OpenRaiser/Intern-Atlas huggingface dataset:
Non-Blind Strategies in Timed Network Congestion Games
Venue: arXiv cs.GT 2022
Year: 2022

Network congestion games are a convenient model for reasoning about routing problems in a network: agents have to move from a source to a target vertex while avoiding congestion, measured as a cost depending on the number of players using the same link. Network congestion games have been extensively studied over the last 40 years, while their extension with timing constraints were considered more recently. Most of the results on network congestion games consider blind strategies: they are static, and do not adapt to the strategies selected by the other players. We extend the recent results of [Bertrand et al., Dynamic network congestion games. FSTTCS'20] to timed network congestion games, in which the availability of the edges depend on (discrete) time. We prove that computing Nash equilibria satisfying some constraint on the total cost (and in particular, computing the best and worst Nash equilibria), and computing the social optimum, can be achieved in exponential space. The social optimum can be computed in polynomial space if all players have the same source and target.
An Integrated System of Drug Matching and Abnormal Approval Number Correction
Venue: arXiv cs.AI 2022
Year: 2022

This essay is based on the joint project with 111, Inc. The pharmacy e-Commerce business grows rapidly in recent years with the ever-increasing medical demand during the pandemic. A big challenge for online pharmacy platforms is drug product matching. The e-Commerce platform usually collects drug product information from multiple data sources such as the warehouse or retailers. Therefore, the data format is inconsistent, making it hard to identify and match the same drug product. This paper creates an integrated system for matching drug products from two data sources. Besides, the system would correct some inconsistent drug approval numbers based on a Naive-Bayes drug type (Chinese or Non-Chinese Drug) classifier. Our integrated system achieves 98.3% drug matching accuracy, with 99.2% precision and 97.5% recall
Mapping Mid-air Haptics with a Low-cost Tactile Robot
Venue: arXiv cs.RO 2022
Year: 2022

Mid-air haptics create a new mode of feedback to allow people to feel tactile sensations in the air. Ultrasonic arrays focus acoustic radiation pressure in space, to induce tactile sensation from the resulting skin deflection. In this work, we present a low-cost tactile robot to test mid-air haptics. By combining a desktop robot arm with a 3D-printed biomimetic tactile sensor, we developed a system that can sense, map, and visualize mid-air haptic sensations created by an ultrasonic transducer array. We evaluate our tactile robot by testing it on a variety of mid-air haptic stimuli, including unmodulated and modulated focal points that create a range of haptic shapes. We compare the mapping of the stimuli to another method used to test mid-air haptics: Laser Doppler Vibrometry, highlighting the advantages of the tactile robot including far lower cost, a small lightweight form-factor, and ease-of-use. Overall, these findings indicate our method has multiple benefits for sensing mid-air haptics and opens up new possibilities for expanding the testing to better emulate human haptic perception.
CRFormer: A Cross-Region Transformer for Shadow Removal
Venue: arXiv cs.CV 2022
Year: 2022

Aiming to restore the original intensity of shadow regions in an image and make them compatible with the remaining non-shadow regions without a trace, shadow removal is a very challenging problem that benefits many downstream image/video-related tasks. Recently, transformers have shown their strong capability in various applications by capturing global pixel interactions and this capability is highly desirable in shadow removal. However, applying transformers to promote shadow removal is non-trivial for the following two reasons: 1) The patchify operation is not suitable for shadow removal due to irregular shadow shapes; 2) shadow removal only needs one-way interaction from the non-shadow region to the shadow region instead of the common two-way interactions among all pixels in the image. In this paper, we propose a novel cross-region transformer, namely CRFormer, for shadow removal which differs from existing transformers by only considering the pixel interactions from the non-shadow region to the shadow region without splitting images into patches. This is achieved by a carefully designed region-aware cross-attention operation that can aggregate the recovered shadow region features conditioned on the non-shadow region features. Extensive experiments on ISTD, AISTD, SRD, and Video Shadow Removal datasets demonstrate the superiority of our method compared to other state-of-the-art methods.
GazBy: Gaze-Based BERT Model to Incorporate Human Attention in Neural Information Retrieval
Venue: arXiv cs.IR 2022
Year: 2022

This paper is interested in investigating whether human gaze signals can be leveraged to improve state-of-the-art search engine performance and how to incorporate this new input signal marked by human attention into existing neural retrieval models. In this paper, we propose GazBy ({\bf Gaz}e-based {\bf B}ert model for document relevanc{\bf y}), a light-weight joint model that integrates human gaze fixation estimation into transformer models to predict document relevance, incorporating more nuanced information about cognitive processing into information retrieval (IR). We evaluate our model on the Text Retrieval Conference (TREC) Deep Learning (DL) 2019 and 2020 Tracks. Our experiments show encouraging results and illustrate the effective and ineffective entry points for using human gaze to help with transformer-based neural retrievers. With the rise of virtual reality (VR) and augmented reality (AR), human gaze data will become more available. We hope this work serves as a first step exploring using gaze signals in modern neural search engines.
Robot Vitals and Robot Health: Towards Systematically Quantifying Runtime Performance Degradation in Robots Under Adverse Conditions
Venue: arXiv cs.RO 2022
Year: 2022

