OpenRaiser/Intern-Atlas huggingface dataset:
Video Frame Interpolation without Temporal Priors
Venue: arXiv cs.CV 2021
Year: 2021

Video frame interpolation, which aims to synthesize non-exist intermediate frames in a video sequence, is an important research topic in computer vision. Existing video frame interpolation methods have achieved remarkable results under specific assumptions, such as instant or known exposure time. However, in complicated real-world situations, the temporal priors of videos, i.e. frames per second (FPS) and frame exposure time, may vary from different camera sensors. When test videos are taken under different exposure settings from training ones, the interpolated frames will suffer significant misalignment problems. In this work, we solve the video frame interpolation problem in a general situation, where input frames can be acquired under uncertain exposure (and interval) time. Unlike previous methods that can only be applied to a specific temporal prior, we derive a general curvilinear motion trajectory formula from four consecutive sharp frames or two consecutive blurry frames without temporal priors. Moreover, utilizing constraints within adjacent motion trajectories, we devise a novel optical flow refinement strategy for better interpolation results. Finally, experiments demonstrate that one well-trained model is enough for synthesizing high-quality slow-motion videos under complicated real-world situations. Codes are available on https://github.com/yjzhang96/UTI-VFI.
MemPool-3D: Boosting Performance and Efficiency of Shared-L1 Memory Many-Core Clusters with 3D Integration
Venue: arXiv cs.AR 2021
Year: 2021

Three-dimensional integrated circuits promise power, performance, and footprint gains compared to their 2D counterparts, thanks to drastic reductions in the interconnects' length through their smaller form factor. We can leverage the potential of 3D integration by enhancing MemPool, an open-source many-core design with 256 cores and a shared pool of L1 scratchpad memory connected with a low-latency interconnect. MemPool's baseline 2D design is severely limited by routing congestion and wire propagation delay, making the design ideal for 3D integration. In architectural terms, we increase MemPool's scratchpad memory capacity beyond the sweet spot for 2D designs, improving performance in a common digital signal processing kernel. We propose a 3D MemPool design that leverages a smart partitioning of the memory resources across two layers to balance the size and utilization of the stacked dies. In this paper, we explore the architectural and the technology parameter spaces by analyzing the power, performance, area, and energy efficiency of MemPool instances in 2D and 3D with 1 MiB, 2 MiB, 4 MiB, and 8 MiB of scratchpad memory in a commercial 28 nm technology node. We observe a performance gain of 9.1% when running a matrix multiplication on the MemPool-3D design with 4 MiB of scratchpad memory compared to the MemPool 2D counterpart. In terms of energy efficiency, we can implement the MemPool-3D instance with 4 MiB of L1 memory on an energy budget 15% smaller than its 2D counterpart, and even 3.7% smaller than the MemPool-2D instance with one-fourth of the L1 scratchpad memory capacity.
LDA2Net: Digging under the surface of COVID-19 topics in scientific literature
Venue: arXiv cs.DL 2021
Year: 2021

During the COVID-19 pandemic, the scientific literature related to SARS-COV-2 has been growing dramatically, both in terms of the number of publications and of its impact on people's life. This literature encompasses a varied set of sensible topics, ranging from vaccination, to protective equipment efficacy, to lockdown policy evaluation. Up to now, hundreds of thousands of papers have been uploaded on online repositories and published in scientific journals. As a result, the development of digital methods that allow an in-depth exploration of this growing literature has become a relevant issue, both to identify the topical trends of COVID-related research and to zoom-in its sub-themes. This work proposes a novel methodology, called LDA2Net, which combines topic modelling and network analysis to investigate topics under their surface. Specifically, LDA2Net exploits the frequencies of pairs of consecutive words to reconstruct the network structure of topics discussed in the Cord-19 corpus. The results suggest that the effectiveness of topic models can be magnified by enriching them with word network representations, and by using the latter to display, analyse, and explore COVID-related topics at different levels of granularity.
Maximum Entropy Model-based Reinforcement Learning
Venue: arXiv cs.AI 2021
Year: 2021

Recent advances in reinforcement learning have demonstrated its ability to solve hard agent-environment interaction tasks on a super-human level. However, the application of reinforcement learning methods to practical and real-world tasks is currently limited due to most RL state-of-art algorithms' sample inefficiency, i.e., the need for a vast number of training episodes. For example, OpenAI Five algorithm that has beaten human players in Dota 2 has trained for thousands of years of game time. Several approaches exist that tackle the issue of sample inefficiency, that either offers a more efficient usage of already gathered experience or aim to gain a more relevant and diverse experience via a better exploration of an environment. However, to our knowledge, no such approach exists for model-based algorithms, that showed their high sample efficiency in solving hard control tasks with high-dimensional state space. This work connects exploration techniques and model-based reinforcement learning. We have designed a novel exploration method that takes into account features of the model-based approach. We also demonstrate through experiments that our method significantly improves the performance of the model-based algorithm Dreamer.
Early Prediction of Mortality in Critical Care Setting in Sepsis Patients Using Structured Features and Unstructured Clinical Notes
Venue: arXiv cs.CY 2021
Year: 2021

