Miracle-dz/UniFAR-FTUs huggingface dataset:
Randomized Value Functions via Multiplicative Normalizing Flows

Randomized value functions offer a promising approach towards the challenge of efficient exploration in complex environments with high dimensional state and action spaces. Unlike traditional point estimate methods, randomized value functions maintain a posterior distribution over action-space values. This prevents the agent's behavior policy from prematurely exploiting early estimates and falling into local optima. In this work, we leverage recent advances in variational Bayesian neural networks and combine these with traditional Deep Q-Networks (DQN) and Deep Deterministic Policy Gradient (DDPG) to achieve randomized value functions for high-dimensional domains. In particular, we augment DQN and DDPG with multiplicative normalizing flows in order to track a rich approximate posterior distribution over the parameters of the value function. This allows the agent to perform approximate Thompson sampling in a computationally efficient manner via stochastic gradient methods. We demonstrate the benefits of our approach through an empirical comparison in high dimensional environments.
A Design Framework for Adaptive Gamification Applications

The application of gamification does not always achieve the expected results due to the shortcomings of the quite common “one size fits all” approach of standard gamification concepts. We therefore propose a design framework that can inform systematic development of adaptive gamification applications. The developed framework draws on the current body of gamification literature, focusing on the emerging research stream of adaptive gamification. It provides design paths and design principles that translate the individual elements into concrete guidelines to assist the design practice. The framework has been successfully applied to the design and implementation of a prototype application using gamification to incentivize knowledge exchange on an existing online platform for physicians in practical medical training. The evaluation in a case study indicated positive user acceptance and increased system usage after the introduction of the developed adaptive gamification solution.
Long-term Sustained Cognitive Benefits of Vagus Nerve Stimulation in Refractory Depression

Background Treatment-resistant depression (TRD) is a serious chronic condition disabling patients functionally and cognitively. Chronic vagus nerve stimulation (VNS) is recognized for the management of TRD, but few studies have examined its long-term effects on cognitive dysfunction in unipolar and bipolar resistant depression. Objective The purpose of this study was to assess the course of cognitive functions and clinical symptoms in a cohort of patients treated with VNS for TRD. Methods In 14 TRD patients with VNS, standardized clinical and neuropsychological measures covering memory, attention/executive functions, and psychomotor speed were analyzed prestimulation and up to 2 years poststimulation. Results Vagus nerve stimulation patients significantly improved on cognitive and clinical measures. Learning and memory improved rapidly after 1 month of stimulation, and other cognitive functions improved gradually over time. Cognitive improvements were sustained up to 2 years of treatment. At 1 month, improvement in Montgomery-Åsberg Depression Rating Scale scores was not correlated with changes in any of the cognitive scores, whereas at 12 months, the change in Montgomery-Åsberg Depression Rating Scale score was significantly correlated with several measures (Stroop interference, verbal fluency, and Rey-Osterrieth Complex Figure delayed recall). Conclusions In recent years, a growing interest in cognitive dysfunction in depression has emerged. Our results suggest that chronic VNS produces sustained clinical and cognitive improvements in TRD patients, with some mental functions improving as soon as 1 month after the initiation of the VNS therapy. Vagus nerve stimulation seems a very promising adjunctive therapy for TRD patients with cognitive impairment.
Rhodanine derivatives as potent anti-HIV and anti-HSV microbicides

Although highly active antiretroviral therapies (HAART) remarkably increased life expectancy of HIV positive people, the rate of novel HIV-1 infections worldwide still represent a major concern. In this context, pre-exposure prophylaxis (PrEP) approaches such as vaginal microbicide gels topically releasing antiretroviral drugs, showed to have a striking impact in limiting HIV-1 spread. Nevertheless, the co-presence of other genital infections, particularly those due to HSV-1 or 2, constitute a serious drawback that strongly limits the efficacy of PrEP approaches. For this reason, combinations of different compounds with mixed antiviral and antiretroviral activity are thoroughly investigated Here we report the synthesis and the biological evaluation of a novel series of rhodanine derivatives, which showed to inhibit both HIV-1 and HSV-1/2 replication at nanomolar concentration, and were found to be active also on acyclovir resistant HSV-2 strains. The compounds showed a considerable reduction of activity in presence of serum due to a high binding to serum albumin, as determined through in vitro ADME evaluations. However, the most promising compound of the series maintained a considerable activity in gel formulation, with an EC50 comparable to that obtained for the reference drug tenofovir. Moreover, the series of compounds showed pharmacokinetic properties suitable for topical formulation, thus suggesting that the novel rhodanine derivatives could represent effective agents to be used as dual anti HIV/HSV microbicides in PrEP approaches.
On the Need for Random Baseline Comparisons in Metaheuristic Search

