Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Social Group Communication with Multi-Agent Imitation Learning

In crowded social scenarios with a myriad of external stimuli, human brains exhibit a natural ability to filter out irrelevant information and narrowly focus their attention. In the midst of multiple groups of people, humans use such sensory gating to effectively further their own group's interactional goals. In this work, we consider the design of a policy network to model multi-group multi-person communication. Our policy takes as input the state of the world such as an agent's gaze direction, body pose of other agents or history of past actions, and outputs an optimal action such as speaking, listening or responding (communication modes). Inspired by humans' natural neurobiological filtering process, a central component of our policy network design is an information gating function, termed the Kinesic-Proxemic-Message Gate (KPM-Gate), that models the ability of an agent to selectively gather information from specific neighboring agents. The degree of influence of a neighbor is based on dynamic non-verbal cues such as body motion, head pose (kinesics) and interpersonal space (proxemics). We further show that the KPM-Gate can be used to discover social groups using its natural interpretation as a social attention mechanism. We pose the communication policy learning problem as a multi-agent imitation learning problem. We learn a single policy shared by all agents under the assumption of a decentralized Markov decision process. We term our policy network as the Multi-Agent Group Discovery and Communication Mode Network (MAGDAM network), as it learns social group structure in addition to the dynamics of group communication. Our experimental validation on both synthetic and real world data shows that our model is able to both discover social group structure and learn an accurate multi-agent communication policy.
Hedging the Drift: Learning to Optimize under Non-Stationarity

We introduce data-driven decision-making algorithms that achieve state-of-the-art dynamic regret bounds for a collection of nonstationary stochastic bandit settings. These settings capture applications such as advertisement allocation, dynamic pricing, and traffic network routing in changing environments. We show how the difficulty posed by the (unknown a priori and possibly adversarial) nonstationarity can be overcome by an unconventional marriage between stochastic and adversarial bandit learning algorithms. Beginning with the linear bandit setting, we design and analyze a sliding window-upper confidence bound algorithm that achieves the optimal dynamic regret bound when the underlying variation budget is known. This budget quantifies the total amount of temporal variation of the latent environments. Boosted by the novel bandit-over-bandit framework that adapts to the latent changes, our algorithm can further enjoy nearly optimal dynamic regret bounds in a (surprisingly) parameter-free manner. We extend our results to other related bandit problems, namely the multiarmed bandit, generalized linear bandit, and combinatorial semibandit settings, which model a variety of operations research applications. In addition to the classical exploration-exploitation trade-off, our algorithms leverage the power of the “forgetting principle” in the learning processes, which is vital in changing environments. Extensive numerical experiments with synthetic datasets and a dataset of an online auto-loan company during the severe acute respiratory syndrome (SARS) epidemic period demonstrate that our proposed algorithms achieve superior performance compared with existing algorithms. This paper was accepted by George J. Shanthikumar for the Management Science Special Issue on Data-Driven Prescriptive Analytics.
Blockchain Meets Database: Design and Implementation of a Blockchain Relational Database

In this paper, we design and implement the first-ever decentralized replicated relational database with blockchain properties that we term blockchain relational database . We highlight several similarities between features provided by blockchain platforms and a replicated relational database, although they are conceptually different, primarily in their trust model. Motivated by this, we leverage the rich features, decades of research and optimization, and available tooling in relational databases to build a blockchain relational database. We consider a permissioned blockchain model of known, but mutually distrustful organizations each operating their own database instance that are replicas of one another. The replicas execute transactions independently and engage in decentralized consensus to determine the commit order for transactions. We design two approaches, the first where the commit order for transactions is agreed upon prior to executing them, and the second where transactions are executed without prior knowledge of the commit order while the ordering happens in parallel. We leverage serializable snapshot isolation (SSI) to guarantee that the replicas across nodes remain consistent and respect the ordering determined by consensus, and devise a new variant of SSI based on block height for the latter approach. We implement our system on PostgreSQL and present detailed performance experiments analyzing both approaches.
Synchronization for Diffusion-Based Molecular Communication Systems via Faster Molecules

In this paper, we address the symbol synchronization issue in molecular communication via diffusion (MCvD). Symbol synchronization among chemical sensors and nanomachines is one of the critical challenges to manage complex tasks in the nanonetworks with molecular communication (MC). As in diffusion-based MC, most of the molecules arrive at the receptor closer to the start of the symbol duration, the wrong estimation of the start of the symbol interval leads to high symbol detection error. By utilizing two types of molecules with different diffusion coefficients we propose a synchronization technique for MCvD. Moreover, we evaluate the symbol-error-rate performance under the proposed symbol synchronization scheme for equal and non-equal symbol duration in MCvD systems.
Unsupervised Domain-Specific Deblurring via Disentangled Representations

