Miracle-dz/UniFAR-FTUs huggingface dataset:
Emotion Recognition Based on Multichannel Physiological Signals with Comprehensive Nonlinear Processing

Multichannel physiological datasets are usually nonlinear and separable in the field of emotion recognition. Many researchers have applied linear or partial nonlinear processing in feature reduction and classification, but these applications did not work well. Therefore, this paper proposed a comprehensive nonlinear method to solve this problem. On the one hand, as traditional feature reduction may cause the loss of significant amounts of feature information, Kernel Principal Component Analysis (KPCA) based on radial basis function (RBF) was introduced to map the data into a high-dimensional space, extract the nonlinear information of the features, and then reduce the dimension. This method can provide many features carrying information about the structure in the physiological dataset. On the other hand, considering its advantages of predictive power and feature selection from a large number of features, Gradient Boosting Decision Tree (GBDT) was used as a nonlinear ensemble classifier to improve the recognition accuracy. The comprehensive nonlinear processing method had a great performance on our physiological dataset. Classification accuracy of four emotions in 29 participants achieved 93.42%.
Early Turn-Taking Prediction in the Operating Room

This work presents the design and implementation of an early turn-taking prediction algorithm for a robotic scrub nurse sys-tem. The turn-taking prediction algorithm analyzes surgeon’s implicit communication cues identifying among those surgical instrument requests before the request actually are explicitly evoked. Communication channels expressed through signals like EEG, EMG and physical signs were used to monitor surgeon’s behaviors and automatically detect implicit instrument requests. Significant features were extracted from those signals, through an automatic feature selection process. Then recurrent neural networks were used for time-sensitive turn-taking prediction. Experimental results indicated that the proposed algorithm has higher prediction accuracies than human baseline when less than 70% of the entire action was observed. This is approximately 1.4 seconds after the action started, and 0.6 seconds before the action ends. At an extremely early stage (only 10% of data), the proposed turn-taking prediction algorithm achieves a F1 score of 82.8%.
Composing Modeling and Inference Operations with Probabilistic Program Combinators

Probabilistic programs with dynamic computation graphs can define measures over sample spaces with unbounded dimensionality, which constitute programmatic analogues to Bayesian nonparametrics. Owing to the generality of this model class, inference relies on `black-box' Monte Carlo methods that are often not able to take advantage of conditional independence and exchangeability, which have historically been the cornerstones of efficient inference. We here seek to develop a `middle ground' between probabilistic models with fully dynamic and fully static computation graphs. To this end, we introduce a combinator library for the Probabilistic Torch framework. Combinators are functions that accept models and return transformed models. We assume that models are dynamic, but that model composition is static, in the sense that combinator application takes place prior to evaluating the model on data. Combinators provide primitives for both model and inference composition. Model combinators take the form of classic functional programming constructs such as map and reduce. These constructs define a computation graph at a coarsened level of representation, in which nodes correspond to models, rather than individual variables. Inference combinators implement operations such as importance resampling and application of a transition kernel, which alter the evaluation strategy for a model whilst preserving proper weighting. Owing to this property, models defined using combinators can be trained using stochastic methods that optimize either variational or wake-sleep style objectives. As a validation of this principle, we use combinators to implement black box inference for hidden Markov models.
Probabilistic Planning for Decentralized Multi-Robot Systems