A wide variety of organizations now regularly rely on established metaheuristic search algorithms in order to find solutions to otherwise intractable optimization problems. Unfortunately, neither the developers of these algorithms nor the organizations that rely on them typically assess the algorithms’ performance against a baseline random search strategy, opting instead to compare a specific algorithm’s performance against that of other metaheuristic search algorithms. This paper reveals the folly of such behavior, and shows by means of an optimization case study that simple random or nearly random search algorithms can, in certain circumstances, substantially outperform several of the most widely used metaheuristic search algorithms in finding solutions to optimization problems. The implications of the observed results for both organizations and researchers are presented and
Connected Enterprise Meets Connected Customer - A Design Approach

In an increasingly digitized environment, enterprises face new challenges. Enabled by ubiquitous Internet accessibility, people, places, and products have become more interconnected and are gradually merging into the Internet of Everything. Simultaneously, a new generation of connected customers is emerging that is establishing new requirements for the capabilities of enterprises to communicate, interact, and respond to unforeseen events. As customer satisfaction is the central source of future competitiveness, companies must initiate a transformation towards a connected enterprise. By analyzing the characteristics of the connected customer, this paper presents guidelines for enterprises to address customer needs adequately and manage their operations in the Internet of Everything. Building upon established enterprise architecture frameworks, we apply a Design Science Research procedure to derive four practical recommendations. Thus, enterprises must manage their business processes holistically, implement information systems and standards for data exchange, provide mechanisms for real-time business intelligence, and determine their optimal degree of connectivity.
Value Chain Creation in Business Analytics

Firms are awash in big data and analytical technology as part of the process of deriving values in the current turbulent environment. The literature has reached a consensus that investments in technology only may not reap benefits from business analytics (BA). As such, the main purpose of BA is not about how to install technical capabilities, but about how to create a process whereby a firm builds a value chain converting data into insights and ultimately into quality outcomes. Drawing upon the theory of the information value chain, this study develops a BA value chain creation model and tests it with 268 firms. Results show that organizational resilience, absorptive capacity, and analytical IT capabilities are critical antecedents of analytical decision-making quality, which in turn influences BA net benefits. In particular, organizational resilience emerges as a more significant determinant of analytical decision-making quality than technology and people in the BA value chain creation. Theoretical and practical implications are also discussed in this paper.
The auxiliary region method: a hybrid method for coupling PDE- and Brownian-based dynamics for reaction–diffusion systems

Reaction–diffusion systems are used to represent many biological and physical phenomena. They model the random motion of particles (diffusion) and interactions between them (reactions). Such systems can be modelled at multiple scales with varying degrees of accuracy and computational efficiency. When representing genuinely multiscale phenomena, fine-scale models can be prohibitively expensive, whereas coarser models, although cheaper, often lack sufficient detail to accurately represent the phenomenon at hand. Spatial hybrid methods couple two or more of these representations in order to improve efficiency without compromising accuracy. In this paper, we present a novel spatial hybrid method, which we call the auxiliary region method (ARM), which couples PDE- and Brownian-based representations of reaction–diffusion systems. Numerical PDE solutions on one side of an interface are coupled to Brownian-based dynamics on the other side using compartment-based ‘auxiliary regions’. We demonstrate that the hybrid method is able to simulate reaction–diffusion dynamics for a number of different test problems with high accuracy. Furthermore, we undertake error analysis on the ARM which demonstrates that it is robust to changes in the free parameters in the model, where previous coupling algorithms are not. In particular, we envisage that the method will be applicable for a wide range of spatial multi-scales problems including filopodial dynamics, intracellular signalling, embryogenesis and travelling wave phenomena.
Selecting Representative Examples for Program Synthesis

Program synthesis is a class of regression problems where one seeks a solution, in the form of a source-code program, mapping the inputs to their corresponding outputs exactly. Due to its precise and combinatorial nature, program synthesis is commonly formulated as a constraint satisfaction problem, where input-output examples are encoded as constraints and solved with a constraint solver. A key challenge of this formulation is scalability: while constraint solvers work well with a few well-chosen examples, a large set of examples can incur significant overhead in both time and memory. We describe a method to discover a subset of examples that is both small and representative: the subset is constructed iteratively, using a neural network to predict the probability of unchosen examples conditioned on the chosen examples in the subset, and greedily adding the least probable example. We empirically evaluate the representativeness of the subsets constructed by our method, and demonstrate such subsets can significantly improve synthesis time and stability.
Toward Diverse Text Generation with Inverse Reinforcement Learning

