Analyzing the Hardware-Software Implications of Multi-modal DNN Workloads using MMBench

The explosive growth of various types of big data and advances in AI technologies have catalyzed a new type of applications called multi-modal DNNs. Multi-modal DNNs are capable of interpreting and reasoning about information from multiple modalities, making them more applicable to real-world AI scenarios. In recent research, multi-modal DNNs have outperformed the best uni-modal DNN in a wide range of applications from traditional multimedia to emerging autonomous systems. However, despite their importance and superiority, very limited research attention has been devoted to understand the characteristics of multi-modal DNNs and their implications on current computing software/hardware platforms. To facilitate research and advance the understanding of these multi-modal DNN workloads, we first present MMbench, an open-source benchmark suite consisting of a set of real-world multi-modal DNN workloads with relevant performance metrics for evaluation. Then we use MMbench to conduct an in-depth analysis on the characteristics of multi-modal DNNs. We study their implications on application and programming framework, operating and scheduling system, as well as execution hardware. Finally, we conduct a case study and extend our benchmark to edge devices. We hope that our work can provide guidance for future software/hardware design and optimization to underpin multi-modal DNNs on both cloud and edge computing platforms

Manifold ranking weighted local maximal occurrence descriptor for person re-identification

Person re-identification is an important task of matching pedestrians across non-overlapping camera views. In this paper, we exploit a weighted feature descriptor for person re-identification.We firstly compute the weights on the superpixel level via graph-based manifold ranking algorithm, then integrate the computed weights into a patch-based feature descriptor, named local maximal occurrence. Finally, the weighted descriptors are fed into a top-push distance learning to mitigate the cross-view gaps. We evaluate the proposed method on three benchmark datasets iLIDS-VID, PRID 450S and VIPeR. The promising experimental results demonstrate the effectiveness of the proposed method comparing with the state-of-the-arts

Sequencing of a Chinese tetralogy of Fallot cohort reveals clustering mutations in myogenic heart progenitors

Tetralogy of Fallot (TOF) is the most common cyanotic heart defect, yet the underlying genetic mechanisms remain poorly understood. Here, we performed whole-genome sequencing analysis on 146 nonsyndromic TOF parent-offspring trios of Chinese ethnicity. Comparison of de novo variants and recessive genotypes of this data set with data from a European cohort identified both overlapping and potentially novel gene loci and revealed differential functional enrichment between cohorts. To assess the impact of these mutations on early cardiac development, we integrated single-cell and spatial transcriptomics of early human heart development with our genetic findings. We discovered that the candidate gene expression was enriched in the myogenic progenitors of the cardiac outflow tract. Moreover, subsets of the candidate genes were found in specific gene coexpression modules along the cardiomyocyte differentiation trajectory. These integrative functional analyses help dissect the pathogenesis of TOF, revealing cellular hotspots in early heart development resulting in cardiac malformations

Using Laser Scanners for Modeling and Analysis in Architecture, Engineering, and Construction

Abstract — Laser scanners are rapidly gaining acceptance as a tool for three dimensional (3D) modeling and analysis in the architecture, engineering, and construction (AEC) domain. Since 2001, our cross-disciplinary research team has been developing new methods for analyzing and modeling laser scanner data, with an emphasis on applications in the AEC domain. This paper provides an overview of our group’s recent research efforts. Our work includes improving our understanding of the low-level aspects of laser scanner data, using comparison methods to analyze laser scanner data and derived models, and developing modeling and recognition algorithms to support the automatic creation of building models from laser scan data. Index Terms—Building information models, laser scanning, reverse engineering. L I

Electrochemical Evaluation of Stress Corrosion Cracking Susceptibility of Ti-6Al-3Nb-2Zr-1Mo Alloy Welded Joint in Simulated Deep-Sea Environment

Titanium alloys have high specific strength and excellent corrosion resistance and have been applied in deep-sea engineering fields. However, stress corrosion cracking may become one of the biggest threats to the service safety of a high-strength titanium alloy, as well as its weldment. In this work, stress corrosion cracking of a gas-tungsten-arc-welded Ti-6Al-3Nb-2Zr-1Mo (Ti6321) alloy influenced by the applied potentials in simulated deep-sea and shallow-sea environments was investigated by combining slow strain rate testing with electrochemical measurements. The results showed that the service environment and applied potential have a substantial effect on the stress corrosion cracking behavior of the Ti6321 welded joint. The Ti6321 welded joint exhibited higher stress corrosion susceptibility in a simulated deep-sea environment and at a strong polarization level owing to the diminishing protection of the passive film under passivation inhibition and the enhancement of the hydrogen effect. The fracture of a Ti6321 welded joint in the weld material could be attributed to the softening effect of the thick secondary α within the coarse-grained martensite. The electrochemical evaluation model of stress corrosion cracking susceptibility of a Ti6321 welded joint in a simulated marine environment was established by adding the criterion in the passivation region based on the literature model, and four potential regions corresponding to different stress corrosion cracking mechanisms were classified and discussed. Our study provides useful guidance for the deep-sea engineering applications of Ti6321 alloys and a rapid assessment method of stress corrosion risk

CYP4F2-Catalyzed Metabolism of Arachidonic Acid Promotes Stromal Cell-Mediated Immunosuppression in Non-Small Cell Lung Cancer

The identification of a role for CYP4F2-dependent metabolism in driving immune evasion in non-small cell lung cancer reveals a strategy to improve the efficacy of immunotherapy by inhibiting CYP4F2. See related article by Van Ginderachter, p. 3882

2D fin field-effect transistors integrated with epitaxial high-k gate oxide

Precise integration of two-dimensional (2D) semiconductors and high-dielectric-constant (k) gate oxides into three-dimensional (3D) vertical-architecture arrays holds promise for developing ultrascaled transistors(1-5), but has proved challenging. Here we report the epitaxial synthesis of vertically aligned arrays of 2D fin-oxide heterostructures, a new class of 3D architecture in which high-mobility 2D semiconductor fin Bi2O2Se and single-crystal high-k gate oxide Bi2SeO5 are epitaxially integrated. These 2D fin-oxide epitaxial heterostructures have atomically flat interfaces and ultrathin fin thickness down to one unit cell (1.2 nm), achieving wafer-scale, site-specific and high-density growth of mono-oriented arrays. The as-fabricated 2D fin field-effect transistors (FinFETs) based on Bi2O2Se/Bi2SeO5 epitaxial heterostructures exhibit high electron mobility (mu) up to 270 cm2 V-1 s(-1), ultralow off-state current (I-OFF) down to about 1 pA mu m(-1), high on/off current ratios (I-ON/I-OFF) up to 10(8) and high on-state current (I-ON) up to 830 mu A mu m(-1) at 400-nm channel length, which meet the low-power specifications projected by the International Roadmap for Devices and Systems (IRDS)(6). The 2D fin-oxide epitaxial heterostructures open up new avenues for the further extension of Moore's law