This paper addresses the problem of automatically detecting and quantifying performance degradation in remote mobile robots during task execution. A robot may encounter a variety of uncertainties and adversities during task execution, which can impair its ability to carry out tasks effectively and cause its performance to degrade. Such situations can be mitigated or averted by timely detection and intervention (e.g., by a remote human supervisor taking over control in teleoperation mode). Inspired by patient triaging systems in hospitals, we introduce the framework of "robot vitals" for estimating overall "robot health". A robot's vitals are a set of indicators that estimate the extent of performance degradation faced by a robot at a given point in time. Robot health is a metric that combines robot vitals into a single scalar value estimate of performance degradation. Experiments, both in simulation and on a real mobile robot, demonstrate that the proposed robot vitals and robot health can be used effectively to estimate robot performance degradation during runtime.
Do Not Take It for Granted: Comparing Open-Source Libraries for Software Development Effort Estimation
Venue: arXiv cs.SE 2022
Year: 2022

In the past two decades, several Machine Learning (ML) libraries have become freely available. Many studies have used such libraries to carry out empirical investigations on predictive Software Engineering (SE) tasks. However, the differences stemming from using one library over another have been overlooked, implicitly assuming that using any of these libraries would provide the user with the same or very similar results. This paper aims at raising awareness of the differences incurred when using different ML libraries for software development effort estimation (SEE), one of most widely studied SE prediction tasks. To this end, we investigate 4 deterministic machine learners as provided by 3 of the most popular ML open-source libraries written in different languages (namely, Scikit-Learn, Caret and Weka). We carry out a thorough empirical study comparing the performance of the machine learners on 5 SEE datasets in the two most common SEE scenarios (i.e., out-of-the-box-ml and tuned-ml) as well as an in-depth analysis of the documentation and code of their APIs. The results of our study reveal that the predictions provided by the 3 libraries differ in 95% of the cases on average across a total of 105 cases studied. These differences are significantly large in most cases and yield misestimations of up to approx. 3,000 hours per project. Moreover, our API analysis reveals that these libraries provide the user with different levels of control on the parameters one can manipulate, and a lack of clarity and consistency, overall, which might mislead users. Our findings highlight that the ML library is an important design choice for SEE studies, which can lead to a difference in performance. However, such a difference is under-documented. We conclude by highlighting open-challenges with suggestions for the developers of libraries as well as for the researchers and practitioners using them.
Providing slowdown information to improve selfish routing
Venue: arXiv cs.GT 2022
Year: 2022

Recent research in the social sciences has identified situations in which small changes in the way that information is provided to consumers can have large aggregate effects on behavior. This has been promoted in popular media in areas of public health and wellness, but its application to other areas has not been broadly studied. This paper presents a simple model which expresses the effect of providing commuters with carefully-curated information regarding aggregate traffic "slowdowns" on the various roads in a transportation network. Much of the work on providing information to commuters focuses specifically on travel-time information. However, the model in the present paper allows a system planner to provide slowdown information as well; that is, commuters are additionally told how much slower each route is as compared to its uncongested state. We show that providing this additional information can improve equilibrium routing efficiency when compared to the case when commuters are only given information about travel time, but that these improvements in congestion are not universal. That is, transportation networks exist on which any provision of slowdown information can harm equilibrium congestion. In addition, this paper illuminates a deep connection between the effects of commuter slowdown-sensitivity and the study of marginal-cost pricing and altruism in congestion games.
Netcast: Low-Power Edge Computing with WDM-defined Optical Neural Networks
Venue: arXiv cs.ET 2022
Year: 2022

This paper analyzes the performance and energy efficiency of Netcast, a recently proposed optical neural-network architecture designed for edge computing. Netcast performs deep neural network inference by dividing the computational task into two steps, which are split between the server and (edge) client: (1) the server employs a wavelength-multiplexed modulator array to encode the network's weights onto an optical signal in an analog time-frequency basis, and (2) the client obtains the desired matrix-vector product through modulation and time-integrated detection. The simultaneous use of wavelength multiplexing, broadband modulation, and integration detection allows large neural networks to be run at the client by effectively pushing the energy and memory requirements back to the server. The performance and energy efficiency are fundamentally limited by crosstalk and detector noise, respectively. We derive analytic expressions for these limits and perform numerical simulations to verify these bounds.
Object-Level Targeted Selection via Deep Template Matching
Venue: arXiv cs.CV 2022
Year: 2022

