Miracle-dz/UniFAR-FTUs huggingface dataset:
Software-Defined Design Space Exploration for an Efficient DNN Accelerator Architecture

Deep neural networks (DNNs) have been shown to outperform conventional machine learning algorithms across a wide range of applications, e.g., image recognition, object detection, robotics, and natural language processing. However, the high computational complexity of DNNs often necessitates extremely fast and efficient hardware. The problem gets worse as the size of neural networks grows exponentially. As a result, customized hardware accelerators have been developed to accelerate DNN processing without sacrificing model accuracy. However, previous accelerator design studies have not fully considered the characteristics of the target applications, which may lead to sub-optimal architecture designs. On the other hand, new DNN models have been developed for better accuracy, but their compatibility with the underlying hardware accelerator is often overlooked. In this article, we propose an application-driven framework for architectural design space exploration of DNN accelerators. This framework is based on a hardware analytical model of individual DNN operations. It models the accelerator design task as a multi-dimensional optimization problem. We demonstrate that it can be efficaciously used in application-driven accelerator architecture design: we use the framework to optimize the accelerator configurations for eight representative DNNs and select the configuration with the highest geometric mean performance. The geometric mean performance improvement of the selected DNN configuration relative to the architectural configuration optimized only for each individual DNN ranges from 12.0 to 117.9 percent. Given a target DNN, the framework can generate efficient accelerator design solutions with optimized performance and area. Furthermore, we explore the opportunity to use the framework for accelerator configuration optimization under simultaneous diverse DNN applications. The framework is also capable of improving neural network models to best fit the underlying hardware resources. We demonstrate that it can be used to analyze the relationship between the operations of the target DNNs and the corresponding accelerator configurations, based on which the DNNs can be tuned for better processing efficiency on the given accelerator without sacrificing accuracy.
OREOS: Oriented Recognition of 3D Point Clouds in Outdoor Scenarios

We introduce a novel method for oriented place recognition with 3D LiDAR scans. A Convolutional Neural Network is trained to extract compact descriptors from single 3D LiDAR scans. These can be used both to retrieve near-by place candidates from a map, and to estimate the yaw discrepancy needed for bootstrapping local registration methods. We employ a triplet loss function for training and use a hard-negative mining strategy to further increase the performance of our descriptor extractor. In an extensive evaluation on the NCLT and KITTI datasets, we demonstrate that our method outperforms related state-of-the-art approaches based on both data-driven and handcrafted data representation in challenging long-term outdoor conditions.
Debiasing Community Detection: The Importance of Lowly Connected Nodes

Community detection is an important task in social network analysis, allowing us to identify and understand the communities within the social structures provided by the network. However, many community detection approaches either fail to assign low-degree (or lowly connected) users to communities, or assign them to trivially small communities that prevent them from being included in analysis. In this work we investigate how excluding these users can bias analysis results. We then introduce an approach that is more inclusive for lowly connected users by incorporating them into larger groups. Experiments show that our approach outperforms the existing state-of-the-art in terms of F1 and Jaccard similarity scores while reducing the bias towards low-degree users.
SpinalLog: Visuo-Haptic Feedback in Musculoskeletal Manipulation Training

Current techniques for teaching spinal mobilisation follow the traditional classroom approach: an instructor demonstrates a technique and students attempt to emulate it by practising on each other while receiving feedback from the instructor. This paper introduces SpinalLog, a novel tangible user interface (TUI) for teaching and learning spinal mobilisation. The system was co-designed with physiotherapy experts to look and feel like a human spine, supporting the learning of mobilisation techniques through real-time visual feedback and deformation based passive haptic feedback. We evaluated Physical Fidelity, Visual Feedback, and Passive Haptic Feedback in an experiment to understand their effects on physiotherapy students' ability to replicate a mobilisation pattern recorded by an expert. We found that simultaneous feedback has the largest effect, followed by passive haptic feedback. The high fidelity of the interface has little effect, but it plays an important role in the perception of the system's benefit.
Distributed Kalman-filtering: Distributed optimization viewpoint

We consider the Kalman-filtering problem with multiple sensors which are connected through a communication network. If all measurements are delivered to one place called fusion center and processed together, we call the process centralized Kalman-filtering (CKF). When there is no fusion center, each sensor can also solve the problem by using local measurements and exchanging information with its neighboring sensors, which is called distributed Kalman-filtering (DKF). Noting that CKF problem is a maximum likelihood estimation problem, which is a quadratic optimization problem, we reformulate DKF problem as a consensus optimization problem, resulting in that DKF problem can be solved by many existing distributed optimization algorithms. A new DKF algorithm employing the distributed dual ascent method is provided and its performance is evaluated through numerical experiments.
Multi-party authorization and conflict mediation for decentralized configuration management processes

