Miracle-dz/UniFAR-FTUs huggingface dataset:
PoseFix: Model-Agnostic General Human Pose Refinement Network

Multi-person pose estimation from a 2D image is an essential technique for human behavior understanding. In this paper, we propose a human pose refinement network that estimates a refined pose from a tuple of an input image and input pose. The pose refinement was performed mainly through an end-to-end trainable multi-stage architecture in previous methods. However, they are highly dependent on pose estimation models and require careful model design. By contrast, we propose a model-agnostic pose refinement method. According to a recent study, state-of-the-art 2D human pose estimation methods have similar error distributions. We use this error statistics as prior information to generate synthetic poses and use the synthesized poses to train our model. In the testing stage, pose estimation results of any other methods can be input to the proposed method. Moreover, the proposed model does not require code or knowledge about other methods, which allows it to be easily used in the post-processing step. We show that the proposed approach achieves better performance than the conventional multi-stage refinement models and consistently improves the performance of various state-of-the-art pose estimation methods on the commonly used benchmark. The code is available in \footnote{\url{https://github.com/mks0601/PoseFix_RELEASE}}.
Optimal Scheduling Strategy for Virtual Power Plants with Interruptible Loads

Increased penetration levels of renewable generation in power systems have reduced environmental emissions but have simultaneously increased generation supply volatility. A virtual power plant (VPP) using smart grid technology is able integrate renewable and conventional generators with controllable or interruptible loads into a single entity that can provide continuously-dependable energy supply while maximizing renewable energy utilization. In a day-ahead market, the VPP operator must formulate a bidding and scheduling strategy that would maximize its profits in the presence of uncertainties in available renewable resource and imbalance penalty prices. A robust optimization approach is proposed to optimize VPP bidding and scheduling, that takes advantage of interruptible loads for technical and economic flexibility. Results of test cases show its effectiveness and superiority over a fuzzy optimization approach.
Resource Allocation in Beam Hopping Communication System

Beam-Hopping provides satellite systems with an additional dimension of agility making it possible to adjust the allocation of the scarce satellite spectral and power resources to the demand which varies in space and geography as well as with time. Provided such flexibility, it has a great potential in enhancing communication satellite capacity. An effective resource allocation mechanism which can meets future demands flexibly is urgently needed in order to achieve the best user communication quality and to provide more users with more satisfactory service. The existing beam steering mainly focus on covering the beam position which largest demand the spectrum resources and do not fully utilize the advantages brought by the flexibility in beam range. In this paper, we propose a beam resource management algorithm that adjusts the beam size according to the service distribution, which can make more users accessible to make full use of beam bandwidth resources by covering more beam positions. Simulation results show that the proposed algorithm can effectively reduce packet loss rate, improve the quality of service (QoS)” and guarantee service priority,
Comixify: Transform video into a comics

In this paper, we propose a solution to transform a video into a comics. We approach this task using a neural style algorithm based on Generative Adversarial Networks (GANs). Several recent works in the field of Neural Style Transfer showed that producing an image in the style of another image is feasible. In this paper, we build up on these works and extend the existing set of style transfer use cases with a working application of video comixification. To that end, we train an end-to-end solution that transforms input video into a comics in two stages. In the first stage, we propose a state-of-the-art keyframes extraction algorithm that selects a subset of frames from the video to provide the most comprehensive video context and we filter those frames using image aesthetic estimation engine. In the second stage, the style of selected keyframes is transferred into a comics. To provide the most aesthetically compelling results, we selected the most state-of-the art style transfer solution and based on that implement our own ComixGAN framework. The final contribution of our work is a Web-based working application of video comixification available at this http URL.
Towards Spotting and Recognition of Handwritten Words in Indic Scripts

