Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Abstraction in ASP with an Application on Reasoning about Agent Policies

ASP programs are a convenient tool for problem solving, whereas with large problem instances the size of the state space can be prohibitive. We consider abstraction as a means of over-approximation and introduce a method to automatically abstract (possibly non-ground) ASP programs that preserves their structure, while reducing the size of the problem. One particular application case is the problem of defining declarative policies for reactive agents and reasoning about them, which we illustrate on examples.
Parallel Implementation of Simultaneous Perturbation Stochastic Approximation with Adaptive Step Sizes

The simultaneous perturbation stochastic approximation(SPSA) is an iterative gradient-free optimization algorithm. This algorithm approximates the optimal solution by estimating the gradient of the loss function and uses only 2 values of the loss function to estimate the gradient which is independent of the dimension of the problem. But for the high dimensional problem with large-scale, the convergence of classical SPSA algorithm is slow, even can not converge to the global minimum value. In order to accelerate the convergence of SPSA algorithm and improve the precision of the optimum solution, this paper proposes an improvement of SPSA algorithm based on the adaptive step sizes, which can adjust the step size of each iteration according to the gradient direction. The modified algorithm is implemented by Message Passing Interface (MPI) parallel in each iteration to widen the search scope for the better descending direction in order to accelerate the convergence. Numerical experiments of several classical nonlinear optimization problems shows that the modified SPSA algorithm can accelerate the convergence of nonlinear optimization problems and find the global extremum point of high-dimension for some optimization problems which the SPSA algorithm can not find. It proves that the modified algorithm with adaptive step sizes outperforms the classical SPSA algorithm.
Integrated compression of vehicle spatio‐temporal trajectories under the road stroke network constraint

With fast growth of all kinds of trajectory datasets, how to effectively manage the trajectory data of moving objects has received a lot of attention. This study proposes a spatio‐temporal data integrated compression method of vehicle trajectories based on stroke paths coding compression under the road stroke network constraint. The road stroke network is first constructed according to the principle of continuous coherence in Gestalt psychology, and then two types of Huffman tree—a road strokes Huffman tree and a stroke paths Huffman tree—are built, based respectively on the importance function of road strokes and vehicle visiting frequency of stroke paths. After the vehicle trajectories are map matched to the spatial paths in the road network, the Huffman codes of the road strokes and stroke paths are used to compress the trajectory spatial paths. An opening window algorithm is used to simplify the trajectory temporal data depicted on a time–distance polyline by setting the maximum allowable speed difference as the threshold. Through analysis of the relative spatio‐temporal relationship between the preceding and latter feature tracking points, the spatio‐temporal data of the feature tracking points are all converted to binary codes together, accordingly achieving integrated compression of trajectory spatio‐temporal data. A series of comparative experiments between the proposed method and representative state‐of‐the‐art methods are carried out on a real massive taxi trajectory dataset from five aspects, and the experimental results indicate that our method has the highest compression ratio. Meanwhile, this method also has favorable performance in other aspects: compression and decompression time overhead, storage space overhead, and historical dataset training time overhead.
Connecting Electroweak Symmetry Breaking and Flavor: A Light Dilaton D and a Sequential Heavy Quark Doublet Q

The 125 GeV boson is quite consistent with the Higgs boson of the Standard Model (SM), but there is a challenge from Anderson as to whether this particle is in the Lagrangian. As Large Hadron Collider (LHC) Run 2 enters its final year of running, we ought to reflect and make sure we have gotten everything right. The ATLAS and CMS combined Run 1 analysis claimed a measurement of 5. 4σ vector boson fusion (VBF) production which is consistent with SM, which seemingly refutes Anderson. However, to verify the source of electroweak symmetry breaking (EWSB), we caution that VBF measurement is too important for us to be imprudent in any way, and gluon–gluon fusion (ggF) with similar tag jets must be simultaneous measured, which should be achievable in LHC Run 2. The point is to truly test the dilaton possibility—the pseudo-Goldstone boson of scale invariance violation. We illustrate EWSB by dynamical mass generation of a sequential quark doublet (Q) via its ultrastrong Yukawa coupling and argue how this might be consistent with a 125 GeV dilaton, D. The ultraheavy 2mQ≳4–5 TeV scale explains the absence of New Physics so far, while the mass generation mechanism shields us from the UV theory for the strong Yukawa coupling. Collider and flavor physics implications are briefly touched upon. Current Run 2 analyses show correlations between the ggF and VBF measurements, but the newly observed tt¯H production at LHC poses a challenge.
Stories to (Re)Name By: Relationally Living and Narratively Inquiring Within the Multilayered Midst

