Relation Networks for Object Detection

Although it is well believed for years that modeling relations between objects would help object recognition, there has not been evidence that the idea is working in the deep learning era. All state-of-the-art object detection systems still rely on recognizing object instances individually, without exploiting their relations during learning. This work proposes an object relation module. It processes a set of objects simultaneously through interaction between their appearance feature and geometry, thus allowing modeling of their relations. It is lightweight and in-place. It does not require additional supervision and is easy to embed in existing networks. It is shown effective on improving object recognition and duplicate removal steps in the modern object detection pipeline. It verifies the efficacy of modeling object relations in CNN based detection. It gives rise to the first fully end-to-end object detector

Information Technology, Cross-Channel Capabilities, and Managerial Actions: Evidence from the Apparel Industry

Information technology (IT) has changed the dynamics of competition in the U.S. economy. Firms are gaining competitive advantage by competing on technology-enabled processes. For the retail industry, technology is breaking down the barriers between different retail channels and is making omnichannel retailing inevitable—an integrated sales experience that melds touch-and-feel information in the physical world with online content. Omnichannel retailing is becoming a trend and critical for retailers’ success. To keep up with the pace of change, existing retailers will need to create an omnichannel strategy and develop more omnichannel innovations. Based on the theories of the resource-based view (RBV), IT business value, and competitive dynamics, this study examines the factors that affect cross-channel capabilities and managerial actions in the U.S. apparel industry. We collected a longitudinal dataset on public apparel companies from 1995 to 2007. The empirical results reveal that both the quantity and scope of investments in enterprise IT applications were positively related to cross-channel capabilities. Financial resources positively moderated the relationship between enterprise IT applications and cross-channel capabilities. We found that enterprise IT applications increased the frequency and broadened the types of managerial actions. We found that cross-channel capabilities had mixed effects on managerial actions. Whereas market-oriented capabilities such as e-commerce and multi-channel cross-selling capabilities broadened the types of managerial actions, operation-oriented capabilities, such as cross-channel fulfillment, narrow the range of a firm’s managerial actions. Our findings provide important implications for managers in apparel and other retail sectors

Deformable Convolutional Networks

Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in its building modules. In this work, we introduce two new modules to enhance the transformation modeling capacity of CNNs, namely, deformable convolution and deformable RoI pooling. Both are based on the idea of augmenting the spatial sampling locations in the modules with additional offsets and learning the offsets from target tasks, without additional supervision. The new modules can readily replace their plain counterparts in existing CNNs and can be easily trained end-to-end by standard back-propagation, giving rise to deformable convolutional networks. Extensive experiments validate the effectiveness of our approach on sophisticated vision tasks of object detection and semantic segmentation. The code would be released

Novel power conditioning circuits for piezoelectric micro power generators

Advanced low power devices promote the development of micro power generators (MPGs) to replace the batteries to power them. Due to the trend in decreasing integrated circuit (IC) supply voltages, power supply designers are facing more and more serious challenges. The objective of this research is to design a power conditioning circuit (PCC) for use in conjunction with low voltage microelectromechanical systems (MEMS)-based Palouse Piezoelectric Power (P3) micro heat engine power generation systems. The PCC enables maximum power extraction from a piezoelectric MPG. The proposed PCC includes a rectifier stage and a regulator stage. The rectifier stage is based on the synchronous rectification technique. The dc-dc regulator is a charge pump-based step-down converter. Interleaved discharge (ID) is proposed to reduce the output voltage ripple significantly, without sacrificing the converter efficiency. The proposed step-down charge pump is analyzed with state-space averaging. In order to facilitate the PSpice simulation of the lead zirconate titanate (PZT) membrane, a simplified PZT model was developed. Both the rectifier and the charge pump are simulated with PSpice. Simulations show that the interleaved discharge method takes full advantage of the step-down charge pump structure, and provides flexibilities to the design of step-down charge pumps. The designed 200mW 5V/1.2V charge pump has an efficiency of 92.2%, with reduced output ripple. Proof-of-concept demonstration of the proposed PCC includes a 4-stage completely passive charge pump driving an analog wristwatch, proving proper operation of the entire P3 micro power system. A maximum output power of 18.8mW has been extracted from a single piezoelectric MPG, with 92% efficiency in the rectifier stage. Arbitrary waveform generator representation (AWGR) of the piezoelectric membrane is also presented. AWGR facilitates ongoing tests and demonstrates the feasibility of cascading many MPGs to extract additional power

Criteria of evaluating initial model for effective dynamic model updating

Finite element model updating is an important research field in structural dynamics. Though a variety of updating methods have been proposed in the past decades, all the methods could be effective only on the assumption that the initial finite element model is updatable. The assumption has led to the fact that many researchers study on how to update the model while little attention is paid to studies on whether the model is updatable. This has become inevitable obstacle between research and engineering applications because the assumption is not a tenable hypothesis in practice. To circumvent this problem, the evaluation of model updatability is studied in this paper. Firstly, two conditional statements about mapping are proved as a theoretical basis. Then, two criteria for evaluation of initial models are deduced. A beam is employed in the numerical simulations. Two different initial models for the beam are constructed with different boundary conditions. The models are evaluated using the proposed criteria. The results indicate that the criteria are able to distinguish the model updatability

Increasing The Odds Of Hit Iidentification By Screening Against Receptor Homologs

Increasing the odds of hit identification in screening is of significance for drug discovery. The odds for finding a hit are closely related either to the diversity of libraries or to the availability of focused libraries. There are no truly diverse libraries and it is difficult to design focused libraries without sufficient information. Hence it is helpful to consider alternative approaches that can enhance the odds using existing libraries. Multiple members of a protein family have been considered collectively in inhibitor design, on the basis of the correlation between protein families and ligands derived from specific compound classes. Such a correlation has been exploited in various drug discovery studies and a general receptor-homolog-based screening scheme may be devised. The feasibility of such a scheme in enhancing the odds of hit identification is discussed.Singapore-MIT Alliance (SMA

Transfer Learning Applied to Stellar Light Curve Classification

Variability carries physical patterns and astronomical information of objects, and stellar light curve variations are essential to understand the stellar formation and evolution processes. The studies of variations in stellar photometry have the potential to expand the list of known stars, protostars, binary stars, and compact objects, which could shed more light on stages of stellar lifecycles. The progress in machine-learning techniques and applications has developed modern algorithms to detect and condense features from big data, which enables us to classify stellar light curves efficiently and effectively. We explore several deep-learning methods on variable star classifications. The sample of light curves is constructed with $\delta$ Scuti, $\gamma$ Doradus, RR Lyrae, eclipsing binaries, and hybrid variables from \textit{Kepler} observations. Several algorithms are applied to transform the light curves into images, continuous wavelet transform (CWT), Gramian angular fields, and recurrent plots. We also explore the representation ability of these algorithms. The processed images are fed to several deep-learning methods for image recognition, including VGG-19, GoogLeNet, Inception-v3, ResNet, SqueezeNet, and Xception architectures. The best transformation method is CWT, resulting in an average accuracy of 95.6\%. VGG-19 shows the highest average accuracy of 93.25\% among all architectures, while it shows the highest accuracy of 97.2\% under CWT transformation method. The prediction can reach $\sim1000$ light curves per second by using NVIDIA RTX 3090. Our results indicate that the combination of big data and deep learning opens a new path to classify light curves automatically.Comment: 30 pages, 19 figure