Introduction Multi-robot systems offer a compelling application domain for multi-agent AI research: they pose challenging questions, they offer an immediate bridge to actual and tangible systems, and they have vast potential for real-world applications. However, most multi-robot systems are controlled by hand-built special-purpose algorithms that are difficult to design, implement and verify. For single robots, automatic planning systems provide a flexible general-purpose strategy for constructing plans given high-level declarative domain specifications, even in the presence of substantial stochasticity and partial observability (Thrun, Burgard, and Fox 2005). Our recent research has shown that this same strategy—finding the appropriate generic model and writing general-purpose planners for it—can be applied to multirobot systems. Our methods allow automatic off-line construction of robust multi-robot policies that support coordinated actions. As a natural consequence, our methods can generate control and communication strategies that optimize the group’s overall objective. Specifically, we are interested in problems where robots share the same objective function and each individual robot can only make noisy, partial observations of the environment. The decentralized partially observable Markov decision process (Dec-POMDP) is a general framework for representing multi-agent coordination problems. DecPOMDPs have been widely studied in artificial intelligence as a way to address the fundamental differences in decisionmaking in decentralized settings (Amato et al. 2013; Bernstein et al. 2002; Oliehoek 2012). Like the POMDP (Kaelbling, Littman, and Cassandra 1998) model that it extends, Dec-POMDPs consider general dynamics, cost and sensor models. Any problem where multiple robots share a single overall reward or cost function can be formalized as a Dec-POMDP. Unfortunately, this generality comes at a cost: Dec-POMDPs are typically infeasible to solve except for small problems (Bernstein et al. 2002; Amato, Konidaris, and Kaelbling. 2014). One reason for the intractability of solving large DecPOMDPs is that current approaches model problems at a low level of granularity, where each robot’s actions are primitive operations lasting exactly one time step. Our recent research has addressed the more realistic MacDec-POMDP case where each robot has macro-actions: temporally extended actions which may require different amounts of time to execute (Amato, Konidaris, and Kaelbling. 2014). An alternative formulation is the Dec-POSMDP, which operates directly in belief space (Omidshafiei et al. 2015). These models allow coordination decisions to only occur at the level of deciding which macro-actions to execute. Macroactions are a natural model for the modular controllers (e.g., navigating to a waypoint or grasping an object) sequenced to obtain robot behavior, bridging the gap between robotics research and Dec-POMDPs.
Neural Processing of Repeated Search Targets Depends Upon the Stimuli: Real World Stimuli Engage Semantic Processing and Recognition Memory

Recent evidence has suggested that visual working memory (VWM) plays an important role in representing the target prior to initiating a visual search. The more familiar we are with the search target, the more refined the representation of the target (or “target template”) becomes. This sharpening of the target template is thought to underlie the reduced response time (RT) and increased accuracy associated with repeatedly searching for the same target. Perhaps target representations transition from limited-capacity VWM to Long-Term Memory (LTM) as targets repeat. In prior work, amplitude of an event-related potential (ERP) component associated with VWM representation decreased with target repetition, broadly supporting this notion. However, previous research has focused on artificial stimuli (Landolt Cs) that are far removed from search targets in the real world. The current study extends this work by directly comparing target representations for artificial stimuli and common object images. We found VWM representation follows the same pattern for real and artificial stimuli. However, the initial selection of the real world objects follows a much different pattern than more typical artificial stimuli. Further, the morphology of nonlateralized waveforms was substantially different for the two stimulus categories. This suggests that the two types of stimuli were processed in fundamentally different ways. We conclude that object type strongly influences how we deploy attentional and mnemonic resources prior to search. Early attentional selection of familiar objects may facilitate additional LTM processes that lead to behavioral benefits not seen with more simplistic stimuli.
Comparative Transcriptome Analysis Provides Novel Insight into Morphologic and Metabolic Changes in the Fat Body during Silkworm Metamorphosis