Configuration management in networks with highest security demands must not depend on just one administrator and her device. Otherwise, problems can be caused by mistakes or malicious behavior of this admin, or when her computer got com-promised, which allows an attacker to abuse the administrator’s far-reaching permissions.Instead, we propose to use a reliable and resilient configuration management process orchestrated by a configuration management system (CMS). This can be achieved by separation of concerns (proposing a configuration vs. authorizing it), employing multi-party authorization (MPA), and enforcing that only authorized configurations can be deployed. This results in a configuration management process that is decentralized on a human, decision-making level, and a technical, device level.However, due to different opinions or adversarial interference, the result of an MPA process can end in a conflict. This raises the question how such conflicts can be mediated in a better way than just employing majority voting, which is insufficient in certain situations. As an alternative, this paper introduces building blocks of customizable conflict mediation strategies which we integrated into our CMS TANCS [1]. The conflict mediation functionality as well as the initial TANCS implementation run on top of the distributed ledger and smart contract framework Hyperledger Fabric which makes all processes resilient and tamper-resistant.
DDoS attack detection method based on feature extraction of deep belief network

Distributed Denial of Service (DDOS) attack is one of the most common network attacks. DDoS attacks are becoming more and more diverse, which makes it difficult for some DDoS attack detection methods based on single network flow characteristics to detect various types of DDoS attacks, while the detection methods of multi-feature DDoS attacks have a certain lag due to the complexity of the algorithm. Therefore, it is necessary and urgent to monitor the trend of traffic change and identify DDoS attacks timely and accurately. In this paper, a method of DDoS attack detection based on deep belief network feature extraction and LSTM model is proposed. This method uses deep belief network to extract the features of IP packets, and identifies DDoS attacks based on LSTM model. This scheme is suitable for DDoS attack detection technology. The model can accurately predict the trend of normal network traffic, identify the anomalies caused by DDoS attacks, and apply to solve more detection methods about DDoS attacks in the future.
Hyper-Parameter Sweep on AlphaZero General

Since AlphaGo and AlphaGo Zero have achieved breakground successes in the game of Go, the programs have been generalized to solve other tasks. Subsequently, AlphaZero was developed to play Go, Chess and Shogi. In the literature, the algorithms are explained well. However, AlphaZero contains many parameters, and for neither AlphaGo, AlphaGo Zero nor AlphaZero, there is sufficient discussion about how to set parameter values in these algorithms. Therefore, in this paper, we choose 12 parameters in AlphaZero and evaluate how these parameters contribute to training. We focus on three objectives~(training loss, time cost and playing strength). For each parameter, we train 3 models using 3 different values~(minimum value, default value, maximum value). We use the game of play 6$\times$6 Othello, on the AlphaZeroGeneral open source re-implementation of AlphaZero. Overall, experimental results show that different values can lead to different training results, proving the importance of such a parameter sweep. We categorize these 12 parameters into time-sensitive parameters and time-friendly parameters. Moreover, through multi-objective analysis, this paper provides an insightful basis for further hyper-parameter optimization.
Learning Reciprocity in Complex Sequential Social Dilemmas

Reciprocity is an important feature of human social interaction and underpins our cooperative nature. What is more, simple forms of reciprocity have proved remarkably resilient in matrix game social dilemmas. Most famously, the tit-for-tat strategy performs very well in tournaments of Prisoner's Dilemma. Unfortunately this strategy is not readily applicable to the real world, in which options to cooperate or defect are temporally and spatially extended. Here, we present a general online reinforcement learning algorithm that displays reciprocal behavior towards its co-players. We show that it can induce pro-social outcomes for the wider group when learning alongside selfish agents, both in a $2$-player Markov game, and in $5$-player intertemporal social dilemmas. We analyse the resulting policies to show that the reciprocating agents are strongly influenced by their co-players' behavior.
Preconditioned P-ULA for Joint Deconvolution-Segmentation of Ultrasound Images

Joint deconvolution and segmentation of ultrasound images is a challenging problem in medical imaging. By adopting a hierarchical Bayesian model, we propose an accelerated Markov chain Monte Carlo scheme where the tissue reflectivity function is sampled thanks to a recently introduced proximal unadjusted Langevin algorithm. This new approach is combined with a forward-backward step and a preconditioning strategy to accelerate the convergence, and with a method based on the majorization-minimization principle to solve the inner nonconvex minimization problems. As demonstrated in numerical experiments conducted on both simulated and in vivo ultrasound images, the proposed method provides high-quality restoration and segmentation results and is up to six times faster than an existing Hamiltonian Monte Carlo method.
AttentiveVideo