Retrieving images with objects that are semantically similar to objects of interest (OOI) in a query image has many practical use cases. A few examples include fixing failures like false negatives/positives of a learned model or mitigating class imbalance in a dataset. The targeted selection task requires finding the relevant data from a large-scale pool of unlabeled data. Manual mining at this scale is infeasible. Further, the OOI are often small and occupy less than 1% of image area, are occluded, and co-exist with many semantically different objects in cluttered scenes. Existing semantic image retrieval methods often focus on mining for larger sized geographical landmarks, and/or require extra labeled data, such as images/image-pairs with similar objects, for mining images with generic objects. We propose a fast and robust template matching algorithm in the DNN feature space, that retrieves semantically similar images at the object-level from a large unlabeled pool of data. We project the region(s) around the OOI in the query image to the DNN feature space for use as the template. This enables our method to focus on the semantics of the OOI without requiring extra labeled data. In the context of autonomous driving, we evaluate our system for targeted selection by using failure cases of object detectors as OOI. We demonstrate its efficacy on a large unlabeled dataset with 2.2M images and show high recall in mining for images with small-sized OOI. We compare our method against a well-known semantic image retrieval method, which also does not require extra labeled data. Lastly, we show that our method is flexible and retrieves images with one or more semantically different co-occurring OOI seamlessly.
Aesthetic Attribute Assessment of Images Numerically on Mixed Multi-attribute Datasets
Venue: arXiv cs.CV 2022
Year: 2022

With the continuous development of social software and multimedia technology, images have become a kind of important carrier for spreading information and socializing. How to evaluate an image comprehensively has become the focus of recent researches. The traditional image aesthetic assessment methods often adopt single numerical overall assessment scores, which has certain subjectivity and can no longer meet the higher aesthetic requirements. In this paper, we construct an new image attribute dataset called aesthetic mixed dataset with attributes(AMD-A) and design external attribute features for fusion. Besides, we propose a efficient method for image aesthetic attribute assessment on mixed multi-attribute dataset and construct a multitasking network architecture by using the EfficientNet-B0 as the backbone network. Our model can achieve aesthetic classification, overall scoring and attribute scoring. In each sub-network, we improve the feature extraction through ECA channel attention module. As for the final overall scoring, we adopt the idea of the teacher-student network and use the classification sub-network to guide the aesthetic overall fine-grain regression. Experimental results, using the MindSpore, show that our proposed method can effectively improve the performance of the aesthetic overall and attribute assessment.
Sedentary Behavior Estimation with Hip-worn Accelerometer Data: Segmentation, Classification and Thresholding
Venue: arXiv cs.LG 2022
Year: 2022

Cohort studies are increasingly using accelerometers for physical activity and sedentary behavior estimation. These devices tend to be less error-prone than self-report, can capture activity throughout the day, and are economical. However, previous methods for estimating sedentary behavior based on hip-worn data are often invalid or suboptimal under free-living situations and subject-to-subject variation. In this paper, we propose a local Markov switching model that takes this situation into account, and introduce a general procedure for posture classification and sedentary behavior analysis that fits the model naturally. Our method features changepoint detection methods in time series and also a two stage classification step that labels data into 3 classes(sitting, standing, stepping). Through a rigorous training-testing paradigm, we showed that our approach achieves > 80% accuracy. In addition, our method is robust and easy to interpret.
Latents2Segments: Disentangling the Latent Space of Generative Models for Semantic Segmentation of Face Images
Venue: arXiv cs.CV 2022
Year: 2022

With the advent of an increasing number of Augmented and Virtual Reality applications that aim to perform meaningful and controlled style edits on images of human faces, the impetus for the task of parsing face images to produce accurate and fine-grained semantic segmentation maps is more than ever before. Few State of the Art (SOTA) methods which solve this problem, do so by incorporating priors with respect to facial structure or other face attributes such as expression and pose in their deep classifier architecture. Our endeavour in this work is to do away with the priors and complex pre-processing operations required by SOTA multi-class face segmentation models by reframing this operation as a downstream task post infusion of disentanglement with respect to facial semantic regions of interest (ROIs) in the latent space of a Generative Autoencoder model. We present results for our model's performance on the CelebAMask-HQ and HELEN datasets. The encoded latent space of our model achieves significantly higher disentanglement with respect to semantic ROIs than that of other SOTA works. Moreover, it achieves a 13% faster inference rate and comparable accuracy with respect to the publicly available SOTA for the downstream task of semantic segmentation of face images.
Vision-based Uneven BEV Representation Learning with Polar Rasterization and Surface Estimation
Venue: arXiv cs.CV 2022
Year: 2022

