Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Accurate Generative Models of Video: A New Metric & Challenges

Recent advances in deep generative models have lead to remarkable progress in synthesizing high quality images. Following their successful application in image processing and representation learning, an important next step is to consider videos. Learning generative models of video is a much harder task, requiring a model to capture the temporal dynamics of a scene, in addition to the visual presentation of objects. While recent attempts at formulating generative models of video have had some success, current progress is hampered by (1) the lack of qualitative metrics that consider visual quality, temporal coherence, and diversity of samples, and (2) the wide gap between purely synthetic video data sets and challenging real-world data sets in terms of complexity. To this extent we propose Fr\'{e}chet Video Distance (FVD), a new metric for generative models of video, and StarCraft 2 Videos (SCV), a benchmark of game play from custom starcraft 2 scenarios that challenge the current capabilities of generative models of video. We contribute a large-scale human study, which confirms that FVD correlates well with qualitative human judgment of generated videos, and provide initial benchmark results on SCV.
Capture Dense: Markerless Motion Capture Meets Dense Pose Estimation

We present a method to combine markerless motion capture and dense pose feature estimation into a single framework. We demonstrate that dense pose information can help for multiview/single-view motion capture, and multiview motion capture can help the collection of a high-quality dataset for training the dense pose detector. Specifically, we first introduce a novel markerless motion capture method that can take advantage of dense parsing capability provided by the dense pose detector. Thanks to the introduced dense human parsing ability, our method is demonstrated much more efficient, and accurate compared with the available state-of-the-art markerless motion capture approach. Second, we improve the performance of available dense pose detector by using multiview markerless motion capture data. Such dataset is beneficial to dense pose training because they are more dense and accurate and consistent, and can compensate for the corner cases such as unusual viewpoints. We quantitatively demonstrate the improved performance of our dense pose detector over the available DensePose. Our dense pose dataset and detector will be made public.
Training Competitive Binary Neural Networks from Scratch

Convolutional neural networks have achieved astonishing results in different application areas. Various methods that allow us to use these models on mobile and embedded devices have been proposed. Especially binary neural networks are a promising approach for devices with low computational power. However, training accurate binary models from scratch remains a challenge. Previous work often uses prior knowledge from full-precision models and complex training strategies. In our work, we focus on increasing the performance of binary neural networks without such prior knowledge and a much simpler training strategy. In our experiments we show that we are able to achieve state-of-the-art results on standard benchmark datasets. Further, to the best of our knowledge, we are the first to successfully adopt a network architecture with dense connections for binary networks, which lets us improve the state-of-the-art even further.
Tideglusib Rescues Neurite Pathology of SPG11 iPSC Derived Cortical Neurons

Mutations in SPG11 cause a complicated autosomal recessive form of hereditary spastic paraplegia (HSP). Mechanistically, there are indications for the dysregulation of the GSK3β/βCat signaling pathway in SPG11. In this study, we tested the therapeutic potential of the GSK3β inhibitor, tideglusib, to rescue neurodegeneration associated characteristics in an induced pluripotent stem cells (iPSCs) derived neuronal model from SPG11 patients and matched healthy controls as well as a CRISPR-Cas9 mediated SPG11 knock-out line and respective control. SPG11-iPSC derived cortical neurons, as well as the genome edited neurons exhibited shorter and less complex neurites than controls. Administration of tideglusib to these lines led to the rescue of neuritic impairments. Moreover, the treatment restored increased cell death and ameliorated the membranous inclusions in iPSC derived SPG11 neurons. Our results provide a first evidence for the rescue of neurite pathology in SPG11-HSP by tideglusib. The current lack of disease-modifying treatments for SPG11 and related types of complicated HSP renders tideglusib a candidate compound for future clinical application.
Coverage and Handoff Analysis of 5G Fractal Small Cell Networks

It is anticipated that a considerably higher network capacity will be achieved by the fifth generation (5G) small cell networks incorporated with the millimeter wave (mm-wave) technology. However, the mm-wave signals are more sensitive to blockages than signals in lower frequency bands, which highlight the effect of anisotropic path loss in network coverage. According to the fractal characteristics of cellular coverage, a multi-directional path loss model is proposed for the 5G small cell networks, where different directions are subject to different path loss exponents. Furthermore, the coverage probability, association probability, and the handoff probability are derived for the 5G fractal small cell networks based on the proposed multi-directional path loss model. The numerical results indicate that the coverage probability with the multi-directional path loss model is less than that with the isotropic path loss model, and the association probability with long link distance, e.g., 150m, increases obviously with the increase of the effect of anisotropic path loss in 5G fractal small cell networks. Moreover, it is observed that the anisotropic propagation environment is having a profound impact on the handoff performance. Meanwhile, we could conclude that the resulting heavy handoff overhead is emerging as a new challenge for 5G fractal small cell networks.
A Symbolic Framework to Analyse Physical Proximity in Security Protocols

