OpenRaiser/Intern-Atlas huggingface dataset:
MoDL-QSM: Model-based Deep Learning for Quantitative Susceptibility Mapping
Venue: arXiv cs.CV 2021
Year: 2021

Quantitative susceptibility mapping (QSM) has demonstrated great potential in quantifying tissue susceptibility in various brain diseases. However, the intrinsic ill-posed inverse problem relating the tissue phase to the underlying susceptibility distribution affects the accuracy for quantifying tissue susceptibility. Recently, deep learning has shown promising results to improve accuracy by reducing the streaking artifacts. However, there exists a mismatch between the observed phase and the theoretical forward phase estimated by the susceptibility label. In this study, we proposed a model-based deep learning architecture that followed the STI (susceptibility tensor imaging) physical model, referred to as MoDL-QSM. Specifically, MoDL-QSM accounts for the relationship between STI-derived phase contrast induced by the susceptibility tensor terms (ki13,ki23,ki33) and the acquired single-orientation phase. The convolution neural networks are embedded into the physical model to learn a regularization term containing prior information. ki33 and phase induced by ki13 and ki23 terms were used as the labels for network training. Quantitative evaluation metrics (RSME, SSIM, and HFEN) were compared with recently developed deep learning QSM methods. The results showed that MoDL-QSM achieved superior performance, demonstrating its potential for future applications.
Introducing the Unitychain Structure: A novel blockchain-like structure that enables greater parallel processing, security, and performance for networks that leverage distributed key generation and classical consensus protocols
Venue: arXiv cs.CR 2021
Year: 2021

A Unitychain is a novel blockchain-like structure that drastically improves transaction scalability and security while maintaining ongoing network performance, even if participating nodes are required to perform a new Distributed Key Generation procedure for security purposes. The Unitychain structure, furthermore, enables greater parallel processing by the assignment of different network node configurations for various database and compute ranges into multiple strands of blockchains that intersect, creating a multi-helix structure, which we call a Unitychain. This thereby enables the network to further bifurcate the roles of nodes into arbitrary yet deterministic network responsibilities in order to maximize the global compute potential.
All-Day Object Tracking for Unmanned Aerial Vehicle
Venue: arXiv cs.CV 2021
Year: 2021

Visual object tracking, which is representing a major interest in image processing field, has facilitated numerous real world applications. Among them, equipping unmanned aerial vehicle (UAV) with real time robust visual trackers for all day aerial maneuver, is currently attracting incremental attention and has remarkably broadened the scope of applications of object tracking. However, prior tracking methods have merely focused on robust tracking in the well-illuminated scenes, while ignoring trackers' capabilities to be deployed in the dark. In darkness, the conditions can be more complex and harsh, easily posing inferior robust tracking or even tracking failure. To this end, this work proposed a novel discriminative correlation filter based tracker with illumination adaptive and anti dark capability, namely ADTrack. ADTrack firstly exploits image illuminance information to enable adaptability of the model to the given light condition. Then, by virtue of an efficient and effective image enhancer, ADTrack carries out image pretreatment, where a target aware mask is generated. Benefiting from the mask, ADTrack aims to solve a dual regression problem where dual filters, i.e., the context filter and target focused filter, are trained with mutual constraint. Thus ADTrack is able to maintain continuously favorable performance in all-day conditions. Besides, this work also constructed one UAV nighttime tracking benchmark UAVDark135, comprising of more than 125k manually annotated frames, which is also very first UAV nighttime tracking benchmark. Exhaustive experiments are extended on authoritative daytime benchmarks, i.e., UAV123 10fps, DTB70, and the newly built dark benchmark UAVDark135, which have validated the superiority of ADTrack in both bright and dark conditions on a single CPU.
Segmenting Transparent Object in the Wild with Transformer
Venue: arXiv cs.CV 2021
Year: 2021

This work presents a new fine-grained transparent object segmentation dataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale transparent object segmentation dataset. Unlike Trans10K-v1 that only has two limited categories, our new dataset has several appealing benefits. (1) It has 11 fine-grained categories of transparent objects, commonly occurring in the human domestic environment, making it more practical for real-world application. (2) Trans10K-v2 brings more challenges for the current advanced segmentation methods than its former version. Furthermore, a novel transformer-based segmentation pipeline termed Trans2Seg is proposed. Firstly, the transformer encoder of Trans2Seg provides the global receptive field in contrast to CNN's local receptive field, which shows excellent advantages over pure CNN architectures. Secondly, by formulating semantic segmentation as a problem of dictionary look-up, we design a set of learnable prototypes as the query of Trans2Seg's transformer decoder, where each prototype learns the statistics of one category in the whole dataset. We benchmark more than 20 recent semantic segmentation methods, demonstrating that Trans2Seg significantly outperforms all the CNN-based methods, showing the proposed algorithm's potential ability to solve transparent object segmentation.
Progressive Co-Attention Network for Fine-grained Visual Classification
Venue: arXiv cs.CV 2021
Year: 2021