The fat body plays key roles in energy storage and utilization as well as biosynthetic and metabolic activities in insects. During metamorphosis from larva to pupa, the fat body undergoes dramatic changes in morphology and metabolic processes. However, the genetic basis underlying these changes has not been completely understood. In this study, the authors performed a time-course transcriptome analysis of the fat body during silkworm metamorphosis using RNA-sequencing. A total of 5217 differentially expressed genes (DEGs) were identified in the fat body at different developmental time points. DEGs involved in lipid synthesis and degradation were highly expressed at the third day of the last larval instar and during the prepupal-pupal transition, respectively. DEGs involved in the ecdysone signaling and bone morphogenetic protein (BMP) signaling pathways that modulate organ development exhibited a high expression level during the fat body remodeling process from prepupa to pupa. Intriguingly, the RNA interference-mediated knockdown of either decapentaplegic (Dpp) or protein 60A (Gbb), two DEGs involved in the BMP signaling pathway, inhibited fat body dissociation but promoted lipid mobilization, suggesting that the BMP signaling pathway not only is required for fat body remodeling, but also moderately inhibits lipid mobilization to ensure an appropriate lipid supply during the pupal-adult transition. In conclusion, the comparative transcriptome analysis provides novel insight into morphologic and metabolic changes in the fat body during silkworm metamorphosis.
SLIRS: Sign Language Interpreting System for Human-Robot Interaction

Deaf-mute communities around the world experience a need in effective human-robot interaction system that would act as an interpreter in public places such as banks, hospitals, or police stations. The focus of this work is to address the challenges presented to hearing-impaired people by developing an interpreting robotic system required for effective communication in public places. To this end, we utilize a previously developed neural network-based learning architecture to recognize Cyrillic manual alphabet, which is used for ﬁn-ger spelling in Kazakhstan. In order to train and test the performance of the recognition system, we collected a depth data set of ten people and applied it to a learning-based method for gesture recognition by modeling motion data. We report our results that show an average accuracy of 77.2% for a complete alphabet recognition consisting of 33 letters.
A spatio-temporal entropy-based approach for the analysis of cyber attacks (demo paper)

Computer networks are ubiquitous systems growing exponentially with a predicted 50 billion devices connected by 2050. This dramatically increases the potential attack surface of Internet networks. A key issue in cyber defense is to detect, categorize and identify these attacks, the way they are propagated and their potential impacts on the systems affected. The research presented in this paper models cyber attacks at large by considering the Internet as a complex system in which attacks are propagated over a network. We model an attack as a path from a source to a target, and where each attack is categorized according to its intention. We setup an experimental testbed with the concept of honeypot that evaluates the spatio-temporal distribution of these Internet attacks. The preliminary results show a series of patterns in space and time that illustrate the potential of the approach, and how cyber attacks can be categorized according to the concept and measure of entropy.
A force-directed approach for offline GPS trajectory map matching

We present a novel algorithm to match GPS trajectories onto maps offline (in batch mode) using techniques borrowed from the field of force-directed graph drawing. We consider a simulated physical system where each GPS trajectory is attracted or repelled by the underlying road network via electrical-like forces. We let the system evolve under the action of these physical forces such that individual trajectories are attracted towards candidate roads to obtain a map matching path. Our approach has several advantages compared to traditional, routing-based, algorithms for map matching, including the ability to account for noise and to avoid large detours due to outliers in the data whilst taking into account the underlying topological restrictions (such as one-way roads). Our empirical evaluation using real GPS traces shows that our method produces better map matching results compared to alternative offline map matching algorithms on average, especially for routes in dense, urban areas.
Direct Transformation of Metallized Paper into Al-Si Nano-Rod and Al Nano-Particles Using Thermal Micronizing Technique

The abundant application of metallized paper and the quick growth of their wastes lead to the removal of a huge amount of valuable resources from economic cycle. In this work, for the first-time, the thermal micronizing technique has been used to directly transform the metallized paper wastes to Al-Si nano-rod and Al nano-particles for use as the input in different manufacturing sectors such as additive manufacturing or composite fabrication. Structure of metallized paper has been investigated using FT-IR analysis and first-principle plane-wave calculation. Then, based on the structure of metallized paper, thermal micronizing technique has been modified to directly transform this waste into nano materials. Structure of nano-particles and nano-rods has been investigated using SEM, TEM, and XPS analysis. Results showed two main Al-Si nano-rod and Al nano-particle morphologies created as a result of the different surface tensions, which facilitate their separation by Eddy current separation technique. These quick transformation and facile separation together make this technique a unique process to deal with this complex waste and producing value-added products which can re-capture these high value materials from waste and make the reforming economically viable.
Software Document Terminology Recognition