For many modern applications like e.g., contactless payment, and keyless systems, ensuring physical proximity is a security goal of paramount importance. Formal methods have proved their usefulness when analysing standard security protocols. However, existing results and tools do not apply to e.g., distance bounding protocols that aims to ensure physical proximity between two entities. This is due in particular to the fact that existing models do not represent in a faithful way the locations of the participants, and the fact that transmission of messages takes time. In this paper, we propose several reduction results: when looking for an attack, it is actually suﬃcient to consider a simple scenario involving at most four participants located at some speciﬁc locations. These reduction results allow one to use veriﬁcation tools (e.g. ProVerif, Tamarin) developed for analysing more classical security properties. As an application, we analyse several distance bounding protocols, as well as a contactless payment protocol.
Invasive species denialism: Sorting out facts, beliefs, and definitions

Abstract In the last decades, thousands of investigations confirmed the detrimental effects of species translocated by man outside of their native ranges (nonindigenous species, or NIS). However, results concluding that many NIS have null, neutral, or positive impacts on the biota and on human interests are as common in the scientific literature as those that point at baneful impacts. Recently, several scholars confronted the stand that origin per se is not a reliable indicator of negative effects, suggesting that such conclusions are the expression of scientific denialism, often led by spurious purposes, and that their numbers are increasing. When assessed in the context of the growing interest in introduced species, the proportion of academic publications claiming that NIS pose no threats to the environment and to social and economic interests is extremely low, and has not increased since 1990. The widely prevailing notion that many NIS are effectively or potentially harmful does not conflict with the fact that most have mixed (negative, neutral, and positive) impacts. When based on solid grounds, reports of positive or neutral impacts should not be labeled as manipulative or misleading unless proven otherwise, even if they may hamper interest in‐ and funding of research and control bioinvasion programs.
Dissecting Person Re-Identification From the Viewpoint of Viewpoint

Variations in visual factors such as viewpoint, pose, illumination and background, are usually viewed as important challenges in person re-identification (re-ID). In spite of acknowledging these factors to be influential, quantitative studies on how they affect a re-ID system are still lacking. To derive insights in this scientific campaign, this paper makes an early attempt in studying a particular factor, viewpoint. We narrow the viewpoint problem down to the pedestrian rotation angle to obtain focused conclusions. In this regard, this paper makes two contributions to the community. First, we introduce a large-scale synthetic data engine, PersonX. Composed of hand-crafted 3D person models, the salient characteristic of this engine is “controllable”. That is, we are able to synthesize pedestrians by setting the visual variables to arbitrary values. Second, on the 3D data engine, we quantitatively analyze the influence of pedestrian rotation angle on re-ID accuracy. Comprehensively, the person rotation angles are precisely customized from 0 to 360, allowing us to investigate its effect on the training, query, and gallery sets. Extensive experiment helps us have a deeper understanding of the fundamental problems in person re-ID. Our research also provides useful insights for dataset building and future practical usage, e.g., a person of a side view makes a better query.
Establishing information security policy compliance culture in organizations

Purpose This paper aims to establish that employees’ non-compliance with information security policy (ISP) could be addressed by nurturing ISP compliance culture through the promotion of factors such as supportive organizational culture, end-user involvement and compliance leadership to influence employees’ attitudes and behaviour intentions towards ISP in organizations. This paper also aims to develop a testable research model that might be useful for future researchers in predicting employees’ behavioural intentions. Design/methodology/approach In view of the study’s aim, a research model to show how three key constructs can influence the attitudes and behaviours of employees towards the establishment of security policy compliance culture (ISPCC) was developed and validated in an empirical field survey. Findings The study found that factors such as supportive organizational culture and end-user involvement significantly influenced employees’ attitudes towards compliance with ISP. However, leadership showed the weakest influence on attitudes towards compliance. The overall results showed that employees’ attitudes and behavioural intentions towards ISP compliance together influenced the establishment of ISPCC for ISP compliance in organizations. Practical implications Organizations should influence employees’ attitudes towards compliance with ISP by providing effective ISP leadership, encouraging end-user involvement during the draft and update of ISP and nurturing a culture that is conducive for ISP compliance. Originality/value The study provides some insights on how to effectively address the problem of non-compliance with ISP in organizations through the establishment of ISPCC, which has not been considered in any past research.
Complete the Look: Scene-Based Complementary Product Recommendation