Text generation is a crucial task in NLP. Recently, several adversarial generative models have been proposed to improve the exposure bias problem in text generation. Though these models gain great success, they still suffer from the problems of reward sparsity and mode collapse. In order to address these two problems, in this paper, we employ inverse reinforcement learning (IRL) for text generation. Specifically, the IRL framework learns a reward function on training data, and then an optimal policy to maximum the expected total reward. Similar to the adversarial models, the reward and policy function in IRL are optimized alternately. Our method has two advantages: (1) the reward function can produce more dense reward signals. (2) the generation policy, trained by ``entropy regularized'' policy gradient, encourages to generate more diversified texts. Experiment results demonstrate that our proposed method can generate higher quality texts than the previous methods.
The Rho regulator Myosin IXb enables nonlymphoid tissue seeding of protective CD8+ T cells

T cells are actively scanning pMHC-presenting cells in lymphoid organs and nonlymphoid tissues (NLTs) with divergent topologies and confinement. How the T cell actomyosin cytoskeleton facilitates this task in distinct environments is incompletely understood. Here, we show that lack of Myosin IXb (Myo9b), a negative regulator of the small GTPase Rho, led to increased Rho-GTP levels and cell surface stiffness in primary T cells. Nonetheless, intravital imaging revealed robust motility of Myo9b−/− CD8+ T cells in lymphoid tissue and similar expansion and differentiation during immune responses. In contrast, accumulation of Myo9b−/− CD8+ T cells in NLTs was strongly impaired. Specifically, Myo9b was required for T cell crossing of basement membranes, such as those which are present between dermis and epidermis. As consequence, Myo9b−/− CD8+ T cells showed impaired control of skin infections. In sum, we show that Myo9b is critical for the CD8+ T cell adaptation from lymphoid to NLT surveillance and the establishment of protective tissue–resident T cell populations.
Active Classification of Large 3D Shape Collection

To efficiently and accurately classify a large 3D shape collection, this paper proposes a novel interactive system by incorporating active learning, online learning and user intervention. Given a shape collection, our system iteratively alternates the interactive annotation and verification until all the shapes are classified. The main advantage is that it provides faster interactive classification rates than alternative approaches. Our system achieves this goal by a unified active learning algorithm that selects the shapes to be annotated or verified, which requires a probability model for simulating the time cost of human input during manual intervention. After manually classifying these selected shapes, we use an extended soft confidence-weighted learning method to update the classifier incrementally and efficiently for the subsequent active selection and shape classification in turn. Experimental results demonstrated the effectiveness of the proposed method.
A hybrid model for Chinese-English machine translation

The paper presents a hybrid model integrating an example based (EB) approach with a knowledge base methodology in support of Chinese-English machine translation (MT). The EB approach normally performs translation by referring to similar source sentences in the example base. It can be employed at different stages of MT including source language analysis, source to target transformation and target language generation. The proposed MT model brings together knowledge in two forms: rules and examples. This system is intended for domains that are understood well but not perfectly. Expert and heuristic knowledge in the form of rules are incorporated to provide a skeletal method for solving problems. Examples are then used to flesh out the method by covering idiosyncrasies and special cases that are not anticipated by the rules. In addition to a reasonably accurate and efficient set of rules to serve as a starting point for problem solving, the system is provided with some specific similarity measurement for critical analysis of Chinese sentences. This is based on word grammatical features and integrated with some dynamic mechanism to improve flexibility and robustness during the translation process.
Innovation diffusion for renewable energy technologies

We present an analysis of innovation diffusion models for solar energy technologies based on the spread of information in real-world social networks. Three dynamic innovation diffusion networks are simulated through diverse configurations of the Barabási Albert model with a linear threshold approach to information spread, using a dataset of Twitter messages about solar and renewable energy as reference information diffusion network. The characteristics of the simulated dynamic innovation diffusion networks (e.g. number of informed nodes) are then compared with those of the reference information diffusion network to establish which innovation diffusion model yields the best fit. The results of this analysis provide a solution for the development of the innovation diffusion component for renewable energy adoption models. We demonstrate the feasibility of including the innovation diffusion factor into an agent-based model of the solar PV market, which is applied to Qatar market.
Mixed-Signal IC With Pulse Width Modulation Wireless Telemetry for Implantable Cardiac Pacemakers in 0.18-μm CMOS