Fine-grained visual classification aims to recognize images belonging to multiple sub-categories within a same category. It is a challenging task due to the inherently subtle variations among highly-confused categories. Most existing methods only take an individual image as input, which may limit the ability of models to recognize contrastive clues from different images. In this paper, we propose an effective method called progressive co-attention network (PCA-Net) to tackle this problem. Specifically, we calculate the channel-wise similarity by encouraging interaction between the feature channels within same-category image pairs to capture the common discriminative features. Considering that complementary information is also crucial for recognition, we erase the prominent areas enhanced by the channel interaction to force the network to focus on other discriminative regions. The proposed model has achieved competitive results on three fine-grained visual classification benchmark datasets: CUB-200-2011, Stanford Cars, and FGVC Aircraft.
Orthogonal Least Squares Based Fast Feature Selection for Linear Classification
Venue: arXiv cs.LG 2021
Year: 2021

An Orthogonal Least Squares (OLS) based feature selection method is proposed for both binomial and multinomial classification. The novel Squared Orthogonal Correlation Coefficient (SOCC) is defined based on Error Reduction Ratio (ERR) in OLS and used as the feature ranking criterion. The equivalence between the canonical correlation coefficient, Fisher's criterion, and the sum of the SOCCs is revealed, which unveils the statistical implication of ERR in OLS for the first time. It is also shown that the OLS based feature selection method has speed advantages when applied for greedy search. The proposed method is comprehensively compared with the mutual information based feature selection methods and the embedded methods using both synthetic and real world datasets. The results show that the proposed method is always in the top 5 among the 12 candidate methods. Besides, the proposed method can be directly applied to continuous features without discretisation, which is another significant advantage over mutual information based methods.
A Fast Optimal Double Row Legalization Algorithm
Venue: arXiv cs.DS 2021
Year: 2021

In Placement Legalization, it is often assumed that (almost) all standard cells possess the same height and can therefore be aligned in cell rows, which can then be treated independently. However, this is no longer true for recent technologies, where a substantial number of cells of double- or even arbitrary multiple-row height is to be expected. Due to interdependencies between the cell placements within several rows, the legalization task becomes considerably harder. In this paper, we show how to optimize quadratic cell movement for pairs of adjacent rows comprising cells of single- as well as double-row height with a fixed left-to-right ordering in time $\mathcal{O}(n\cdot\log(n))$, whereby $n$ denotes the number of cells involved. Opposed to prior works, we thereby do not artificially bound the maximum cell movement and can guarantee to find an optimum solution. Experimental results show an average percental decrease of over $26\%$ in the total quadratic movement when compared to a legalization approach that fixes cells of more than single-row height after Global Placement.
Discovering Multi-Label Actor-Action Association in a Weakly Supervised Setting
Venue: arXiv cs.CV 2021
Year: 2021

Since collecting and annotating data for spatio-temporal action detection is very expensive, there is a need to learn approaches with less supervision. Weakly supervised approaches do not require any bounding box annotations and can be trained only from labels that indicate whether an action occurs in a video clip. Current approaches, however, cannot handle the case when there are multiple persons in a video that perform multiple actions at the same time. In this work, we address this very challenging task for the first time. We propose a baseline based on multi-instance and multi-label learning. Furthermore, we propose a novel approach that uses sets of actions as representation instead of modeling individual action classes. Since computing, the probabilities for the full power set becomes intractable as the number of action classes increases, we assign an action set to each detected person under the constraint that the assignment is consistent with the annotation of the video clip. We evaluate the proposed approach on the challenging AVA dataset where the proposed approach outperforms the MIML baseline and is competitive to fully supervised approaches.
References of References: How Far is the Knowledge Ancestry
Venue: arXiv cs.DL 2021
Year: 2021

Scientometrics studies have extended from direct citations to high-order citations, as simple citation count is found to tell only part of the story regarding scientific impact. This extension is deemed to be beneficial in scenarios like research evaluation, science history modeling, and information retrieval. In contrast to citations of citations (forward citation generations), references of references (backward citation generations) as another side of high-order citations, is relatively less explored. We adopt a series of metrics for measuring the unfolding of backward citations of a focal paper, tracing back to its knowledge ancestors generation by generation. Two sub-fields in Physics are subject to such analysis on a large-scale citation network. Preliminary results show that (1) most papers in our dataset can be traced to their knowledge ancestry; (2) the size distribution of backward citation generations presents a decreasing-and-then-increasing shape; and (3) citations more than one generation away are still relevant to the focal paper, from either a forward or backward perspective; yet, backward citation generations are higher in topic relevance to the paper of interest. Furthermore, the backward citation generations shed lights for literature recommendation, science evaluation, and sociology of science studies.
Learning rich touch representations through cross-modal self-supervision
Venue: arXiv cs.RO 2021
Year: 2021

