs layer and along the $c$-axis increase dramatically below $T_s$, and show weak anomaly across $T_N$. These results suggest that the electronic nematic phase present in the paramagnetic tetragonal phase is closely associated with dynamic spin-lattice coupling, possibly arising from the one-phonon-two-magnon mechanism