Handwriting recognition (HWR) in Indic scripts is a challenging problem due to the inherent subtleties in the scripts, cursive nature of the handwriting and similar shape of the characters. Lack of publicly available handwriting datasets in Indic scripts has affected the development of handwritten word recognizers, and made direct comparisons across different methods an impossible task in the field. In this paper, we propose a framework for annotating large scale of handwritten word images with ease and speed. We also release a new handwritten word dataset for Telugu, which is collected and annotated using the proposed framework. We also benchmark major Indic scripts such as Devanagari, Bangla and Telugu for the tasks of word spotting and handwriting recognition using state of the art deep neural architectures. Finally, we evaluate the proposed pipeline on RoyDB, a public dataset, and achieve significant reduction in error rates.
A Genetic Algorithm for the Winner Determination Problem in Combinatorial Auctions

Auctions are a very popular way of allocating multiple items. There are three different auction types, such as sequential auctions, parallel auctions, and combinatorial auctions. This study focuses on combinatorial auctions. Combinatorial auctions allow bidders to bid on a collection of items rather than a single item. This results in more efficient allocations than traditional auctions. The problem is determining the winners of all bids in a way that maximizes the auctioneer’s profit. Some instances of the problem may be solvable in polynomial time with a few items and bidders. However, with excessive numbers of items and bidders, the problem becomes NP-complete. In this paper, a genetic algorithm is proposed and compared with a random search algorithm on various sized datasets. The results for experiments indicate that the proposed genetic algorithm performs better than random search as the size of the problem increases.
Real-Time Referring Expression Comprehension by Single-Stage Grounding Network

In this paper, we propose a novel end-to-end model, namely Single-Stage Grounding network (SSG), to localize the referent given a referring expression within an image. Different from previous multi-stage models which rely on object proposals or detected regions, our proposed model aims to comprehend a referring expression through one single stage without resorting to region proposals as well as the subsequent region-wise feature extraction. Specifically, a multimodal interactor is proposed to summarize the local region features regarding the referring expression attentively. Subsequently, a grounder is proposed to localize the referring expression within the given image directly. For further improving the localization accuracy, a guided attention mechanism is proposed to enforce the grounder to focus on the central region of the referent. Moreover, by exploiting and predicting visual attribute information, the grounder can further distinguish the referent objects within an image and thereby improve the model performance. Experiments on RefCOCO, RefCOCO+, and RefCOCOg datasets demonstrate that our proposed SSG without relying on any region proposals can achieve comparable performance with other advanced models. Furthermore, our SSG outperforms the previous models and achieves the state-of-art performance on the ReferItGame dataset. More importantly, our SSG is time efficient and can ground a referring expression in a 416*416 image from the RefCOCO dataset in 25ms (40 referents per second) on average with a Nvidia Tesla P40, accomplishing more than 9* speedups over the existing multi-stage models.
Data Driven Optimization of Charging Station Placement for EV Free Floating Car Sharing

Free Floating Car Sharing (FFCS) is a transport paradigm where customers are free to rent and drop cars of a fleet within city limits. In this work we consider the design of a FFCS system based on Electric Vehicles (EVs), We face the problem of finding the minimum number of charging stations and their placement, given the battery constraints of electric cars, the cost of installing the charging network, and the time-varying car usage patterns of customers. Differently from other studies, we base our solution on actual rentals collected from traditional combustion FFCS systems currently in use in two cities. We use about 450 000 actual rentals to characterize the system utilization. We propose a user-behavior model and system policies for the charging events. Then we evaluate via accurate trace driven simulations the performance with different charging station placement policies. We first present greedy solutions, and then perform a local optimization with a meta-heuristic that 1) guarantee system operativeness, i.e., car batteries never get depleted, and 2) minimize users' discomfort, i.e., users are only seldom forced to drop cars in a far-away charging station. Results show that it is possible to guarantee service continuity by installing charging stations in just 6 % of city areas, while 15% of equipped zones guarantee limited impact on users' discomfort.
Offline Signature Verification Via Structural Methods: Graph Edit Distance and Inkball Models

