Miracle-dz/UniFAR-FTUs huggingface dataset:
Loopless Semi-Stochastic Gradient Descent with Less Hard Thresholding for Sparse Learning

Stochastic gradient hard thresholding methods have recently been shown to work favorably for solving large-scale empirical risk minimization problems under sparsity constraints. Many stochastic hard thresholding methods (e.g., SVRG-HT) conduct a full gradient update with a constant frequency and perform a hard thresholding operation at each iteration, which leads to a high computational complexity especially for high-dimensional and sparse problems. To be more efficient in large-scale datasets, we propose an efficient single-layer semi-stochastic gradient hard thresholding (LSSG-HT) method. The proposed algorithm updates full gradient with a given probability p and reduces lots of hard thresholding operations by setting frequency m, which reduces hard thresholding complexity in theory to O(κ_s/młog(1/ε)) compared with O(κ_słog(1/ε)) of SVRG-HT. We prove that our algorithm can converge to an optimal solution with a linear convergence rate. Furthermore, we also present an asynchronous parallel variant of LSSG-HT. Numerical experimental results demonstrate that the efficiency of our algorithms with comparison against the state-of-the-art algorithms.
Face and Expression Analysis Using Local Directional Number Patter

LDN encodes the directional information of the face’s textures in a compacted way, generating a more discriminative code than current methods. LDN encodes the structure of a local neighborhood by analyzing its directional information. The edge response in the neighborhood is computed in eight different directions with a compass mask. From all the directions the top positive and negative directions are chosen to produce a meaningful descriptor for different textures with similar structural patterns. The extracted information is encoded using the prominent direction indices such as directional numbers and sign which allows us to distinguish among similar structural patterns that have different intensity transitions.
Are Out-of-Distribution Detection Methods Effective on Large-Scale Datasets?

Supervised classification methods often assume the train and test data distributions are the same and that all classes in the test set are present in the training set. However, deployed classifiers often require the ability to recognize inputs from outside the training set as unknowns. This problem has been studied under multiple paradigms including out-of-distribution detection and open set recognition. For convolutional neural networks, there have been two major approaches: 1) inference methods to separate knowns from unknowns and 2) feature space regularization strategies to improve model robustness to outlier inputs. There has been little effort to explore the relationship between the two approaches and directly compare performance on anything other than small-scale datasets that have at most 100 categories. Using ImageNet-1K and Places-434, we identify novel combinations of regularization and specialized inference methods that perform best across multiple outlier detection problems of increasing difficulty level. We found that input perturbation and temperature scaling yield the best performance on large scale datasets regardless of the feature space regularization strategy. Improving the feature space by regularizing against a background class can be helpful if an appropriate background class can be found, but this is impractical for large scale image classification datasets.
Mobile Photometric Stereo with Keypoint-Based SLAM for Dense 3D Reconstruction

The standard photometric stereo is a technique to densely reconstruct objects' surfaces using light variation under the assumption of a static camera with a moving light source. In this work, we use photometric stereo to reconstruct dense 3D scenes while moving the camera and the light altogether. In such non-static case, camera poses as well as correspondences between pixels of each frame to apply photometric stereo are required. ORB-SLAM is a technique that can be used to estimate camera poses. To retrieve correspondences, our idea is to start from a sparse 3D mesh obtained with ORB SLAM and then densify the mesh by a plane sweep method using a multi-view photometric consistency. By combining ORB-SLAM and photometric stereo, it is possible to reconstruct dense 3D scenes with a off-the-shelf smartphone and its embedded torchlight. Note that SLAM systems usually struggle with textureless object, which is effectively compensated by the photometric stereo in our method. Experiments are conducted to show that our proposed method gives better results than SLAM alone or COLMAP, especially for partially textureless surfaces.
Wegner In memory of

Leo Kadanoff has worked in many fields of statistical mechani cs. His contributions had an enormous impact. This holds in particu lar for critical phenomena, where he explained Widom’s homogeneity laws by mean s of block-spin transformations and laid the basis for Wilson’s renormaliz ation group equation. I had the pleasure to work in his group for one year. A short hist orically account is given.
L-STAP: Learned Spatio-Temporal Adaptive Pooling for Video Captioning