Modeling fashion compatibility is challenging due to its complexity and subjectivity. Existing work focuses on predicting compatibility between product images (e.g. an image containing a t-shirt and an image containing a pair of jeans). However, these approaches ignore real-world ‘scene’ images (e.g. selfies); such images are hard to deal with due to their complexity, clutter, variations in lighting and pose (etc.) but on the other hand could potentially provide key context (e.g. the user’s body type, or the season) for making more accurate recommendations. In this work, we propose a new task called ‘Complete the Look’, which seeks to recommend visually compatible products based on scene images. We design an approach to extract training data for this task, and propose a novel way to learn the scene-product compatibility from fashion or interior design images. Our approach measures compatibility both globally and locally via CNNs and attention mechanisms. Extensive experiments show that our method achieves significant performance gains over alternative systems. Human evaluation and qualitative analysis are also conducted to further understand model behavior. We hope this work could lead to useful applications which link large corpora of real-world scenes with shoppable products.
Enacting Space in Virtual Reality: A Comparison Between Money’s Road Map Test and Its Virtual Version

In the field of spatial cognition research the mutual relationship between perception and action that brings out spatial orientation was lately investigated. Besides, the sameness between creating a cognitive map from the exploration of a not simulated environment, from the use of an allocentric (survey-like) sketched map, and from the interaction with egocentric (route-like) 3D virtual environments, is generally contrived. To understand if different embodied affordances could provide different knowledge organization during wayfinding through the use of distinct spatial simulations, the same group of 61 healthy subjects experienced both the classical version of the Money’s Road Map test (M-RMT) and a virtual reality version of the Road Map test (VR-RMT). The M-RMT requires a allocentric to egocentric right/left reasoning to explore a stylized city provided in a survey perspective. The VR-RMT is a 3D version of the same environment through which participants can actively navigate by choosing egocentric-based right/left directions in a route perspective. The results showed that the different embodiments afforded by the two environments and the increasing complexity in turn types provides different spatial outcomes. Results were discussed according to the sensorimotor coupling theory provided from the enactive cognition approach and significances for spatial cognition research were provided.
Localized online learning-based control of a soft redundant manipulator under variable loading

ABSTRACT Soft robots are inherently compliant and manoeuvrable manipulators that can passively adapt to their environment. However, in order to fully make use of their unique properties, accurate control should still be maintained when affected by external loading. Commonly used model-based approaches often have low tolerance to unmodelled loading, resulting in significant error when acted on by them. Therefore, in this study we employ a nonparametric learning-based method that can approximate and update the inverse model of a redundant two-segment soft robot in an online manner. The primary contribution of this work is the application and evaluation of the proposed framework on a redundant soft robot. With the addition of redundancy, a constrained optimization approach is taken to consistently resolve null-space behaviour. Through this control framework, the controller can continuously adapt to unknown external disturbances during runtime and maintain end-effector accuracy. The performance of the control framework was evaluated by tracking of a 3D trajectory with a static tip load, and a variable weight tip load. The results indicate that the proposed controller could effectively adapt to the disturbances and continue to track the desired trajectory accurately. GRAPHICAL ABSTRACT
An inertia model for the adoption of new farming practices

Nutrient emissions from agricultural land are now widely recognized as one of the key contributors to poor water quality in local lakes, rivers and streams. Nutrient trading for non-point sources, including farm land, has been suggested as a regulatory tool to improve and protect water quality. However, farmers’ attitudes suggest that they are resistant to making the changes required under such a scheme where this requires them to adopt unfamiliar technologies and farm management practices. This study develops a model of farmers’ resistance to change and how this affects their adoption of new mitigation technologies under nutrient trading regulation. We specify resistance as a bound on the adoption of new technologies and allow this bound to relax as farmers’ resistance to change weakens.
Prognosis of patients with neuroendocrine tumor: a SEER database analysis