Sepsis is an important cause of mortality, especially in intensive care unit (ICU) patients. Developing novel methods to identify early mortality is critical for improving survival outcomes in sepsis patients. Using the MIMIC-III database, we integrated demographic data, physiological measurements and clinical notes. We built and applied several machine learning models to predict the risk of hospital mortality and 30-day mortality in sepsis patients. From the clinical notes, we generated clinically meaningful word representations and embeddings. Supervised learning classifiers and a deep learning architecture were used to construct prediction models. The configurations that utilized both structured and unstructured clinical features yielded competitive F-measure of 0.512. Our results showed that the approaches integrating both structured and unstructured clinical features can be effectively applied to assist clinicians in identifying the risk of mortality in sepsis patients upon admission to the ICU.
Local Justice and the Algorithmic Allocation of Societal Resources
Venue: arXiv cs.CY 2021
Year: 2021

AI is increasingly used to aid decision-making about the allocation of scarce societal resources, for example housing for homeless people, organs for transplantation, and food donations. Recently, there have been several proposals for how to design objectives for these systems that attempt to achieve some combination of fairness, efficiency, incentive compatibility, and satisfactory aggregation of stakeholder preferences. This paper lays out possible roles and opportunities for AI in this domain, arguing for a closer engagement with the political philosophy literature on local justice, which provides a framework for thinking about how societies have over time framed objectives for such allocation problems. It also discusses how we may be able to integrate into this framework the opportunities and risks opened up by the ubiquity of data and the availability of algorithms that can use them to make accurate predictions about the future.
Towards Continuous Compounding Effects and Agile Practices in Educational Experimentation
Venue: arXiv cs.CY 2021
Year: 2021

Randomised control trials are currently the definitive gold standard approach for formal educational experiments. Although conclusions from these experiments are highly credible, their relatively slow experimentation rate, high expense and rigid framework can be seen to limit scope on: 1. $\textit{metrics}$: automation of the consistent rigorous computation of hundreds of metrics for every experiment; 2. $\textit{concurrency}$: fast automated releases of hundreds of concurrent experiments daily; and 3. $\textit{safeguards}$: safety net tests and ramping up/rolling back treatments quickly to minimise negative impact. This paper defines a framework for categorising different experimental processes, and places a particular emphasis on technology readiness. On the basis of our analysis, our thesis is that the next generation of education technology successes will be heralded by recognising the context of experiments and collectively embracing the full set of processes that are at hand: from rapid ideation and prototyping produced in small scale experiments on the one hand, to influencing recommendations of best teaching practices with large-scale and technology-enabled online A/B testing on the other. A key benefit of the latter is that the running costs tend towards zero (leading to `free experimentation'). This offers low-risk opportunities to explore and drive value though well-planned lasting campaigns that iterate quickly at a large scale. Importantly, because these experimental platforms are so adaptable, the cumulative effect of the experimental campaign delivers compounding value exponentially over time even if each individual experiment delivers a small effect.
Health Detection on Cattle Compressed Images in Precision Livestock Farming
Venue: arXiv cs.CY 2021
Year: 2021

The constant population growth brings the needing to make up for food also grows at the same rate. The livestock provides one-third of humans protein base as meat and milk. To improve cattles health and welfare the pastoral farming employs Precision Livestock farming (PLF). This technique implementation brings a challenge to minimize energy consumption due to farmers not having enough energy or devices to transmit large volumes of information at the size are received from their farms monitors. Therefore, in this project, we will design an algorithm to compress and decompress images reducing energy consumption with the less information lost. Initially, the related problems have been read and analyzed to learn about the techniques used in the past and to be updated with the current works. We implemented Seam Carving and LZW algorithms. The compression of all images, around 1000 takes a time of 5 hours 10 min. We got a compression rate of 1.82:1 with 13.75s average time for each file and a decompression rate of 1.64:1 and 7.5 s average time for each file. The memory consumption we obtained was between 146MB and 504 MB and time consumption was between 30,5s for 90MB to 12192s for 24410 MB, it was all files.
Where the Earth is flat and 9/11 is an inside job: A comparative algorithm audit of conspiratorial information in web search results
Venue: arXiv cs.IR 2021
Year: 2021