The sense of touch is fundamental in several manipulation tasks, but rarely used in robot manipulation. In this work we tackle the problem of learning rich touch features from cross-modal self-supervision. We evaluate them identifying objects and their properties in a few-shot classification setting. Two new datasets are introduced using a simulated anthropomorphic robotic hand equipped with tactile sensors on both synthetic and daily life objects. Several self-supervised learning methods are benchmarked on these datasets, by evaluating few-shot classification on unseen objects and poses. Our experiments indicate that cross-modal self-supervision effectively improves touch representation, and in turn has great potential to enhance robot manipulation skills.
DAF:re: A Challenging, Crowd-Sourced, Large-Scale, Long-Tailed Dataset For Anime Character Recognition
Venue: arXiv cs.CV 2021
Year: 2021

In this work we tackle the challenging problem of anime character recognition. Anime, referring to animation produced within Japan and work derived or inspired from it. For this purpose we present DAF:re (DanbooruAnimeFaces:revamped), a large-scale, crowd-sourced, long-tailed dataset with almost 500 K images spread across more than 3000 classes. Additionally, we conduct experiments on DAF:re and similar datasets using a variety of classification models, including CNN based ResNets and self-attention based Vision Transformer (ViT). Our results give new insights into the generalization and transfer learning properties of ViT models on substantially different domain datasets from those used for the upstream pre-training, including the influence of batch and image size in their training. Additionally, we share our dataset, source-code, pre-trained checkpoints and results, as Animesion, the first end-to-end framework for large-scale anime character recognition: https://github.com/arkel23/animesion
Conceptualization and cases of study on cyber operations against the sustainability of the tactical edge
Venue: arXiv cs.NI 2021
Year: 2021

The last decade consolidated the cyberspace as fifth domain of operations, which extends its preliminarily intelligence and information exchange purposes towards enabling complex offensive and defensive operations supported/supportively of parallel kinetic domain actuations. Although there is a plethora of well documented cases on strategic and operational interventions of cyber commands, the cyber tactical military edge is still a challenge, where cyber fires barely integrate to the traditional joint targeting cycle due among others to long planning/development times, asymmetric effects, strict target reachability requirements, or the fast propagation of collateral damage; the latter rapidly deriving on hybrid impacts (political, economic, social, etc.) and evidencing significant socio-technical gaps. In this context, it is expected that tactical clouds disruptively facilitate cyber operations at the edge while exposing the rest of the digital assets of the operation to them. On these grounds, the main purpose of the conducted research is to review and in depth analyze the risks and opportunities of jeopardizing the sustainability of the military tactical clouds at the edge by cyber operations. Along with a 1) comprehensively formulation of the researched problematic, the study 2) formalizes the Tactical Denial of Sustainability (TDoS) concept; 3) introduces the phasing, potential attack surfaces, terrains and impact of TDoS attacks; 4) emphasizes the related human and socio-technical aspects; 5) analyzes the threats/opportunities inherent to their impact on the cloud energy efficiency; 6) reviews their implications at the military cyber thinking for tactical operations; 7) illustrates five extensive CONOPS that facilitate the understanding of the TDoS concept; and given the high novelty of the discussed topics, it 8) paves the way for further research and development actions.
Multi-robot energy autonomy with wind and constrained resources
Venue: arXiv cs.RO 2021
Year: 2021

One aspect of the ever-growing need for long term autonomy of multi-robot systems, is ensuring energy sufficiency. In particular, in scenarios where charging facilities are limited, battery-powered robots need to coordinate to share access. In this work we extend previous results by considering robots that carry out a generic mission while sharing a single charging station, while being affected by air drag and wind fields. Our mission-agnostic framework based on control barrier functions (CBFs) ensures energy sufficiency (i.e., maintaining all robots above a certain voltage threshold) and proper coordination (i.e., ensuring mutually exclusive use of the available charging station). Moreover, we investigate the feasibility requirements of the system in relation to individual robots' properties, as well as air drag and wind effects. We show simulation results that demonstrate the effectiveness of the proposed framework.
Assessing the Benefits of Model Ensembles in Neural Re-Ranking for Passage Retrieval
Venue: arXiv cs.IR 2021
Year: 2021

Our work aimed at experimentally assessing the benefits of model ensembling within the context of neural methods for passage reranking. Starting from relatively standard neural models, we use a previous technique named Fast Geometric Ensembling to generate multiple model instances from particular training schedules, then focusing or attention on different types of approaches for combining the results from the multiple model instances (e.g., averaging the ranking scores, using fusion methods from the IR literature, or using supervised learning-to-rank). Tests with the MS-MARCO dataset show that model ensembling can indeed benefit the ranking quality, particularly with supervised learning-to-rank although also with unsupervised rank aggregation.
Model-based Policy Search for Partially Measurable Systems
Venue: arXiv cs.RO 2021
Year: 2021

