Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Latent Space Dynamics for Tactile Servoing

To achieve a dexterous robotic manipulation, we need to endow our robot with tactile feedback capability, i.e. the ability to drive action based on tactile sensing. In this paper, we specifically address the challenge of tactile servoing, i.e. given the current tactile sensing and a target/goal tactile sensing — memorized from a successful task execution in the past — what is the action that will bring the current tactile sensing to move closer towards the target tactile sensing at the next time step. We develop a data-driven approach to acquire a dynamics model for tactile servoing by learning from demonstration. Moreover, our method represents the tactile sensing information as to lie on a surface — or a 2D manifold — and perform a manifold learning, making it applicable to any tactile skin geometry. We evaluate our method on a contact point tracking task using a robot equipped with a tactile finger.
RadIoT: Radio Communications Intrusion Detection for IoT - A Protocol Independent Approach

Internet-of-Things (IoT) devices are nowadays massively integrated in daily life: homes, factories, or public places. This technology offers attractive services to improve the quality of life as well as new economic markets through the exploitation of the collected data. However, these connected objects have also become attractive targets for attackers because their current security design is often weak or flawed, as illustrated by several vulnerabilities such as Mirai, Blueborne, etc. This paper presents a novel approach for detecting intrusions in smart spaces such as smarthomes, or smartfactories, that is based on the monitoring and profiling of radio communications at the physical layer using machine learning techniques. The approach is designed to be independent of the large and heterogeneous set of wireless communication protocols typically implemented by connected objects such as WiFi, Bluetooth, Zigbee, Bluetooth-Low-Energy (BLE) or proprietary communication protocols. The main concepts of the proposed approach are presented together with an experimental case study illustrating its feasibility based on data collected during the deployment of the intrusion detection approach in a smart home under real-life conditions.
PhotoAgeClock: deep learning algorithms for development of non-invasive visual biomarkers of aging

Aging biomarkers are the qualitative and quantitative indicators of the aging processes of the human body. Estimation of biological age is important for assessing the physiological state of an organism. The advent of machine learning lead to the development of the many age predictors commonly referred to as the “aging clocks” varying in biological relevance, ease of use, cost, actionability, interpretability, and applications. Here we present and investigate a novel non-invasive class of visual photographic biomarkers of aging. We developed a simple and accurate predictor of chronological age using just the anonymized images of eye corners called the PhotoAgeClock. Deep neural networks were trained on 8414 anonymized high-resolution images of eye corners labeled with the correct chronological age. For people within the age range of 20 to 80 in a specific population, the model was able to achieve a mean absolute error of 2.3 years and 95% Pearson and Spearman correlation.
Sample-Efficient Policy Learning based on Completely Behavior Cloning

Direct policy search is one of the most important algorithm of reinforcement learning. However, learning from scratch needs a large amount of experience data and can be easily prone to poor local optima. In order to overcome these challenges, this paper proposed a training-free behavior cloning algorithm called Policy Learning based on Completely Behavior Cloning (PLCBC). PLCBC transforms the Model Predictive Control (MPC) controller into a PieceWise Affine (PWA) function with multi-parametric programming, and uses a neural network to express this function. By this way, off-the-shelf deep reinforcement learning algorithms can be used to fine-tune this neural network. The experiments show that our method can help agent learn at the high reward state region, and converge faster and better.
Non-invasive Oxygen-Ozone therapy in treating digital ulcers of patients with systemic sclerosis.