Web search engines are important online information intermediaries that are frequently used and highly trusted by the public despite multiple evidence of their outputs being subjected to inaccuracies and biases. One form of such inaccuracy, which so far received little scholarly attention, is the presence of conspiratorial information, namely pages promoting conspiracy theories. We address this gap by conducting a comparative algorithm audit to examine the distribution of conspiratorial information in search results across five search engines: Google, Bing, DuckDuckGo, Yahoo and Yandex. Using a virtual agent-based infrastructure, we systematically collect search outputs for six conspiracy theory-related queries (flat earth, new world order, qanon, 9/11, illuminati, george soros) across three locations (two in the US and one in the UK) and two observation periods (March and May 2021). We find that all search engines except Google consistently displayed conspiracy-promoting results and returned links to conspiracy-dedicated websites in their top results, although the share of such content varied across queries. Most conspiracy-promoting results came from social media and conspiracy-dedicated websites while conspiracy-debunking information was shared by scientific websites and, to a lesser extent, legacy media. The fact that these observations are consistent across different locations and time periods highlight the possibility of some search engines systematically prioritizing conspiracy-promoting content and, thus, amplifying their distribution in the online environments.
Achieving a Data-driven Risk Assessment Methodology for Ethical AI
Venue: arXiv cs.CY 2021
Year: 2021

The AI landscape demands a broad set of legal, ethical, and societal considerations to be accounted for in order to develop ethical AI (eAI) solutions which sustain human values and rights. Currently, a variety of guidelines and a handful of niche tools exist to account for and tackle individual challenges. However, it is also well established that many organizations face practical challenges in navigating these considerations from a risk management perspective. Therefore, new methodologies are needed to provide a well-vetted and real-world applicable structure and path through the checks and balances needed for ethically assessing and guiding the development of AI. In this paper we show that a multidisciplinary research approach, spanning cross-sectional viewpoints, is the foundation of a pragmatic definition of ethical and societal risks faced by organizations using AI. Equally important is the findings of cross-structural governance for implementing eAI successfully. Based on evidence acquired from our multidisciplinary research investigation, we propose a novel data-driven risk assessment methodology, entitled DRESS-eAI. In addition, through the evaluation of our methodological implementation, we demonstrate its state-of-the-art relevance as a tool for sustaining human values in the data-driven AI era.
Detecting Extratropical Cyclones of the Northern Hemisphere with Single Shot Detector
Venue: arXiv cs.LG 2021
Year: 2021

In this paper, we propose a deep learning-based model to detect extratropical cyclones (ETCs) of northern hemisphere, while developing a novel workflow of processing images and generating labels for ETCs. We first label the cyclone center by adapting an approach from Bonfanti et.al. [1] and set up criteria of labeling ETCs of three categories: developing, mature, and declining stages. We then propose a framework of labeling and preprocessing the images in our dataset. Once the images and labels are ready to serve as inputs, we create our object detection model named Single Shot Detector (SSD) to fit the format of our dataset. We train and evaluate our model with our labeled dataset on two settings (binary and multiclass classifications), while keeping a record of the results. Finally, we achieved relatively high performance with detecting ETCs of mature stage (mean Average Precision is 86.64%), and an acceptable result for detecting ETCs of all three categories (mean Average Precision 79.34%). We conclude that the single-shot detector model can succeed in detecting ETCs of different stages, and it has demonstrated great potential in the future applications of ETC detection in other relevant settings.
An Intelligent Vice Cluster Head Election Protocol in WSN
Venue: arXiv cs.NI 2021
Year: 2021

Wireless sensor networks (WSNs) has a practical ability to link a set of sensors to build a wireless network that can be accessed remotely; this technology has become increasingly popular in recent years. Wi-Fi-enabled sensor networks (WSNs) are used to gather information from the environment in which the network operates. Many obstacles prevent wireless sensor networks from being used in a wide range of fields. This includes maintaining network stability and extending network life. In a wireless network, sensors are the most essential component. Sensors are powered by a battery that has a finite amount of power. The battery is prone to power loss, and the sensor is therefore rendered inoperative as a result. In addition, the growing number of sensor nodes off-site affects the network's stability. The transmission and reception of information between the sensors and the base consumes the most energy in the sensor. An Intelligent Vice Cluster Head Selection Protocol is proposed in this study (IVC LEACH). In order to achieve the best performance with the least amount of energy consumption, the proposed hierarchical protocol relies on a fuzzy logic algorithm using four parameters to calculate the value of each node in the network and divides them into three hierarchical levels based on their value. This improves network efficiency and reliability while extending network life by 50 percent more than the original Low Energy Adaptive Clustering Hierarchy protocol
Lists of Top Artists to Watch computed algorithmically
Venue: arXiv cs.CY 2021
Year: 2021