For handwritten signature verification, signature images are typically represented with fixed-sized feature vectors capturing local and global properties of the handwriting. Graph-based representations offer a promising alternative, as they are flexible in size and model the global structure of the handwriting. However, they are only rarely used for signature verification, which may be due to the high computational complexity involved when matching two graphs. In this paper, we take a closer look at two recently presented structural methods for handwriting analysis, for which efficient matching methods are available: keypoint graphs with approximate graph edit distance and inkball models. Inkball models, in particular, have never been used for signature verification before. We investigate both approaches individually and propose a combined verification system, which demonstrates an excellent performance on the MCYT and GPDS benchmark data sets when compared with the state of the art.
Cloud-Assisted Distributed Control System Architecture for Platooning

This paper presents a functional architecture for controlling and managing platoons of vehicles assisted by a cloud computing platform for passenger vehicles in mixed traffic scenarios. The architecture modules are distributed in the three separate logical layers suggested by the on-going CARMA (Cloud-Assisted Real-time Methods for Autonomy) research project which aims to develop and test cooperative automated driving technologies and distributed control systems. The architecture also exploits the potential of the CARMA platform both in terms of computing and data sharing.
Calmodulinopathy: Functional Effects of CALM Mutations and Their Relationship With Clinical Phenotypes

In spite of the widespread role of calmodulin (CaM) in cellular signaling, CaM mutations lead specifically to cardiac manifestations, characterized by remarkable electrical instability and a high incidence of sudden death at young age. Penetrance of the mutations is surprisingly high, thus postulating a high degree of functional dominance. According to the clinical patterns, arrhythmogenesis in CaM mutations can be attributed, in the majority of cases, to either prolonged repolarization (as in long-QT syndrome, LQTS phenotype), or to instability of the intracellular Ca2+ store (as in catecholamine-induced tachycardias, CPVT phenotype). This review discusses how mutations affect CaM signaling function and how this may relate to the distinct arrhythmia phenotypes/mechanisms observed in patients; this involves mechanistic interpretation of negative dominance and mutation-specific CaM-target interactions. Knowledge of the mechanisms involved may allow critical approach to clinical manifestations and aid in the development of therapeutic strategies for “calmodulinopathies,” a recently identified nosological entity.
Sensorimotor Communication for Humans and Robots: Improving Interactive Skills by Sending Coordination Signals

During joint actions, humans continuously exchange coordination signals and use nonverbal, sensorimotor forms of communication. Here we discuss a specific example of sensorimotor communication—“signaling”—which consists in the intentional modification of one’s own action plan (e.g., a plan for reaching a glass of wine) to make it more predictable or discriminable from alternative action plans that are contextually plausible (e.g., a plan for reaching another glass on the same table). We first review the existing evidence on signaling in human-human interactions, discussing under which conditions humans use signaling. Successively, we distill these insights into a computational theory of signaling during online interactions. Central to our approach are the following ideas: 1) signaling endows pragmatic plans with communicative goals; 2) signaling can be understood within a cost-benefit scheme, balancing the costs for the signaling agent against its benefits for interaction success; and 3) signaling may be part of an interactive strategy that optimizes success when joint goals are uncertain. Finally, we exemplify the benefits of signaling in a series of simulations and discuss how endowing robots with signaling abilities can increase the quality of human-robot interactions by making their behavior more predictable and “legible” for humans.
An Adjustable Dalteparin Sodium Dose Regimen for the Prevention of Clotting in the Extracorporeal Circuit in Hemodialysis: A Clinical Trial of Safety and Efficacy (the PARROT Study)