BACKGROUND Digital ulcers (DUs) in Systemic sclerosis (SSc) result from recurrent Raynaud's phenomenon (RP) and microtrauma with high impact on quality of life. Medical use of ozone (triatomic oxygen) was initiated in the 19th century. Ozone has multiple therapeutic effects in wound healing due to the property of releasing nascent oxygen, which has been shown to stimulate antioxidant enzymes. We aimed to assess the effects of ozone therapy on the healing of scleroderma DUs and determine levels of expression of vascular endothelial growth factor (VEGF), and endothelin-1 type A receptor (ETAR) autoantibodies in the wounds after treatment. SUBJECTS AND METHODS Fifty SSc female patients with DUs, were randomized into ozone group (I) (n=25) treated by calcium channel blockers plus oxygen-ozone treatment and control group (II) (n=25) treated by calcium channel blockers only. Ozone group received noninvasive oxygen-ozone treatments for 30 minutes per day for 20 days using the ozone generator device. Therapeutic effects were graded into 4 levels according to Zhang and other researchers. The wounds sizes were measured at baseline and day 20, respectively. Expressions of VEGF and ETAR autoantibodies proteins were determined by immune-histochemical examination. RESULTS Demographics and clinical characteristics of the 2 groups showed no significant differences. At day 20, the effective healing rate was significantly higher in group (I) than in group (II), where it represented 96% (24/25) in ozone group versus 44% (11/25) in control group, and (𝑃 = 0.007). After treatment, the wound sizes in both groups were significantly smaller than before treatment. In group (I), the wound size reduction was significantly more than in group (II) (0.75 ± 0.30 versus 2.44 ± 0.80 mm), (𝑃 = 0.00). At day 20, VEGF was significantly higher in ozone group (I), than in control group (II), (83.96±9.68) versus (67.92±6.55), (𝑃 = 0.00) while, ETAR was significantly lower in ozone group (I), than in control group (II), (3.14±1.12 versus 4.59±1.24), (𝑃 = 0.00). CONCLUSION Ozone therapy may be beneficial tool in the treatment of DUs in SSc patients, where it promotes the wound healing through a potential induction of VEGF and down-regulation of ETAR at sites of the ulcers.
Analysis of Fleet Modularity in an Artificial Intelligence-Based Attacker-Defender Game

Because combat environments change over time and technology upgrades are widespread for ground vehicles, a large number of vehicles and equipment become quickly obsolete. A possible solution for the U.S. Army is to develop fleets of modular military vehicles, which are built by interchangeable substantial components also known as modules. One of the typical characteristics of module is their ease of assembly and disassembly through simple means such as plug-in/pull-out actions, which allows for real-time fleet reconfiguration to meet dynamic demands. Moreover, military demands are time-varying and highly stochastic because commanders keep reacting to enemy's actions. To capture these characteristics, we formulated an intelligent agent-based model to imitate decision making process during fleet operation, which combines real-time optimization with artificial intelligence. The agents are capable of inferring enemy's future move based on historical data and optimize dispatch/operation decisions accordingly. We implement our model to simulate an attacker-defender game between two adversarial and intelligent players, representing the commanders from modularized fleet and conventional fleet respectively. Given the same level of combat resources and intelligence, we highlight the tactical advantages of fleet modularity in terms of win rate, unpredictability and suffered damage.
A Microprocessor implemented in 65nm CMOS with Configurable and Bit-scalable Accelerator for Programmable In-memory Computing

This paper presents a programmable in-memory-computing processor, demonstrated in a 65nm CMOS technology. For data-centric workloads, such as deep neural networks, data movement often dominates when implemented with today's computing architectures. This has motivated spatial architectures, where the arrangement of data-storage and compute hardware is distributed and explicitly aligned to the computation dataflow, most notably for matrix-vector multiplication. In-memory computing is a spatial architecture where processing elements correspond to dense bit cells, providing local storage and compute, typically employing analog operation. Though this raises the potential for high energy efficiency and throughput, analog operation has significantly limited robustness, scale, and programmability. This paper describes a 590kb in-memory-computing accelerator integrated in a programmable processor architecture, by exploiting recent approaches to charge-domain in-memory computing. The architecture takes the approach of tight coupling with an embedded CPU, through accelerator interfaces enabling integration in the standard processor memory space. Additionally, a near-memory-computing datapath both enables diverse computations locally, to address operations required across applications, and enables bit-precision scalability for matrix/input-vector elements, through a bit-parallel/bit-serial (BP/BS) scheme. Chip measurements show an energy efficiency of 152/297 1b-TOPS/W and throughput of 4.7/1.9 1b-TOPS (scaling linearly with the matrix/input-vector element precisions) at VDD of 1.2/0.85V. Neural network demonstrations with 1-b/4-b weights and activations for CIFAR-10 classification consume 5.3/105.2 $\mu$J/image at 176/23 fps, with accuracy at the level of digital/software implementation (89.3/92.4 $\%$ accuracy).
Isolation and Culture of Microglia