Background Neuroendocrine tumors (NETs) are a group of heterogeneous cancers arising from a variety of anatomic sites. Their incidence has increased in recent years. This study aimed to analyze the prognosis of NETs originating from different anatomic sites. Methods We identified 73,782 patients diagnosed with NETs from the Surveillance Epidemiology and Ends Results (SEER) database from 1973 to 2014. Clinical data were compared between patients with different primary tumor sites using the chi-squared test. Differences in survival among NET patients with different tumor sites were compared by Kaplan–Meier analysis. Cox proportional hazard models were performed to identify the prognostic factors of overall survival. Results In this cohort, the lung/bronchus was the most common site of NETs, accounting for 30.6%, followed by the small intestine (22.2%), rectum (16.2%), colon (13.4%), pancreas (10.8%), and stomach (6.8%). Totally, 73,782 patients were selected for this cohort from 1973 to 2014. The median survival duration was 41 months. The 1-, 3-, 5-, and 10-year overall survival rates for patients with NETs were 72.8%, 52.7%, 39.4%, and 18.1%, respectively. Patients with NETs located in the rectum had the best prognosis, followed by those with NETs in the small intestine (HR, 1.660, 95% CI, 1.579, 1.744), lung/bronchus (HR, 1.786, 95% CI, 1.703, 1.874), stomach (HR, 1.865, 95% CI, 1.755, 1.982), and colon (HR, 1.896, 95% CI, 1.799, 1.999). Patients with NETs in the pancreas had the highest risk of mortality (HR, 2.034, 95% CI, 1.925, 2.148). Conclusion Significant differences in survival were found among various primary tumor sites. NETs in the rectum had the best prognosis, while those in the pancreas had the worst. Primary tumor sites might be one of the most useful outcome predictors in patients with NETs.
Learning to Compose Dynamic Tree Structures for Visual Contexts

We propose to compose dynamic tree structures that place the objects in an image into a visual context, helping visual reasoning tasks such as scene graph generation and visual Q&A. Our visual context tree model, dubbed VCTree, has two key advantages over existing structured object representations including chains and fully-connected graphs: 1) The efficient and expressive binary tree encodes the inherent parallel/hierarchical relationships among objects, e.g., ``clothes'' and ``pants'' are usually co-occur and belong to ``person''; 2) the dynamic structure varies from image to image and task to task, allowing more content-/task-specific message passing among objects. To construct a VCTree, we design a score function that calculates the task-dependent validity between each object pair, and the tree is the binary version of the maximum spanning tree from the score matrix. Then, visual contexts are encoded by bidirectional TreeLSTM and decoded by task-specific models. We develop a hybrid learning procedure which integrates end-task supervised learning and the tree structure reinforcement learning, where the former's evaluation result serves as a self-critic for the latter's structure exploration. Experimental results on two benchmarks, which require reasoning over contexts: Visual Genome for scene graph generation and VQA2.0 for visual Q&A, show that VCTree outperforms state-of-the-art results while discovering interpretable visual context structures.
Balancing QoS and power consumption in programmable 5G infrastructures

The advent of fifth generation is promising to push far more intelligence than today to the network boundary, hence boosting novel computing models based on fog/edge paradigms. The need for proximity in computation, coupled with various forms of mobility, will be responsible for dynamic shifting of workload within the system, with large fluctuations in resource usage. This eventually turns into poor energy efficiency of the whole infrastructure. However, improving efficiency usually deteriorates quality of service, hence the dilemma about how to balance these two contrasting aspects.
Computing Bayes-Nash Equilibria in Combinatorial Auctions with Verification

We present a new algorithm for computing pure-strategy ε-Bayes-Nash equilibria (ε-BNEs) in combinatorial auctions. The main innovation of our algorithm is to separate the algorithm’s search phase (for finding the ε-BNE) from the verification phase (for computing the ε). Using this approach, we obtain an algorithm that is both very fast and provides theoretical guarantees on the ε it finds. Our main contribution is a verification method which, surprisingly, allows us to upper bound the ε across the whole continuous value space without making assumptions about the mechanism. Using our algorithm, we can now compute ε-BNEs in multi-minded domains that are significantly more complex than what was previously possible to solve. We release our code under an open-source license to enable researchers to perform algorithmic analyses of auctions, to enable bidders to analyze different strategies, and many other applications.