In this work, we propose PolarBEV for vision-based uneven BEV representation learning. To adapt to the foreshortening effect of camera imaging, we rasterize the BEV space both angularly and radially, and introduce polar embedding decomposition to model the associations among polar grids. Polar grids are rearranged to an array-like regular representation for efficient processing. Besides, to determine the 2D-to-3D correspondence, we iteratively update the BEV surface based on a hypothetical plane, and adopt height-based feature transformation. PolarBEV keeps real-time inference speed on a single 2080Ti GPU, and outperforms other methods for both BEV semantic segmentation and BEV instance segmentation. Thorough ablations are presented to validate the design. The code will be released at \url{https://github.com/SuperZ-Liu/PolarBEV}.
StyleFlow For Content-Fixed Image to Image Translation
Venue: arXiv cs.CV 2022
Year: 2022

Image-to-image (I2I) translation is a challenging topic in computer vision. We divide this problem into three tasks: strongly constrained translation, normally constrained translation, and weakly constrained translation. The constraint here indicates the extent to which the content or semantic information in the original image is preserved. Although previous approaches have achieved good performance in weakly constrained tasks, they failed to fully preserve the content in both strongly and normally constrained tasks, including photo-realism synthesis, style transfer, and colorization, etc. To achieve content-preserving transfer in strongly constrained and normally constrained tasks, we propose StyleFlow, a new I2I translation model that consists of normalizing flows and a novel Style-Aware Normalization (SAN) module. With the invertible network structure, StyleFlow first projects input images into deep feature space in the forward pass, while the backward pass utilizes the SAN module to perform content-fixed feature transformation and then projects back to image space. Our model supports both image-guided translation and multi-modal synthesis. We evaluate our model in several I2I translation benchmarks, and the results show that the proposed model has advantages over previous methods in both strongly constrained and normally constrained tasks.
Additive complementary dual codes over $\F_4$
Venue: arXiv cs.IT 2022
Year: 2022

A linear code is linear complementary dual (LCD) if it meets its dual trivially. LCD codes have been a hot topic recently due to Boolean masking application in the security of embarked electronics (Carlet and Guilley, 2014). Additive codes over $\F_4$ are $\F_4$-codes that are stable by codeword addition but not necessarily by scalar multiplication. An additive code over $\F_4$ is additive complementary dual (ACD) if it meets its dual trivially. The aim of this research is to study such codes which meet their dual trivially. All the techniques and problems used to study LCD codes are potentially relevant to ACD codes. Interesting constructions of ACD codes from binary codes are given with respect to the trace Hermitian and trace Euclidean inner product. The former product is relevant to quantum codes.
Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing
Venue: arXiv cs.DB 2022
Year: 2022

Big data processing at the production scale presents a highly complex environment for resource optimization (RO), a problem crucial for meeting performance goals and budgetary constraints of analytical users. The RO problem is challenging because it involves a set of decisions (the partition count, placement of parallel instances on machines, and resource allocation to each instance), requires multi-objective optimization (MOO), and is compounded by the scale and complexity of big data systems while having to meet stringent time constraints for scheduling. This paper presents a MaxCompute-based integrated system to support multi-objective resource optimization via fine-grained instance-level modeling and optimization. We propose a new architecture that breaks RO into a series of simpler problems, new fine-grained predictive models, and novel optimization methods that exploit these models to make effective instance-level recommendations in a hierarchical MOO framework. Evaluation using production workloads shows that our new RO system could reduce 37-72% latency and 43-78% cost at the same time, compared to the current optimizer and scheduler, while running in 0.02-0.23s.
A Stochastic Game Approach to Masking Fault-Tolerance: Bisimulation and Quantification
Venue: arXiv cs.LO 2022
Year: 2022

We introduce a formal notion of masking fault-tolerance between probabilistic transition systems based on a variant of probabilistic bisimulation (named masking simulation). We also provide the corresponding probabilistic game characterization. Even though these games could be infinite, we propose a symbolic way of representing them, such that it can be decided in polynomial time if there is a masking simulation between two probabilistic transition systems. We use this notion of masking to quantify the level of masking fault-tolerance exhibited by almost-sure failing systems, i.e., those systems that eventually fail with probability 1. The level of masking fault-tolerance of almost-sure failing systems can be calculated by solving a collection of functional equations. We produce this metric in a setting in which the minimizing player behaves in a strong fair way (mimicking the idea of fair environments), and limit our study to memoryless strategies due to the infinite nature of the game. We implemented these ideas in a prototype tool, and performed an experimental evaluation.