Microglia represent 5‐10% of cells in the central nervous system and contribute to the development, homeostasis, injury, and repair of neural tissues. As the tissue‐resident macrophages of the central nervous system, microglia execute core innate immune functions such as detection of pathogens/damage, cytokine secretion, and phagocytosis. However, additional properties that are specific to microglia and their neural environment are beginning to be appreciated. This article describes approaches for purification of microglia by fluorescence‐activated cell sorting using microglia‐specific surface markers and for enrichment of microglia by magnetic sorting and immunopanning. Detailed information about culturing primary microglia at various developmental stages is also provided. Throughout, we focus on special considerations for handling microglia and compare the relative strengths or disadvantages of different protocols. © 2018 by John Wiley & Sons, Inc.
Siroheme Is Essential for Assimilation of Nitrate and Sulfate as Well as Detoxification of Nitric Oxide but Dispensable for Murine Virulence of Aspergillus fumigatus

The saprophytic mold Aspergillus fumigatus is the most common airborne fungal pathogen causing severe invasive fungal infections in immunocompromised patients. Siroheme is a heme-like prosthetic group used by plants and microorganisms for sulfate and nitrate assimilation but is absent in higher eukaryotes. Here, we investigated the role of siroheme in A. fumigatus by deletion of the gene encoding the bifunctional dehydrogenase/ferrochelatase enzyme Met8. Met8-deficiency resulted in the inability to utilize sulfate and nitrate as sulfur and nitrogen sources, respectively. These results match previous data demonstrating that siroheme is an essential cofactor for nitrite and sulfite reductases. Moreover, Met8-deficiency caused significantly decreased resistance against nitric oxide (NO) underlining the importance of nitrite reductase in NO detoxification. Met8-deficiency did not affect virulence in murine models for invasive aspergillosis indicating that neither NO-detoxification nor assimilation of sulfate and nitrate play major roles in virulence in this host. Interestingly, Met8-deficiency resulted in mild virulence attenuation in the Galleria mellonella infection model revealing differences in interaction of A. fumigatus with G. mellonella and mouse.
Influence of Technical and Social Factors for Introducing Bugs

As the modern open-source paradigm makes it easier to contribute to software projects, the number of developers involved in these projects keep increasing. This growth in the amount of developers makes it more difficult to deal with harmful contributions. Recent researches have found that technical and social factors can predict the success of contributions to open-source projects on GitHub. However, these researches do not study the relation between these factors with the introduction of bugs. Our study aims at investigating the influence of technical (such as, developers' experience) and social (such as, number of followers) factors on the introduction of bugs, using information from 14 projects hosted on GitHub. Understanding the influence of these factors may be useful to developers, code reviewers and researchers. For instance, code reviewers may want to double check commits from developers that present bug-related factors. We found that technical factors have a consistent influence in the introduction of bugs. On the other hand, social factors present signs of influence in bug introduction that would require more data to be properly evaluated. Moreover, we found that perils present in the mining of GitHub may impact the factors results.
Reachability-based safe learning for optimal control problem