A low-power mixed-signal IC for implantable pacemakers is presented. The proposed system features three independent intracardiac signal readout channels with pulse-width-modulated outputs. Also, the proposed system is capable of measuring the amplitude and phase of the bioimpedance with pulse-width-modulated outputs for use in rate adaptive pacemakers. Moreover, a stimulation system is embedded, offering 16 different amplitudes from 1 to 7.8 V. A backscattering transmitter transfers the output signals outside the body with very little power consumption. The proposed low-power mixed-signal IC is fabricated in a 0.18-μm HV CMOS process and occupies 2.38 mm2. The biopotential channels extract the heart signals with 9.2 effective number of bits and the bioimpedance channels measure the amplitude and phase of the heart impedance with 1.35 Ωrms accuracy. The complete IC consumes only 4.2 μA from a 1-V power supply.
Energy-Efficient UAV Communication with Multiple GTs Based on Trajectory Optimization

Wireless communications with unmanned aerial vehicles (UAVs) is a promising technology offering potential high mobility and low cost. This paper studies a UAV-enabled communication system, in which a fixed-wing UAV is deployed to collect information from a group of distributed ground terminals (GTs). Considering the requirements for quality of service (QoS) (i.e., the throughput of each GT is above a given threshold) and GT scheduling, we maximize the energy efficiency (EE) of the UAV in bits/Joule by optimizing the UAV’s flight trajectory. In this paper, a mixed integer nonconvex optimization problem is formulated. As that is difficult to solve, we divide the formulated problem into two subproblems and apply standard linear programming (LP) and successive convex optimization techniques. We further propose an efficient iterative algorithm that jointly optimizes GT scheduling and the UAV’s trajectory. Moreover, we set two special cases as benchmarks to measure the performance of the proposed design. The numerical results show that our proposed design achieves much better performance than the other two benchmark designs.
Pattern-Identified Online Task Scheduling in Multitier Edge Computing for Industrial IoT Services

In smart manufacturing, production machinery and auxiliary devices, referred to as industrial Internet of things (IIoT), are connected to a unified networking infrastructure for management and command deliveries in a precise production process. However, providing autonomous, reliable, and real-time offloaded services for such a production is an open challenge since these IIoT devices are assumed lightweight embedded platforms with limited computing performance. In this paper, we propose a pattern-identified online task scheduling (PIOTS) mechanism for the networking infrastructure, where multitier edge computing is provided, in order to handle the offloaded tasks in real time. First, historical IIoT task patterns in every timeslot are used to train a self-organizing map (SOM), which represents the features of the task patterns within defined dimensions. Consequently, offline task scheduling among edge computing-enabled entities is performed on the set of all SOM neurons using the Hungarian method to determine the expected optimal task assignments. In real-time context, whenever a task arrives at the infrastructure, the expected optimal assignment for the task is scheduled to the appropriate edge computing-enabled entity. Numerical simulation results show that the proposed PIOTS mechanism overcomes existing solutions in terms of computation performance and service capability.
Analysis of thermal effects in integrated radio transmitters

The combination of compact size and low efficiency at mm-waves has turned heat dissipation into a fundamental constraint for design of multi-antenna radios. This paper describes methods for analysis of thermal effects at both at the circuit, system and component level. The first part describes how thermal analysis can be combined with advanced RF modeling techniques to predict self-heating and thermal coupling in multi-antenna transmitter systems. Experimental methods are then used to determine thermal coupling effects occurring at chip level. Various experimental and theoretical results, using MIMO amplifiers and GaN HEMTs, are used to demonstrate the methods in realistic application scenarios.
Tuning of Customer Relationship Management (CRM) via Customer Experience Management (CEM) using Sentiment Analysis on Aspects Level

This study proposes a framework that combines a supervised machine learning and a semantic orientation approach to tune Customer Relationship Management (CRM) via Customer Experience Management (CEM). The framework extracts data from social media first and then integrates CRM and CEM by tuning and optimising CRM to reflect the needs and expectations of users on social media. In other words, in order to reduce the gap between the users’ predicted opinions in CRM and their opinions on social media, the existing data from CEM will be applied to determine the similar behavioural patterns of customers towards similar outcomes within CRM. CRM data and extracted data from social media will be consolidated by the unsupervised data mining method (association). The framework will lead to a quantitative approach to uncover relationships between the extracted data from social media and the CRM data. The results show that changing some aspects of the e-learning criteria that were required by students in their social media posts can help to enhance the classification accuracy in the learning management system (LMS) data and to understand more students’ studying statuses. Furthermore, the results show matching between students’ opinions in CRM and CEM, especially in the negative and neutral classes.