Background: Dalteparin sodium, a low-molecular-weight heparin, is indicated for prevention of clotting in the extracorporeal circuit during hemodialysis (HD). Product labeling recommends a fixed single-bolus dose of 5000 international units (IU) for HD sessions lasting up to 4 hours, but adjustable dosing may be beneficial in clinical practice. Objective: The aim of the PARROT study was to investigate the safety and efficacy of an adjustable dose of dalteparin in patients with end-stage renal disease requiring 3 to 4 HD sessions per week. Design: A 7-week, open-label, multicenter study with a single treatment arm, conducted between October 2013 and March 2016. Setting: Ten sites in Canada. Patients: A total of 152 patients with end-stage renal disease requiring 3 to 4 HD sessions per week. Measurements: The primary outcome was the proportion of HD sessions completed without premature termination due to inadequate anticoagulation. Methods: All participants initially received a dose of 5000 IU dalteparin, which could be adjusted at subsequent HD sessions when clinically indicated, by increment or decrement of 500 or 1000 IU, with no specified dose limits. Results: Patients were followed for 256 patient-months. Nearly all (99.9%; 95% confidence interval [CI]: 99.7-100) evaluable HD sessions were completed without premature clotting. Dose was adjusted for more than half (52.3%) of participants, mostly owing to clotting or access compression time >10 minutes. Median dalteparin dose was 5000 IU (range: 500-13 000 IU). There were no major bleeds, and minor bleeding was reported in 2.3% of all HD sessions. There was no evidence of bioaccumulation. Limitations: This short-term study, with a single treatment arm, was designed to optimize dalteparin dose using a flexible dosing schedule; it was not designed to specifically evaluate dalteparin dose minimization, provide a direct comparison of dalteparin versus unfractionated heparin, or provide information on long-term safety for flexible dalteparin dosing. Patients were excluded if they were at high risk of bleeding, including those on anticoagulants and those on antiplatelet agents other than aspirin <100 mg/d. Conclusions: Overall, an adjustable dalteparin sodium dose regimen allowed safe completion of HD, with clinical benefits over fixed dosing. Trial Registration: ClinicalTrials.gov NCT01879618, registered June 13, 2013.
2-to- $M$  Coordinated Multipoint-Based Uplink Transmission in Ultra-Dense Cellular Networks

The ongoing densification of small cells, i.e., ultimately evolving toward ultra-dense networks (UDNs), yields an unprecedented paradigm shift in network design by bringing the base stations (BSs) and users to approximate the spatial scale and number magnitude. To address the super-frequent handovers of mobile users in such UDNs, the coordinated multipoint (CoMP) then becomes a feasible solution by effectively improving communication reliability. In this paper, we consider CoMP-based uplink transmission in heterogeneous UDNs. As our first step, a CoMP uplink transmission scheme is proposed for each user to transmit to multiple (2 to $M$ ) cooperating BSs with channel inversion power control in a multi-channel scenario. The performance of the proposed uplink CoMP is analyzed in the context of UDNs using stochastic geometry, in terms of outage probability and ergodic capacity, considering the effects of BS intensity, power control, number of cooperating nodes, and network tiers, first in a single-tier network, and later extended to a $K$ -tier heterogeneous network. Performance evaluation through extensive simulations is conducted and it is shown that the simulation results fit well with the analytical ones. The performance of the proposed scheme is then compared with that of the non-CoMP scheme, and it is found that the proposed scheme can significantly improve outage probability and ergodic capacity of mobile users.
Reformed Residual Network With Sparse Feedbacks for 3D Reconstruction From a Single Image

Deep neural networks are difficult to train due to the large number of unknown parameters. To increase the trainable performance, we present a novel network model with moderate depth for three-dimensional reconstruction. The proposed network model, called SFResNet, only has eight layers, and sparse feedbacks were added in the middle and last layers, which is mainly used to add the constraints and improve the stability of the network model. In addition, a joint strategy is proposed to reduce the artificial Mosaic trace at the seam of the patches; hence, SFResNet can also evaluate an input image of any size. Visually pleasing output results can be produced with a reconstructed shape and normal surface. The experimental results show the effectiveness of the proposed method.
Feature Denoising for Improving Adversarial Robustness