In this work we seek for an approach to integrate safety in the learning process that relies on a partly known state-space model of the system and regards the unknown dynamics as an additive bounded disturbance. We introduce a framework for safely learning a control strategy for a given system with an additive disturbance. On the basis of the known part of the model, a safe set in which the system can learn safely, the algorithm can choose optimal actions for pursuing the target set as long as the safety-preserving condition is satisfied. After some learning episodes, the disturbance can be updated based on real-world data. To this end, Gaussian Process regression is conducted on the collected disturbance samples. Since the unstable nature of the law of the real world, for example, change of friction or conductivity with the temperature, we expect to have the more robust solution of optimal control problem. For evaluation of approach described above we choose an inverted pendulum as a benchmark model. The proposed algorithm manages to learn a policy that does not violate the pre-specified safety constraints. Observed performance is improved when it was incorporated exploration set up to make sure that an optimal policy is learned everywhere in the safe set. Finally, we outline some promising directions for future research beyond the scope of this paper.
Exploring the Role of Sex and Sexual Experience in Predicting American Indian Adolescent Condom Use Intention Using Protection Motivation Theory

Introduction: American Indian (AI) youth experience poor sexual health outcomes. Research indicates the Protection Motivation Theory (PMT) is a robust model for understanding how sexual risk and protective behaviors are associated with condom use intention (CUI). Studies indicate the constructs of the PMT which influence CUI vary by sex and sexual experience. This analysis explores associations between PMT constructs and CUI by sex and sexual experience among AI youth who participated in the Respecting the Circle of Life (RCL) trial, a sexual and reproductive health intervention. Methods: We analyzed baseline data from the sample of 267 AIs, ages 13–19, who participated in the evaluation. We examined CUI and PMT construct scores by sex and sexual experience utilizing generalized estimated equations and multiple regression models to test which PMT constructs were associated with CUI across sex and sexual experience. Results: Twenty-two percentage of participants were sexually experienced; 56.8% reported CUI at baseline. We found several differences in scores in PMT constructs by sex and sexual experience including self-efficacy, response efficacy, vulnerability, severity, and extrinsic rewards. We also found constructs varied that were associated with CUI varied across sex and sexual experience. No PMT constructs were associated with CUI among sexually experienced youth. Conclusion: Results provide support for developing, selecting and delivering sexual health programs by sex and sexual experience in American Indian communities. Girls programs should focus on internal satisfaction and self-worth while boys should focus on negative impacts of not using condoms. Programs for youth who are not sexually active should focus on negative impacts of not using condoms. Programs for sexually inactive youth should work to change peer norms around condom use and improve knowledge about the efficacy of condom use.
Memory enhancement by ferulic acid ester across species

Inspired from plant medicine, a drug that partially compensates for memory decline in aged flies and mice is identified. Cognitive impairments can be devastating for quality of life, and thus, preventing or counteracting them is of great value. To this end, the present study exploits the potential of the plant Rhodiola rosea and identifies the constituent ferulic acid eicosyl ester [icosyl-(2E)-3-(4-hydroxy-3-methoxyphenyl)-prop-2-enoate (FAE-20)] as a memory enhancer. We show that food supplementation with dried root material from R. rosea dose-dependently improves odor-taste reward associative memory scores in larval Drosophila and prevents the age-related decline of this appetitive memory in adult flies. Task-relevant sensorimotor faculties remain unaltered. From a parallel approach, a list of candidate compounds has been derived, including R. rosea–derived FAE-20. Here, we show that both R. rosea–derived FAE-20 and synthetic FAE-20 are effective as memory enhancers in larval Drosophila. Synthetic FAE-20 also partially compensates for age-related memory decline in adult flies, as well as genetically induced early-onset loss of memory function in young flies. Furthermore, it increases excitability in mouse hippocampal CA1 neurons, leads to more stable context-shock aversive associative memory in young adult (3-month-old) mice, and increases memory scores in old (>2-year-old) mice. Given these effects, and given the utility of R. rosea—the plant from which we discovered FAE-20—as a memory enhancer, these results may hold potential for clinical applications.
Minimizing and Computing the Inverse Geodesic Length on Trees