Automatic video captioning can be used to enrich TV programs with textual informations on scenes. These informations can be useful for visually impaired people, but can also be used to enhance indexing and research of TV records. Video captioning can be seen as being more challenging than image captioning. In both cases, we have to tackle a challenging task where a visual object has to be analyzed, and translated into a textual description in natural language. However, analyzing videos requires not only to parse still images, but also to draw correspondences through time. Recent works in video captioning have intended to deal with these issues by separating spatial and temporal analysis of videos. In this paper, we propose a Learned Spatio-Temporal Adaptive Pooling (L-STAP) method that combines spatial and temporal analysis. More specifically, we first process a video frame-by-frame through a Convolutional Neural Network. Then, instead of applying an average pooling operation to reduce dimensionality, we apply our L-STAP, which attends to specific regions in a given frame based on what appeared in previous frames. Experiments on MSVD and MSR-VTT datasets show that our method outperforms state-of-the-art methods on the video captioning task in terms of several evaluation metrics.
Experimental Study of Multivariate Time Series Forecasting Models

Multivariate time series forecasting has wide applications such as traffic flow prediction, supermarket commodity demand forecasting and etc. In literature, Due to the complex temporal patterns and inter-dependencies among multivariate time series, a large number of forecasting models have been developed. However, one question still remains unclear: how these models perform on a certain forecasting task, and there is lack of comprehensive performance comparison of these models on different tasks. To this end, in this paper, we conduct a systematic evaluation of eight representative forecasting models over eight multivariate time series datasets, and have the following findings: 1) When the datasets exhibit strong periodic patterns, deep learning models perform best. Otherwise on the datasets in a non-periodic manner, the statistical models such as ARIMA perform best. 2) For the long term prediction involving a high horizon value, the direct prediction strategy could lead to lower errors than the recursive one, but at the cost of higher training time. 3) For the multivariate time series explicitly involving graphic inter-dependencies among the multivariates, e.g., the road network topology in the spatio-temporal time series of traffic volumes in multiple routes, the Graph Convolution Network can incorporate the graphic inter-dependencies into their forecasting models for smaller prediction errors.
Testing of PUF based secure ICs

Secure systems based on Physical Unclonable Functions (PUFs) are gaining popularity due to their hardware intrinsic security. Achieving high quality in such a system requires high quality manufacturing tests; hence, a good controllability and observability of the internal nodes of the system are required. However, this makes it easier for hackers to get access to internal secret information. Therefore, providing a high quality test while making it very hard/impossible to hack the system is a major challenge. This thesis targets a secure test approach for Fuzzy Extractors (FEs), which constitutes the main component of PUF systems. Two novel Built-in self-test (BIST) methods are proposed; daisy chaining and parallel. Both methods provide a higher security compared to existing methods, due to the fact that they are both scan-chain free and do not require any external connections. The daisy chaining test method reuses existing blocks in the FE for pattern generation and output compression to minimize the area overhead; the method realizes a fault coverage of 95% at the cost of 50k clock cycles and an area overhead of only 2.2%. The parallel test method tests individual and simultaneously each of the Fuzzy Extractor blocks with a Random Test Pattern Generator (RTPG), minimizing the overall test time; the test method realizes a fault coverage of 95% at the cost of 8k clock cycles and an area overhead of 18.6%. The fault coverage can be increased at the cost of either test time or area overhead. The methods presented are generic and are applicable to any Fuzzy Extractor.
MONOPOLY: Learning to Price Public Facilities for Revaluing Private Properties with Large-Scale Urban Data

The value assessment of private properties is an attractive but challenging task which is widely concerned by a majority of people around the world. A prolonged topic among us is "how much is my house worth? ". To answer this question, most experienced agencies would like to price a property given the factors of its attributes as well as the demographics and the public facilities around it. However, no one knows the exact prices of these factors, especially the values of public facilities which may help assess private properties. In this paper, we introduce our newly launched project "Monopoly" (named after a classic board game) in which we propose a distributed approach for revaluing private properties by learning to price public facilities (such as hospitals, schools, and metros) with the large-scale urban data we have accumulated via Baidu Maps. To be specific, our method organizes many points of interest (POIs) into an undirected weighted graph and formulates multiple factors including the virtual prices of surrounding public facilities as adaptive variables to parallelly estimate the housing prices we know. Then the prices of both public facilities and private properties can be iteratively updated according to the loss of prediction until convergence. We have conducted extensive experiments with the large-scale urban data of several metropolises in China. Results show that our approach outperforms several mainstream methods with significant margins. Further insights from more in-depth discussions demonstrate that the "Monopoly" is an innovative application in the interdisciplinary field of business intelligence and urban computing, and it will be beneficial to tens of millions of our users for investments and to the governments for urban planning as well as taxation.
SyDPose: Object Detection and Pose Estimation in Cluttered Real-World Depth Images Trained using Only Synthetic Data