Our goal in this paper is to achieve automatic extraction and classiﬁcation of key phrases from software development documents, such as requirements, speciﬁcations, and so on. In software development projects, creating dictionaries is important for deﬁning the terminologies used to enable accurate communication between customers and vendors, as well as among developers. However, each target domain, such as a medical, ﬁnancial, transportational, or other ﬁeld, has its own particular terminology; moreover, each customer employs its own terms and their respective meanings. Building a dictionary of a target domain requires experts’ knowledge in the given domain and considerable effort. To assist in dictionary building, we are developing a software document terminology recognizer (SDTR) with the use of named entity recognition (NER) methods. A signiﬁcant amount of research exists on NER; however, most of it is focused on general named entities, such as person names, or biological domain named entities, such as names of compounds. However, the problem of building effective entity recognizers in a new domain where you have very little supervised data available is very understudied. There are a lot of small domains each of them has different terminology because software is used in various domains and organizations. Also it is impractical to build taggers by traditional supervised NER methods for SDTR because the tuning cost in individual software development projects is limited. Building method of an SDTR should cover cross-domain terminologies using small size of corpus; nevertheless, an SDTR must cope with very speciﬁc terminologies for individual projects. In this paper, we propose a multi-layered SDTR system consisting of an identiﬁer that uses general features based on the probability of phrases and spelling conventions, and an identiﬁer that employs a temporary dictionary automatically built into the general feature identiﬁer. Currently, our prototype achieves a greater than 0.8 F1-value on a small software development project corpus.
An Updated Meta-Analysis of the Associations Between MicroRNA Polymorphisms and Susceptibility to Rheumatoid Arthritis

Aims: Rheumatoid arthritis (RA) is characterized by cartilage and bone damage leading to disability. Here, the association between microRNA (miRNA) polymorphisms and susceptibility to RA was evaluated by performing an updated meta-analysis and systematic review. Main methods: An electronic search of databases including PubMed and Embase was performed from inception to December 8, 2017 to retrieve studies investigating the association between miRNA polymorphisms and RA risk. Two reviewers independently screened literature according to the inclusion and exclusion criteria and extracted data. The meta-analysis was conducted using Stata 14.0 software. Key findings: Thirteen case-control studies with 2660 cases and 4098 controls were screened out after a systematic search. One study from the miR-146a rs2910164 G > C polymorphism group and two from the miR-499 rs3746444 T > C polymorphism group were excluded because of deviations from Hardy-Weinberg equilibrium. Pooled analysis demonstrated that miR-146a rs2910164 G > C polymorphism was not significantly associated with susceptibility to RA. However, a significant association was observed between miR-499 rs3746444 T > C polymorphism and RA risk (C vs. T: OR = 1.22, 95% CI = 1.05–1.42, P = 0.008; TC vs. TT: OR = 1.26, 95% CI = 1.05–1.50, P = 0.011; TC/CC vs. TT: OR = 1.26, 95% CI = 1.07–1.5, P = 0.007). Subgroup analysis based on ethnicity showed no significant association between miR-499 T > C polymorphism and susceptibility to RA in the Asian population (P > 0.05). However, in Caucasian population, the C allele in the miR-499 T > C polymorphism was a contributor to RA susceptibility in some genetic models (C vs. T: OR = 1.64, 95% CI = 1.28–2.11, P < 0.001; TC vs. TT: OR = 1.95, 95% CI = 1.40–2.71, P < 0.001; TC/CC vs. TT: OR = 1.96, 95% CI = 1.43–2.69, P < 0.001). Significance: The miR-146a rs2910164 G > C polymorphism was not associated with susceptibility to RA. In the Caucasian population, the C allele in the miR-499 T > C polymorphism contributed to RA susceptibility.
Discovering Subgoals in Complex Domains