Adversarial attacks to image classification systems present challenges to convolutional networks and opportunities for understanding them. This study suggests that adversarial perturbations on images lead to noise in the features constructed by these networks. Motivated by this observation, we develop new network architectures that increase adversarial robustness by performing feature denoising. Specifically, our networks contain blocks that denoise the features using non-local means or other filters; the entire networks are trained end-to-end. When combined with adversarial training, our feature denoising networks substantially improve the state-of-the-art in adversarial robustness in both white-box and black-box attack settings. On ImageNet, under 10-iteration PGD white-box attacks where prior art has 27.9% accuracy, our method achieves 55.7%; even under extreme 2000-iteration PGD white-box attacks, our method secures 42.6% accuracy. Our method was ranked first in Competition on Adversarial Attacks and Defenses (CAAD) 2018 --- it achieved 50.6% classification accuracy on a secret, ImageNet-like test dataset against 48 unknown attackers, surpassing the runner-up approach by ~10%. Code is available at https://github.com/facebookresearch/ImageNet-Adversarial-Training.
Differential Interaction of Platelet-Derived Extracellular Vesicles With Circulating Immune Cells: Roles of TAM Receptors, CD11b, and Phosphatidylserine

Secretion and exchange of biomolecules by extracellular vesicles (EVs) are crucial in intercellular communication and enable cells to adapt to alterations in their microenvironment. EVs are involved in a variety of cellular processes under physiological conditions as well as in pathological settings. In particular, they exert profound effects on the innate immune system, and thereby are also capable of modulating adaptive immunity. The mechanisms underlying their interaction with their recipient cells, particularly their preferential association with monocytes and granulocytes in the circulation, however, remain to be further clarified. Surface molecules exposed on EVs are likely to mediate immune recognition and EV uptake by their recipient cells. Here, we investigated the involvement of Tyro3, Axl, and Mer (TAM) tyrosine kinase receptors and of integrin CD11b in the binding of platelet-derived EVs, constituting the large majority of circulating EVs, to immune cells in the circulation. Flow cytometry and Western Blotting demonstrated a differential expression of TAM receptors and CD11b on monocytes, granulocytes, and lymphocytes, as well as on monocyte subsets. Of the TAM receptors, only Axl and Mer were detected at low levels on monocytes and granulocytes, but not on lymphocytes. Likewise, CD11b was present on circulating monocytes and granulocytes, but remained undetectable on lymphocytes. Differentiation of monocytes into classical, intermediate, and non-classical monocyte subsets revealed distinct expression patterns of Mer and activated CD11b. Co-incubation of isolated monocytes and granulocytes with platelet-derived EVs showed that the binding of EVs to immune cells was dependent on Ca++. Our data do not support a particular role for TAM receptors or for activated CD11b in the association of platelet-derived EVs with monocytes and granulocytes in the circulation, as anti-TAM antibodies did not interfere with EV binding to isolated immune cells, as binding was not dependent on the presence of TIM4 acting synergistically with TAM receptors, and as neither low levels of Gas6, required as a linker between phosphatidylserine (PS) on the EV surface and TAM receptors on immune cells, nor masking of PS on the EV surface did interfere with EV binding.
Attention-Guided Unified Network for Panoptic Segmentation

This paper studies panoptic segmentation, a recently proposed task which segments foreground (FG) objects at the instance level as well as background (BG) contents at the semantic level. Existing methods mostly dealt with these two problems separately, but in this paper, we reveal the underlying relationship between them, in particular, FG objects provide complementary cues to assist BG understanding. Our approach, named the Attention-guided Unified Network (AUNet), is a unified framework with two branches for FG and BG segmentation simultaneously. Two sources of attentions are added to the BG branch, namely, RPN and FG segmentation mask to provide object-level and pixel-level attentions, respectively. Our approach is generalized to different backbones with consistent accuracy gain in both FG and BG segmentation, and also sets new state-of-the-arts both in the MS-COCO (46.5% PQ) and Cityscapes (59.0% PQ) benchmarks.