In this paper, we propose a Model-Based Reinforcement Learning (MBRL) algorithm for Partially Measurable Systems (PMS), i.e., systems where the state can not be directly measured, but must be estimated through proper state observers. The proposed algorithm, named Monte Carlo Probabilistic Inference for Learning COntrol for Partially Measurable Systems (MC-PILCO4PMS), relies on Gaussian Processes (GPs) to model the system dynamics, and on a Monte Carlo approach to update the policy parameters. W.r.t. previous GP-based MBRL algorithms, MC-PILCO4PMS models explicitly the presence of state observers during policy optimization, allowing to deal PMS. The effectiveness of the proposed algorithm has been tested both in simulation and in two real systems.
Soloist: Generating Mixed-Initiative Tutorials from Existing Guitar Instructional Videos Through Audio Processing
Venue: arXiv cs.HC 2021
Year: 2021

Learning musical instruments using online instructional videos has become increasingly prevalent. However, pre-recorded videos lack the instantaneous feedback and personal tailoring that human tutors provide. In addition, existing video navigations are not optimized for instrument learning, making the learning experience encumbered. Guided by our formative interviews with guitar players and prior literature, we designed Soloist, a mixed-initiative learning framework that automatically generates customizable curriculums from off-the-shelf guitar video lessons. Soloist takes raw videos as input and leverages deep-learning based audio processing to extract musical information. This back-end processing is used to provide an interactive visualization to support effective video navigation and real-time feedback on the user's performance, creating a guided learning experience. We demonstrate the capabilities and specific use-cases of Soloist within the domain of learning electric guitar solos using instructional YouTube videos. A remote user study, conducted to gather feedback from guitar players, shows encouraging results as the users unanimously preferred learning with Soloist over unconverted instructional videos.
Active Hybrid Classification
Venue: arXiv cs.LG 2021
Year: 2021

Hybrid crowd-machine classifiers can achieve superior performance by combining the cost-effectiveness of automatic classification with the accuracy of human judgment. This paper shows how crowd and machines can support each other in tackling classification problems. Specifically, we propose an architecture that orchestrates active learning and crowd classification and combines them in a virtuous cycle. We show that when the pool of items to classify is finite we face learning vs. exploitation trade-off in hybrid classification, as we need to balance crowd tasks optimized for creating a training dataset with tasks optimized for classifying items in the pool. We define the problem, propose a set of heuristics and evaluate the approach on three real-world datasets with different characteristics in terms of machine and crowd classification performance, showing that our active hybrid approach significantly outperforms baselines.
Minimizing Age of Incorrect Information for Unreliable Channel with Power Constraint
Venue: arXiv cs.IT 2021
Year: 2021

Age of Incorrect Information (AoII) is a newly introduced performance metric that considers communication goals. Therefore, comparing with traditional performance metrics and the recently introduced metric - Age of Information (AoI), AoII achieves better performance in many real-life applications. However, the fundamental nature of AoII has been elusive so far. In this paper, we consider the AoII in a system where a transmitter sends updates about a multi-state Markovian source to a remote receiver through an unreliable channel. The communication goal is to minimize AoII subject to a power constraint. We cast the problem into a Constrained Markov Decision Process (CMDP) and prove that the optimal policy is a mixture of two deterministic threshold policies. Afterward, by leveraging the notion of Relative Value Iteration (RVI) and the structural properties of threshold policy, we propose an efficient algorithm to find the threshold policies as well as the mixing coefficient. Lastly, numerical results are laid out to highlight the performance of AoII-optimal policy.
REPOSE: Distributed Top-k Trajectory Similarity Search with Local Reference Point Tries
Venue: arXiv cs.DB 2021
Year: 2021

Trajectory similarity computation is a fundamental component in a variety of real-world applications, such as ridesharing, road planning, and transportation optimization. Recent advances in mobile devices have enabled an unprecedented increase in the amount of available trajectory data such that efficient query processing can no longer be supported by a single machine. As a result, means of performing distributed in-memory trajectory similarity search are called for. However, existing distributed proposals suffer from either computing resource waste or are unable to support the range of similarity measures that are being used. We propose a distributed in-memory management framework called REPOSE for processing top-k trajectory similarity queries on Spark. We develop a reference point trie (RP-Trie) index to organize trajectory data for local search. In addition, we design a novel heterogeneous global partitioning strategy to eliminate load imbalance in distributed settings. We report on extensive experiments with real-world data that offer insight into the performance of the solution, and show that the solution is capable of outperforming the state-of-the-art proposals.