We present ongoing research to develop novel option discovery methods for complex domains that are represented as Object-Oriented Markov Decision Processes (OO-MDPs) (Diuk, Cohen, and Littman, 2008). We describe Portable Multi-policy Option Discovery for Automated Learning (P-MODAL), an initial framework that extends Pickett and Barto’s (2002) PolicyBlocks approach to OO-MDPs. We also discuss future work that will use additional representations and techniques to handle scalability and learning challenges. We present our ongoing and planned work on Portable Multi-policy Option Discovery for Automated Learning (P-MODAL), a framework for ﬁnding options that can be used to identify useful subgoals in Object-Oriented Markov Decision Process (OO-MDP) domains (Diuk, Cohen, and Littman, 2008). An option (Sutton, Precup, and Singh, 1999) is an extended action that enables an agent to achieve a subgoal.
Pegasos: primal estimated sub-gradient solver for SVM

We describe and analyze a simple and effective stochastic sub-gradient descent algorithm for solving the optimization problem cast by Support Vector Machines (SVM). We prove that the number of iterations required to obtain a solution of accuracy $${\epsilon}$$ is $${\tilde{O}(1 / \epsilon)}$$, where each iteration operates on a single training example. In contrast, previous analyses of stochastic gradient descent methods for SVMs require $${\Omega(1 / \epsilon^2)}$$ iterations. As in previously devised SVM solvers, the number of iterations also scales linearly with 1/λ, where λ is the regularization parameter of SVM. For a linear kernel, the total run-time of our method is $${\tilde{O}(d/(\lambda \epsilon))}$$, where d is a bound on the number of non-zero features in each example. Since the run-time does not depend directly on the size of the training set, the resulting algorithm is especially suited for learning from large datasets. Our approach also extends to non-linear kernels while working solely on the primal objective function, though in this case the runtime does depend linearly on the training set size. Our algorithm is particularly well suited for large text classification problems, where we demonstrate an order-of-magnitude speedup over previous SVM learning methods.
Single-Blind Placebo-Controlled Response Test with Phenytoin 10% Cream in Neuropathic Pain Patients

Background: Phenytoin cream applied topically has been explored in neuropathic pain conditions. In several case series, phenytoin 5% and 10% cream could reduce pain in a clinically relevant way with a fast onset of action within 30 min, and with positive effects on sleep. Objective: To evaluate a single-blind placebo-controlled response test (SIBRET) for use in clinical practice. Materials and Methods: Patients with localized neuropathic pain, having an equal pain intensity in at least 2 areas (e.g., both feet), and a pain intensity of at least 4 on the 11-point numerical rating scale (NRS), were selected to perform the SIBRET. In one area, placebo cream consisting of the base cream was applied, and on the other area, phenytoin 10% cream was applied with separate hands to avoid contamination. Responders were defined as patients who experienced within 30 min at least 2-points difference as scored on the NRS, between the phenytoin 10% and the placebo cream applied areas, in favor of the former. Responders were subsequently prescribed phenytoin 10% cream. Results: Of the 21 patients, 15 patients (71.45%) were classified as responders. The mean pain reduction after 30 min as measured with the NRS in the phenytoin 10% cream area was 3.3 (SD: 1.3) and in the placebo cream area 1.2 (SD: 1.1). The difference of the mean percentage pain reduction between phenytoin 10% cream and placebo cream was 33.2% (SD: 17.6, p < 0.001). Using a 50% reduction on the NRS as a full response criterion, we could identify 57.1% of responders on phenytoin 10% cream and only 9.5% responders on placebo cream. Conclusions: The SIBRET helps patients and clinicians to quickly identify the appropriate treatment and can thus be seen as an important contributor to the domain of personalized medicine in pain. These results can also be regarded as a proof of principle for the analgesic activity of 10% phenytoin cream.