Image deblurring aims to restore the latent sharp images from the corresponding blurred ones. In this paper, we present an unsupervised method for domain-specific, single-image deblurring based on disentangled representations. The disentanglement is achieved by splitting the content and blur features in a blurred image using content encoders and blur encoders. We enforce a KL divergence loss to regularize the distribution range of extracted blur attributes such that little content information is contained. Meanwhile, to handle the unpaired training data, a blurring branch and the cycle-consistency loss are added to guarantee that the content structures of the deblurred results match the original images. We also add an adversarial loss on deblurred results to generate visually realistic images and a perceptual loss to further mitigate the artifacts. We perform extensive experiments on the tasks of face and text deblurring using both synthetic datasets and real images, and achieve improved results compared to recent state-of-the-art deblurring methods.
Improving Cross-Domain Chinese Word Segmentation with Word Embeddings

Cross-domain Chinese Word Segmentation (CWS) remains a challenge despite recent progress in neural-based CWS. The limited amount of annotated data in the target domain has been the key obstacle to a satisfactory performance. In this paper, we propose a semi-supervised word-based approach to improving cross-domain CWS given a baseline segmenter. Particularly, our model only deploys word embeddings trained on raw text in the target domain, discarding complex hand-crafted features and domain-specific dictionaries. Innovative subsampling and negative sampling methods are proposed to derive word embeddings optimized for CWS. We conduct experiments on five datasets in special domains, covering domains in novels, medicine, and patent. Results show that our model can obviously improve cross-domain CWS, especially in the segmentation of domain-specific noun entities. The word F-measure increases by over 3.0% on four datasets, outperforming state-of-the-art semi-supervised and unsupervised cross-domain CWS approaches with a large margin. We make our data and code available on Github.
Multi-Agent Learning in Network Zero-Sum Games is a Hamiltonian System

Zero-sum games are natural, if informal, analogues of closed physical systems where no energy/utility can enter or exit. This analogy can be extended even further if we consider zero-sum network (polymatrix) games where multiple agents interact in a closed economy. Typically, (network) zero-sum games are studied from the perspective of Nash equilibria. Nevertheless, this comes in contrast with the way we typically think about closed physical systems, e.g., Earth-moon systems which move perpetually along recurrent trajectories of constant energy. We establish a formal and robust connection between multi-agent systems and Hamiltonian dynamics -- the same dynamics that describe conservative systems in physics. Specifically, we show that no matter the size, or network structure of such closed economies, even if agents use different online learning dynamics from the standard class of Follow-the-Regularized-Leader, they yield Hamiltonian dynamics. This approach generalizes the known connection to Hamiltonians for the special case of replicator dynamics in two agent zero-sum games developed by Hofbauer. Moreover, our results extend beyond zero-sum settings and provide a type of a Rosetta stone (see e.g. Table 1) that helps to translate results and techniques between online optimization, convex analysis, games theory, and physics.
Robust joint stem detection and crop‐weed classification using image sequences for plant‐specific treatment in precision farming

Conventional farming still relies on large quantities of agrochemicals for weed management which have several negative side‐effects on the environment. Autonomous robots offer the potential to reduce the amount of chemicals applied, as robots can monitor and treat each plant in the field individually and thereby circumventing the uniform chemical treatment of the whole field. Such agricultural robots need the ability to identify individual crops and weeds in the field using sensor data and must additionally select effective treatment methods based on the type of weed. For example, certain types of weeds can only be effectively treated mechanically due to their resistance to herbicides, whereas other types can be treated trough selective spraying. In this article, we present a novel system that provides the necessary information for effective plant‐specific treatment. It estimates the stem location for weeds, which enables the robots to perform precise mechanical treatment, and at the same time provides the pixel‐accurate area covered by weeds for treatment through selective spraying. The major challenge in developing such a system is the large variability in the visual appearance that occurs in different fields. Thus, an effective classification system has to robustly handle substantial environmental changes including varying weed pressure, various weed types, different growth stages, changing visual appearance of the plants and the soil. Our approach uses an end‐to‐end trainable fully convolutional network that simultaneously estimates plant stem positions as well as the spatial extent of crop plants and weeds. It jointly learns how to detect the stems and the pixel‐wise semantic segmentation and incorporates spatial information by considering image sequences of local field strips. The jointly learned feature representation for both tasks furthermore exploits the crop arrangement information that is often present in crop fields. This information is considered even if it is only observable from the image sequences and not a single image. Such image sequences, as typically provided by robots navigating over the field along crop rows, enable our approach to robustly estimate the semantic segmentation and stem positions despite the large variations encountered in different fields. We implemented and thoroughly tested our approach on images from multiple farms in different countries. The experiments show that our system generalizes well to previously unseen fields under varying environmental conditions—a key capability to deploy such systems in the real world. Compared to state‐of‐the‐art approaches, our approach generalizes well to unseen fields and not only substantially improves the stem detection accuracy, that is, distinguishing crop and weed stems, but also improves the semantic segmentation performance.
Fire-Induced Changes in Soil and Implications on Soil Sorption Capacity and Remediation Methods