Understanding a target audience's emotional responses to a video advertisement is crucial to evaluate the advertisement's effectiveness. However, traditional methods for collecting such information are slow, expensive, and coarse grained. We propose AttentiveVideo, a scalable intelligent mobile interface with corresponding inference algorithms to monitor and quantify the effects of mobile video advertising in real time. Without requiring additional sensors, AttentiveVideo employs a combination of implicit photoplethysmography (PPG) sensing and facial expression analysis (FEA) to detect the attention, engagement, and sentiment of viewers as they watch video advertisements on unmodified smartphones. In a 24-participant study, AttentiveVideo achieved good accuracy on a wide range of emotional measures (the best average accuracy = 82.6% across nine measures). While feature fusion alone did not improve prediction accuracy with a single model, it significantly improved the accuracy when working together with model fusion. We also found that the PPG sensing channel and the FEA technique have different strength in data availability, latency detection, accuracy, and usage environment. These findings show the potential for both low-cost collection and deep understanding of emotional responses to mobile video advertisements.
Human and DNN Classification Performance on Images With Quality Distortions

Image quality is an important practical challenge that is often overlooked in the design of machine vision systems. Commonly, machine vision systems are trained and tested on high-quality image datasets, yet in practical applications the input images cannot be assumed to be of high quality. Modern deep neural networks (DNNs) have been shown to perform poorly on images affected by blur or noise distortions. In this work, we investigate whether human subjects also perform poorly on distorted stimuli and provide a direct comparison with the performance of DNNs. Specifically, we study the effect of Gaussian blur and additive Gaussian noise on human and DNN classification performance. We perform two experiments: one crowd-sourced experiment with unlimited stimulus display time, and one lab experiment with 100ms display time. In both cases, we found that humans outperform neural networks on distorted stimuli, even when the networks are retrained with distorted data.
TetRotation: Utilising Multimodal Analytics and Gestural Interaction to Nurture Mental Rotation Skills

Embodied Interaction (EI) offers unique opportunities to uncover novel ways to achieve experiential learning whilst keeping students stimulated and engaged. Spatial abilities have been repeatedly demonstrated as a success predictor for educations and professions in Science, Technology, Engineering and Mathematics. However, many researchers argue that training and assessment of this pertinent reasoning skill is vastly underrepresented in the school curriculum. This paper presents TetRotation, a PhD centred on how affordances coming from Multimodal Analytics can be coupled with EI to nurture Mental Rotation (MR) skills. The overarching objectives of the project are two fold. First, the TetRotation Interaction Design study will highlight best practices identified through the assessment of efficiency, level of engagement and learning gains achieved when using gesture based EI to solve MR tasks. Next, in the TetRotation Game study, these design practices will guide the implementation of an interactive serious game purposed to support the development of MR skills. This research relies on mixed method techniques, including data collections from users' actions, like motion sensing, EEG, gaze tracking, video-recordings, click streams, interviews and surveys.
Rumor Blocking through Online Link Deletion on Social Networks

In recent years, social networks have become important platforms for people to disseminate information. However, we need to take effective measures such as blocking a set of links to control the negative rumors spreading over the network. In this article, we propose a Rumor Spread Minimization (RSM) problem, i.e., we remove an edge set from network such that the rumor spread is minimized. We first prove the objective function of RSM problem is not submodular. Then, we propose both submodular lower-bound and upper-bound of the objective function. Next, we develop a heuristic algorithm to approximate the objective function. Furthermore, we reformulate our objective function as the DS function (the Difference of Submodular functions). Finally, we conduct experiments on real-world datasets to evaluate our proposed method. The experiment results show that the upper and lower bounds are very close, which indicates the good quality of them. And, the proposed method outperforms the comparison methods.
Adaptivity, Variance and Separation for Adversarial Bandits

We make three contributions to the theory of k-armed adversarial bandits. First, we prove a first-order bound for a modified variant of the INF strategy by Audibert and Bubeck [2009], without sacrificing worst case optimality or modifying the loss estimators. Second, we provide a variance analysis for algorithms based on follow the regularised leader, showing that without adaptation the variance of the regret is typically {\Omega}(n^2) where n is the horizon. Finally, we study bounds that depend on the degree of separation of the arms, generalising the results by Cowan and Katehakis [2015] from the stochastic setting to the adversarial and improving the result of Seldin and Slivkins [2014] by a factor of log(n)/log(log(n)).
Delayed state synchronization of continuous-time multi-agent systems in the presence of unknown communication delays