Object pose estimation is an important problem in robotics because it supports scene understanding and enables subsequent grasping and manipulation. Many methods, including modern deep learning approaches, exploit known object models, however, in industry these are difficult and expensive to obtain. 3D CAD models, on the other hand, are often readily available. Consequently, training a deep architecture for pose estimation exclusively from CAD models leads to a considerable decrease of the data creation effort. While this has been shown to work well for feature-and template-based approaches, real-world data is still required for pose estimation in clutter using deep learning. We use synthetically created depth data with domain-relevant background randomized noise heuristics to train an end-to-end, multi-task network, for pose estimation. We simultaneously detect, classify and estimate the poses of texture-less objects in cluttered real-world depth images of an arbitrary amount of objects. We present the results of our experiments with the LineMOD and the Occlusion dataset.
Time Series Prediction with Interpretable Data Reconstruction

Time series prediction plays a key role in wide applications and has been investigated for a couple of decades. Nevertheless, most of the prior works fail to identify the most effective frequency components of time series before passing through the prediction, which induces the drop of the performance. In this paper, we propose a novel predictor which integrates the sequence to sequence (seq2seq) model based on long short-term memory units (LSTM) with interpretable data reconstruction, where the learned hidden state is taken as a bridge. The reconstructor can effectively regularize data based on the learned frequency components and extract the effective components of time series. Moreover, we present an alternative training mechanism and a dedicated loss function to guarantee the success of prediction. Experimental results on extensive real-world datasets show a prominent superiority in comparison to state-of-the-art methods.
Learning to Effectively Estimate the Travel Time for Fastest Route Recommendation

Fastest Route Recommendation (FRR) aims to find the fastest path in response to user's queries in a large complex road network. Early studies cast the FRR task as a pathfinding problem on graphs and adopt heuristic algorithms as the major solution due to the efficiency and robustness. A major problem of heuristic algorithms is that the heuristic function is usually empirically set with simple methods, which is difficult to model other useful factors. In this paper, we extend the classic A* algorithm for the FRR task by modeling complex traffic information with neural networks. Specially, we identify an important factor that is important to improve the FRR task, i.e. the estimation of travel time. For this purpose, we first develop a module for predicting the time-varying traffic speed for a road segment, which is the foundation for estimating the travel time. Conditioned on this module, we further design another module to estimate the fastest travel time between two locations connected by routes. We adopt neural networks to implement both modules for enabling the capacity of modeling complex traffic characteristics and dynamics. In this way, the original two cost functions of A* algorithm have been set in a more principled way with neural networks. To our knowledge, we are the first to use neural networks for improving A* algorithm in the FRR task. It elegantly combines the merits of A* algorithm and the powerful modeling capacities of neural networks for the FRR task. Extensive results on the three real-world datasets have shown the effectiveness and robustness of the proposed model.
Intent Term Weighting in E-commerce Queries

E-commerce search engines can fail to retrieve results that satisfy a query's product intent because: (i) conventional retrieval approaches, such as BM25, may ignore the important terms in queries owing to their low "inverse document frequency" " (IDF), and (ii) for long queries, as is usually the case in rare queries (i.e., tail queries), they may fail to determine the relevant terms that are representative of the query's product intent. In this paper, we leverage the historical query reformulation logs of a large e-retailer (walmart.com) to develop a distant-supervision-based approach to identify the relevant terms that characterize the query's product intent. The key idea underpinning our approach is that the terms retained in the reformulation of a query are more important in describing the query's product intent than the discarded terms. Additionally, we also use the fact that the significance of a term depends on its context (other terms in the neighborhood) in the query to determine the term's importance towards the query's product intent. We show that identifying and emphasizing the terms that define the query's product intent leads to a 3% improvement in ranking and outperforms the context-unaware baselines.
A review of GPS/GLONASS studies of the ionospheric response to natural and anthropogenic processes and phenomena

The article is a review of studies of ionospheric effects carried out in ISTP SB RAS. The main results of GPS/GLONASS radio sounding of ionospheric disturbances of natural and anthropogenic origin are presented. The article is devoted to ionospheric effects of solar eclipses, solar flares, solar terminator, earthquakes, tropical cyclones, large-scale ionospheric disturbances of auroral origin, rocket launches. Dynamics of global electron content analysis is also presented. The special attention is paid on the influence of solar flares and ionospheric irregularities on GPS and GLONASS performance. The work is a tribute to the leader of GNSS-monitoring workgroup Prof. E.L. Afraimovich (12 March 1940–8 November 2009).
An Improved Key Graph based Key Management Scheme for Smart Grid AMI systems