Vegetation changes caused by fire events are visible instantly but changes in soils are less apparent, and could be short-term, long-term or permanent in nature. Research has shown that soils undergo changes in their mineralogical, geochemical, physico-chemical and biological properties after a fire event that may vary depending on the intensity and duration of the fire, and the properties of the soil. Some of these properties make significant contributions towards soil’s ability to sorb contaminants. Changes in these properties could affect soil sorption complex and the effectiveness of remediation methods used to clean these soils when contaminated. This review synthesizes available information on fire-induced changes in soil properties affecting soil sorption and the factors which dictate these changes. The implications of changes in these properties on the soil’s natural attenuation capacity and choice of remediation method to clean up fire-affected contaminated soils are also discussed.
Exploring credentials of Wi‐Fi sensors as a complementary transport data: an Indian experience

This study investigates the performance of Wi-Fi sensors positioned at varying lateral and vertical positions on data collection efficiency under prevailing traffic conditions in India. Investigation revealed that penetration rate (a measure of performance of sensor) is significantly influenced by height and lateral position of the sensor. Time gap (time-headway) between successive detection for varying height and the lateral position was modelled using statistical distribution to investigate, how height and lateral positions of sensor influences its performance. It was observed that the variation in time headway causes variation in the number of detections and hence, affects the performance of the sensor. A new performance measure named time-to-detection was developed from the observed distribution of headways and it was observed that time-to-detection is significantly influenced by the sensor location. Further, in addition to lateral and vertical positions, stream speed was also observed to have a significant effect on the sensor performance. Field observed headway and headway values computed using sensor data were thereafter correlated to optimise the sensor location in terms of its vertical and lateral positions. Based on the headway distribution analysis, the study recommends installing a Wi-Fi sensor mainly at ground level along the median to capture temporal variations in traffic parameters more effectively.
Sustainable Economic Development and Greenhouse Gas Emissions: The Dynamic Impact of Renewable Energy Consumption, GDP, and Corruption

The paper investigates the relationships between economic, social, and environmental dimensions of sustainable development. GDP growth represents the main economic dimension, greenhouse gas (GHG) emissions and renewable energy consumption the environmental dimension, and corruption the social dimension of sustainable development. The investigation of these relationships is based on the concept of the Environmental Kuznets Curve hypothesis about the non-linear relationship between economic growth and environmental pollution. The authors used the panel data of EU countries and Ukraine for 2000–2016 years from the Eurostat database. The obtained results confirmed the Environmental Kuznets curve hypothesis for the EU and Ukraine. All the indicators were statistically significant at 1% and 5% levels. The findings proved that increasing renewable energy (RE) by 1% led to a decline of GHG in the interval (0.166103, 0.220551), and аn increase of the Control of Corruption Index by 1% provoked a decline of GHG by 0.88%. The conducted study enabled the authors to conclude that Ukraine needs to increase the GDP level per capita given the economy diversification and via the introduction of more effective and “clean” production technologies.
Unemployment and Imprisonment

In recent congressional testimony, Professor M. Harvey Brenner referred to a number of studies which attempted to gauge the relationship between prison admissions and the economy. ' Specifically, between 1967 and 1974, Cox and Carr observed that the Georgia prison population fluctuated with changes in the unemployment rate.2 Similarly, Brenner and Jankovic found that admissions to state prisons correlated positively with the unemployment rate.3 Indeed, Brenner testified that for every 1 percent increase in the unemployment rate, state prison admissions rose by 3,340 inmates, even after controlling for the effects of time, the percent of males in the population, per capita income, and inflation. However, Brenner and other researchers have failed to control for sentencing practices which could explain the variation in prison admissions. This note examines whether sentencing practices, rather than unemployment, explain these fluctuations in prison population. In an effort to test the "sentencing" thesis, this note replicated an internal staff study by Colin Frank, a former employee of the Bureau of Prisons.4 Frank's study indicated that the quarterly unemployment rate of males age twenty and over explained 59 percent of the variation in the federal prison population sentenced between 1952 and 1974.5 However, Frank's study failed to introduce sentencing variables into the analysis. Data from the Administrative Office of the
Changing disabling places