The inverse geodesic length (IGL) of a graph $G=(V,E)$ is the sum of inverse distances between every two vertices: $IGL(G) = \sum_{\{u,v\} \subseteq V} \frac{1}{d_G(u,v)}$. In the MinIGL problem, the input is a graph $G$, an integer $k$, and a target inverse geodesic length $T$, and the question is whether there are $k$ vertices whose deletion decreases the IGL of $G$ to at most $T$. Aziz et al. (2018) proved that MinIGL is $W[1]$-hard for parameter treewidth, but the complexity status of the problem remains open in the case where $G$ is a tree. We show that MinIGL on trees is subexponential by giving a $2^{O\left((n \log n)^{5/6}\right)}$ time, polynomial space algorithm, where $n$ is the number of vertices of the input graph. The distance distribution of a graph $G$ is a sequence $\{a_i\}$ describing the number of vertex pairs distance $i$ apart in $G$: $a_i = |\{ \{u, v\}: d_G(u, v) = i\}|$. Given only the distance distribution, one can easily determine graph parameters such as diameter, Wiener index, and particularly, the IGL. We show that the distance distribution of a tree can be computed in $O(n \log^2 n)$ time by reduction to polynomial multiplication. We also extend the result to graphs of bounded treewidth by showing that the first $P$ values of the distance distribution can be computed in $2^{O(\text{tw}(G))} n^{1 + \varepsilon} \sqrt{P}$ time.
Effective Capacity in Wireless Networks: A Comprehensive Survey

Low latency applications, such as multimedia communications, autonomous vehicles, and Tactile Internet are the emerging applications for next-generation wireless networks, such as 5th generation (5G) mobile networks. Existing physical-layer channel models, however, do not explicitly consider quality-of-service (QoS) aware related parameters under specific delay constraints. To investigate the performance of low-latency applications in future networks, a new mathematical framework is needed. Effective capacity (EC), which is a link-layer channel model with QoS-awareness, can be used to investigate the performance of wireless networks under certain statistical delay constraints. In this paper, we provide a comprehensive survey on existing works, that use the EC model in various wireless networks. We summarize the work related to EC for different networks, such as cognitive radio networks (CRNs), cellular networks, relay networks, adhoc networks, and mesh networks. We explore five case studies encompassing EC operation with different design and architectural requirements. We survey various delay-sensitive applications, such as voice and video with their EC analysis under certain delay constraints. We finally present the future research directions with open issues covering EC maximization.
A Two-Step Neural Dialog State Tracker for Task-Oriented Dialog Processing

Dialog state tracking in a spoken dialog system is the task that tracks the flow of a dialog and identifies accurately what a user wants from the utterance. Since the success of a dialog is influenced by the ability of the system to catch the requirements of the user, accurate state tracking is important for spoken dialog systems. This paper proposes a two-step neural dialog state tracker which is composed of an informativeness classifier and a neural tracker. The informativeness classifier which is implemented by a CNN first filters out noninformative utterances in a dialog. Then, the neural tracker estimates dialog states from the remaining informative utterances. The tracker adopts the attention mechanism and the hierarchical softmax for its performance and fast training. To prove the effectiveness of the proposed model, we do experiments on dialog state tracking in the human-human task-oriented dialogs with the standard DSTC4 data set. Our experimental results prove the effectiveness of the proposed model by showing that the proposed model outperforms the neural trackers without the informativeness classifier, the attention mechanism, or the hierarchical softmax.
Polynomial-time Approximation Scheme for Minimum k-cut in Planar and Minor-free Graphs

The $k$-cut problem asks, given a connected graph $G$ and a positive integer $k$, to find a minimum-weight set of edges whose removal splits $G$ into $k$ connected components. We give the first polynomial-time algorithm with approximation factor $2-\epsilon$ (with constant $\epsilon > 0$) for the $k$-cut problem in planar and minor-free graphs. Applying more complex techniques, we further improve our method and give a polynomial-time approximation scheme for the $k$-cut problem in both planar and minor-free graphs. Despite persistent effort, to the best of our knowledge, this is the first improvement for the $k$-cut problem over standard approximation factor of $2$ in any major class of graphs.