Lists of top artists to watch are periodically published by various art world media publications. These lists are selected editorially and reflect the subjective opinions of their creators. We show an application of ranking by momentum method to algorithmically produce lists of artists to watch derived from our platform Articker. We use our algorithms every month to produce a new list of momentum leaders on www.articker.org. The lists of momentum leaders computed this way has the following properties: a.It is small (because of the small frontier property). b.It is unbiased -- with no bias towards famous artists nor emerging, and yet unknown, artists c It is fair -- artists who are not included in the list must be Pareto dominated by at least one member of the momentum leaders list (Pareto frontier) d.It is objective -- it is computed automatically, not editorially selected.
Stationary Diffusion State Neural Estimation for Multiview Clustering
Venue: arXiv cs.LG 2021
Year: 2021

Although many graph-based clustering methods attempt to model the stationary diffusion state in their objectives, their performance limits to using a predefined graph. We argue that the estimation of the stationary diffusion state can be achieved by gradient descent over neural networks. We specifically design the Stationary Diffusion State Neural Estimation (SDSNE) to exploit multiview structural graph information for co-supervised learning. We explore how to design a graph neural network specially for unsupervised multiview learning and integrate multiple graphs into a unified consensus graph by a shared self-attentional module. The view-shared self-attentional module utilizes the graph structure to learn a view-consistent global graph. Meanwhile, instead of using auto-encoder in most unsupervised learning graph neural networks, SDSNE uses a co-supervised strategy with structure information to supervise the model learning. The co-supervised strategy as the loss function guides SDSNE in achieving the stationary state. With the help of the loss and the self-attentional module, we learn to obtain a graph in which nodes in each connected component fully connect by the same weight. Experiments on several multiview datasets demonstrate effectiveness of SDSNE in terms of six clustering evaluation metrics.
MuA-Ori: Multimodal Actuated Origami
Venue: arXiv cs.RO 2021
Year: 2021

Recently, inflatable elements integrated in robotics systems have enabled complex motions as a result of simple inputs. However, these fluidic actuators typically exhibit unimodal deformation upon inflation. Here, we present a new design concept for modular, fluidic actuators that can switch between deformation modes as a response to an input threshold. Our system comprises bistable origami modules in which snapping breaks rotational symmetry, giving access to a bending deformation. By tuning geometry, the modules can be designed to snap at different pressure thresholds, rotate clockwise or counterclockwise when actuated, and bend in different planes. Due to their ability to assume multiple deformation modes as response to a single pressure input we call our system MuA-Ori, or Multimodal Actuated Origami. MuA-Ori provides an ideal platform to design actuators that can switch between different configurations, reach multiple, pre-defined targets in space, and move along complex trajectories.
Digital Twin-Assisted Controlling of AGVs in Flexible Manufacturing Environments
Venue: arXiv cs.RO 2021
Year: 2021

Digital Twins are increasingly being introduced for smart manufacturing systems to improve the efficiency of the main disciplines of such systems. Formal techniques, such as graphs, are a common way of describing Digital Twin models, allowing broad types of tools to provide Digital Twin based services such as fault detection in production lines. Obtaining correct and complete formal Digital Twins of physical systems can be a complicated and time consuming process, particularly for manufacturing systems with plenty of physical objects and the associated manufacturing processes. Automatic generation of Digital Twins is an emerging research field and can reduce time and costs. In this paper, we focus on the generation of Digital Twins for flexible manufacturing systems with Automated Guided Vehicles (AGVs) on the factory floor. In particular, we propose an architectural framework and the associated design choices and software development tools that facilitate automatic generation of Digital Twins for AGVs. Specifically, the scope of the generated digital twins is controlling AGVs in the factory floor. To this end, we focus on different control levels of AGVs and utilize graph theory to generate the graph-based Digital Twin of the factory floor.
InsCLR: Improving Instance Retrieval with Self-Supervision
Venue: arXiv cs.CV 2021
Year: 2021

This work aims at improving instance retrieval with self-supervision. We find that fine-tuning using the recently developed self-supervised (SSL) learning methods, such as SimCLR and MoCo, fails to improve the performance of instance retrieval. In this work, we identify that the learnt representations for instance retrieval should be invariant to large variations in viewpoint and background etc., whereas self-augmented positives applied by the current SSL methods can not provide strong enough signals for learning robust instance-level representations. To overcome this problem, we propose InsCLR, a new SSL method that builds on the \textit{instance-level} contrast, to learn the intra-class invariance by dynamically mining meaningful pseudo positive samples from both mini-batches and a memory bank during training. Extensive experiments demonstrate that InsCLR achieves similar or even better performance than the state-of-the-art SSL methods on instance retrieval. Code is available at https://github.com/zeludeng/insclr.