This thesis documents, 'develops and demonstrates a novel form of praxis in relation to disability in Scottish Higher Education. 'Praxis', as I use the term in this thesis, refers to an ongoing, irreducible, collective process through which is enacted, in one and the same process: 'knowledgementing' (the construction and legitimation of knowledge claims); 'radical reflexivity' (the bringing to awareness and critical problematisation of interests served by what is thought, said and done by all relevant parties); and 'ideologically progressive social action' (the pursuit of emancipatory process and just outcomes and the contesting of'external and internal' institutional oppression). The meaning of praxis is explicated in this thesis and demonstrated in action with reference to disability in Scottish Higher Education. Particular attention is paid to explicating and demonstrating the conceptual unity of praxis and the interconnectivity in actuality of the practices, procedures and policies which disable in assemblages or apparatuses, as Foucault uses the terms. The interconnectivity of the praxis is, it is claimed, the key to unlocking the interconnectivity of the assemblages which produce and maintain disability in Scottish Higher Education. The thesis traces the connections between the various elements of the assemblage producing a novel account (and new knowledges) which, it is claimed, could only have been derived as a result of the praxis and which can also account for the knowledges presented in previous research into disability in British Higher Education, locating these studies as part of the disabling assemblage. The thesis concludes by drawing out wider implications of praxis for conventional research, for psychology and social science.
Minimum Short Circuit Ratio Requirement for MMC-HVDC Systems Based on Small-Signal Stability Analysis

This paper determines the minimum short circuit ratio (SCR) requirement for a modular multilevel converter based high-voltage direct current (MMC-HVDC) transmission systems. Firstly, a simplified model of MMC is introduced; the MMC is represented by its AC and DC side equivalent circuit. Next, by linearizing the MMC subsystem and the DC network subsystem, the deduction of the small-signal models of MMC subsystem, the small-signal model of the DC network and MMC-HVDC are carried out successively. Thirdly, the procedure for determining the minimum SCR requirement of MMC-HVDC is described. Finally, case studies are performed on a two-terminal MMC-HVDC system under four typical control schemes. The results show that the restraint factors for the rectifier MMC is predominantly the voltage safety limit constraint, and the restraint factors for the inverter MMC are mainly the phase locked loop (PLL) or the outer reactive power controller. It is suggested that the minimum SCR requirement for the sending and the receiving systems should be 2.0 and 1.5 in the planning stage.
Activation of Carbon Porous Paper for Alkaline Alcoholic Fuel Cells

In this study, various treatment methods to increase the reactivity of carbon porous electrodes for alkaline alcoholic fuel cells were investigated with commercially available carbon papers to understand the characteristic electrochemical behaviors of the treated carbon electrodes and to find the best method to enhance the cell performance. Effects of thermal treatment, potassium hydroxide (KOH) treatment, N2 doping, and reaction-area control via a multi-layered structure were compared in the cell-based tests, and a huge improvement in the cell performance (i.e., 64% increase of open circuit voltage (OCV) and 320% increase of max power density) was found from the thermal-treated four-layered carbon porous electrode. The results were compared with those from platinum on carbon (Pt/C)-based cells, and a discussion on the direction of research in the future was conducted. The results of this study are expected to provide key guidelines for alcoholic fuel cell (AFC) developers to develop cost-effective AFC with a carbon electrode.
Seismic performance of a nonlinear energy sink with negative stiffness and sliding friction

In this paper, to enhance passive targeted energy transfer, a novel nonlinear energy sink (NES) system is developed. The NES system integrates negative stiffness obtained through geometrical nonlinearities, friction, and sliding mass. Considering a one‐story shear frame, governing equations of the NES system are developed and substantiated through experimental work. The governing equations of the system were subsequently used to select the optimal design parameters. The experimental results validate the numerical predictions that a significant fraction of energy introduced directly to the primary structure by seismic excitation is transferred to the present NES and dissipated. The seismic performance of the present NES is compared with those of the linear tuned mass damper and the cubic NES. The comparison shows that the attenuation observed under the present NES control is competitive and is robust with respect to variation of the primary structural stiffness. Numerically, further sensitivity analysis was carried out to investigate the effect of peak ground acceleration values and stiffness ratio.