This paper studies delayed synchronization of continuous-time multi-agent systems (MAS) in the presence of unknown nonuniform communication delays. A delay-free transformation is developed based on a communication network which is a directed spanning tree, which can transform the original MAS to a new one without delays. By using this transformation, we design a static protocol for full-state coupling and a dynamic protocol for delayed state synchronization for homogeneous MAS via full- and partial-state coupling. Meanwhile, the delayed output synchronization is also studied for heterogeneous MAS, which is achieved by using a low-gain and output regulation based dynamic protocol design via the delay-free transformation.
A Synaptically Connected Hypothalamic Magnocellular Vasopressin-Locus Coeruleus Neuronal Circuit and Its Plasticity in Response to Emotional and Physiological Stress

The locus coeruleus (LC)-norepinephrine (NE) system modulates a range of salient brain functions, including memory and response to stress. The LC-NE system is regulated by neurochemically diverse inputs, including a range of neuropeptides such as arginine-vasopressin (AVP). Whilst the origins of many of these LC inputs, their synaptic connectivity with LC neurons, and their contribution to LC-mediated brain functions, have been well characterized, this is not the case for the AVP-LC system. Therefore, our aims were to define the types of synapses formed by AVP+ fibers with LC neurons using immunohistochemistry together with confocal and transmission electron microscopy (TEM), the origins of such inputs, using retrograde tracers, and the plasticity of the LC AVP system in response to stress and spatial learning, using the maternal separation (MS) and Morris water maze (MWM) paradigms, respectively, in rat. Confocal microscopy revealed that AVP+ fibers contacting tyrosine hydroxylase (TH)+ LC neurons were also immunopositive for vesicular glutamate transporter 2, a marker of presynaptic glutamatergic axons. TEM confirmed that AVP+ axons formed Gray type I (asymmetric) synapses with TH+ dendrites thus confirming excitatory synaptic connections between these systems. Retrograde tracing revealed that these LC AVP+ fibers originate from hypothalamic vasopressinergic magnocellular neurosecretory neurons (AVPMNNs). MS induced a significant increase in the density of LC AVP+ fibers. Finally, AVPMNN circuit upregulation by water-deprivation improved MWM performance while increased Fos expression was found in LC and efferent regions such as hippocampus and prefrontal cortex, suggesting that AVPMMN projections to LC could integrate homeostatic responses modifying neuroplasticity.
Highly Variable Sialylation Status of Donor-Specific Antibodies Does Not Impact Humoral Rejection Outcomes

Clinical outcome in antibody-mediated rejection (AMR) shows high inter-individual heterogeneity. Sialylation status of the Fc fragment of IgGs is variable, which could modulate their ability to bind to C1q and/or Fc receptors. In this translational study, we evaluated whether DSA sialylation influence AMR outcomes. Among 938 kidney transplant recipients for whom a graft biopsy was performed between 2004 and 2012 at Lyon University Hospitals, 69 fulfilled the diagnosis criteria for AMR and were enrolled. Sera banked at the time of the biopsy were screened for the presence of DSA by Luminex. The sialylation status of total IgG and DSA was quantified using Sambucus nigra agglutinin-based chromatography. All patients had similar levels of sialylation of serum IgGs (~2%). In contrast, the proportion of sialylated DSA were highly variable (median = 9%; range = 0–100%), allowing to distribute the patients in two groups: high DSA sialylation (n = 44; 64%) and low DSA sialylation (n = 25; 36%). The two groups differed neither on the intensity of rejection lesions (C4d, ptc, and g; p > 0.05) nor on graft survival rates (Log rank test, p = 0.99). in vitro models confirmed the lack of impact of Fc sialylation on the ability of a monoclonal antibody to trigger classical complement cascade and activate NK cells. We conclude that DSA sialylation status is highly variable but has not impact on DSA pathogenicity and AMR outcome.
Violent video game engagement is not associated with adolescents' aggressive behaviour: evidence from a registered report

In this study, we investigated the extent to which adolescents who spend time playing violent video games exhibit higher levels of aggressive behaviour when compared with those who do not. A large sample of British adolescent participants (n = 1004) aged 14 and 15 years and an equal number of their carers were interviewed. Young people provided reports of their recent gaming experiences. Further, the violent contents of these games were coded using official EU and US ratings, and carers provided evaluations of their adolescents' aggressive behaviours in the past month. Following a preregistered analysis plan, multiple regression analyses tested the hypothesis that recent violent game play is linearly and positively related to carer assessments of aggressive behaviour. Results did not support this prediction, nor did they support the idea that the relationship between these factors follows a nonlinear parabolic function. There was no evidence for a critical tipping point relating violent game engagement to aggressive behaviour. Sensitivity and exploratory analyses indicated these null effects extended across multiple operationalizations of violent game engagement and when the focus was on another behavioural outcome, namely, prosocial behaviour. The discussion presents an interpretation of this pattern of effects in terms of both the ongoing scientific and policy debates around violent video games, and emerging standards for robust evidence-based policy concerning young people's technology use.