In this paper, we focus on versatile and scalable key management for Advanced Metering Infrastructure (AMI) in Smart Grid (SG). We show that a recently proposed key graph based scheme for AMI systems (VerSAMI) suffers from efficiency flaws in its broadcast key management protocol. Then, we propose a new key management scheme (iVerSAMI) by modifying VerSAMI's key graph structure and proposing a new broadcast key update process. We analyze security and performance of the proposed broadcast key management in details to show that iVerSAMI is secure and efficient in terms of storage and communication overheads.
SkyRec: Finding Pareto Optimal Groups

We present SkyRec (Skyline Recommender), a recommendation toolkit for finding optimal groups based on the notion of group skyline. Skyline computation, aiming at identifying a set of skyline points that are not dominated by any other point, is particularly useful for multi-criteria data analysis and decision-making. Traditional skyline computation, however, is inadequate to answer queries that need to analyze not only individual points but also groups of points. o address this gap, SkyRec finds Pare to optimal groups with two group skyline models: G-Skyline [3] and Sum-Skyline [2]. SkyRecre turns Pare to optimal groups with group size k that are not dom-inated by any other group with the same group size. Users can examine the results of the group skyline based recommendation compared to traditional top-k and skyline based recommendation and how different group skyline notions differ from each other. Although we demonstrate Sky Rec for hotel reservation in this paper, it can be applied to various decision-making applications
Cross-Domain Face Synthesis using a Controllable GAN

The performance of face recognition (FR) systems for video surveillance has been shown to improve when the design data is augmented through synthetic face generation. This is true, for instance, with pair-wise matchers (e.g., deep Siamese networks) that rely on a reference gallery, typically with one still image per individual. However, generating synthetic images based on stills (from the source domain) may not improve performance during operations due to the domain shift w.r. t. the target domain. Moreover, despite the emergence of Generative Adversarial Networks (GANs) for realistic synthetic generation, it is often difficult to control the conditions under which synthetic faces are generated. In this paper, a cross-domain face synthesis approach is proposed that integrates a new Controllable GAN (C-GAN). It employs an off-the-shelf 3D face model as a simulator to generate facial images under various poses. The simulated images and noise are input to the C-GAN for realism refinement. It relies on an additional adversarial game as a third player to preserve the identity and specific facial attributes of the refined images. This allows generating realistic synthetic face images that reflect capture conditions in the target domain, while controlling the GAN output such that faces may be generated under desired pose conditions. Experiments were performed using videos from the Chokepoint and COX-S2V datasets, and a deep Siamese network for FR with a single reference still per person. Results indicate that the proposed approach can provide a higher level of accuracy compared to state- of-the-art approaches for synthetic data augmentation1.
Learning to Customize Language Model for Personalized Conversation Systems

Personalized conversation systems have received increasing attention recently. Existing personalized conversation models tend to employ the meta-learning framework that ﬁrst ﬁnds the initial parameters, then ﬁne-tunes on a few personal utterances. However, ﬁne-tuning can only make slight changes to the initial parameters, resulting in similar language models for different users. In this paper, we propose to customize a conversation model with unique network structures for each user. Concretely, we introduce a private network to the language model, whose structure will evolve during training to better capture the unique characteristics of the user. The private network is only trained on the corpora of the corresponding user, and similar users can share partial private structure for data reuse purpose. Experiment results show that our algo-rithm excels all the baselines in terms of personality, quality, and diversity measurement.
Exploration and Exploitation of Off-path Cached Content in Network Coding Enabled Named Data Networking

Named Data Networking (NDN) intrinsically supports in-network caching and multipath forwarding. The two salient features offer the potential to simultaneously transmit content segments that comprise the requested content from original content publishers and in-network caches. However, due to the complexity of maintaining the reachability information of off-path cached content at the fine-grained packet level of granularity, the multipath forwarding and off-path cached copies are significantly underutilized in NDN so far. Network coding enabled NDN, referred to as NC-NDN, was proposed to effectively utilize multiple on-path routes to transmit content, but off-path cached copies are still unexploited. This work enhances NC-NDN with an On-demand Off-path Cache Exploration based Multipath Forwarding strategy, dubbed as O2CEMF, to take full advantage of the multipath forwarding to efficiently utilize off-path cached content. In O2CEMF, each network node reactively explores the reachability information of nearby off-path cached content when consumers begin to request a generation of content, and maintains the reachability at the coarse-grained generation level of granularity instead. Then the consumers simultaneously retrieve content from the original content publisher(s) and the explored capable off-path caches. Our experimental studies validate that this strategy improves the content delivery performance efficiently as compared to that in the present NC-NDN.