Re)telling and (re)living our stories within our response community, we became more wakeful (Greene, 1995) to processes of (re)naming that contoured ways in which we were storied in diverse worlds (Lugones, 1987). We also gained a deeper appreciation as to how we, in turn, (re)named ourselves and others. Narratively inquiring (Clandinin & Connelly, 2000) into the stories of our names, we autobiographically illuminate our understandings of our (shifting and multiple) selves and experiences and make visible educative possibilities for educators and others looking to compose spaces of mutual care and understanding within the many worlds we co-inhabit.
Direct Wind Power Forecasting Using a Polynomial Decomposition of the General Differential Equation

The wind power is primarily induced by the local wind speed, whose accurate daily forecasts are important for planning and utilization of the unstable power generation and its integration into the electrical grid. The main problem of the wind speed or direct output power forecasting is its intermittent nature due to the high correlation with chaotic large-scale pattern atmospheric circulation processes, which together with local characteristics and anomalies largely influence its temporal flow. Numerical global weather systems solve sets of differential equations to describe the time change of each three-dimensional grid cell in several atmospheric layers. They can provide as a rule only rough short-term surface wind speed prognoses, which are not entirely adequate to specific local conditions, e.g., the wind farm siting, surrounding terrain, and ground level (hub height). Statistical methods using historical observations can particularize the daily forecasts or provide independent predictions in several hours. Extended polynomial networks can produce fraction substitution sum terms in all the nodes, in consideration of data samples, to decompose and substitute for the general linear partial differential equation, being able to describe the local atmospheric dynamics. The designed method using the inverse Laplace transformation aims at the formation of stand-alone spatial derivative models, which can represent current local weather conditions for a trained input–output time shift to predict the daily wind power up to 12 h ahead. The proposed intraday multistep predictions are more precise than those based on middle-term numerical forecasts or adaptive intelligence techniques using local time series, which are worthless beyond a few hours.
An Adaptive Control Strategy for Virtual Synchronous Generator

The adoption of the virtual synchronous generator (VSG) has attracted wide attention because of the equivalent operating mechanism as a synchronous generator, providing a feasible scheme for the distributed generation connected to utility grid. However, the delivered power and frequency of the VSG may easily oscillate when the dramatic power fluctuation occurred in the distribution generation system. Fortunately, the oscillation can be damped though adjusting the rotating inertia and damping coefficient. Thus, to investigate the influences of parameters perturbation on the active power and frequency for a VSG, the small-signal model is derived and the dynamic performances are analyzed in detail. Then, based on the results of parameters analysis, an adaptive control strategy is proposed in this paper. And an optimal damping ratio can be maintained throughout the whole process of operation to suppress the oscillation of power and frequency. Therefore, the dynamic performances of the VSG are enhanced since the dynamic indexes such as the response time and overshoots are optimized. In the end, a prototype of single-phase VSG has been built and the proposed adaptive control strategy has been verified through experimental results.
Selective Depletion of CREB in Serotonergic Neurons Affects the Upregulation of Brain-Derived Neurotrophic Factor Evoked by Chronic Fluoxetine Treatment

Neurotrophic factors are regarded as crucial regulatory components in neuronal plasticity and are postulated to play an important role in depression pathology. The abundant expression of brain-derived neurotrophic factor (BDNF) in various brain structures seems to be of particular interest in this context, as downregulation of BDNF is postulated to be correlated with depression and its upregulation is often observed after chronic treatment with common antidepressants. It is well-known that BDNF expression is regulated by cyclic AMP response element-binding protein (CREB). In our previous study using mice lacking CREB in serotonergic neurons (Creb1TPH2CreERT2 mice), we showed that selective CREB ablation in these particular neuronal populations is crucial for drug-resistant phenotypes in the tail suspension test observed after fluoxetine administration in Creb1TPH2CreERT2 mice. The aim of this study was to investigate the molecular changes in the expression of neurotrophins in Creb1TPH2CreERT2 mice after chronic fluoxetine treatment, restricted to the brain structures implicated in depression pathology with profound serotonergic innervation including the prefrontal cortex (PFC) and hippocampus. Here, we show for the first time that BDNF upregulation observed after fluoxetine in the hippocampus or PFC might be dependent on the transcription factor CREB residing, not within these particular structures targeted by serotonergic projections, but exclusively in serotonergic neurons. This observation may shed new light on the neurotrophic hypothesis of depression, where the effects of BDNF observed after antidepressants in the hippocampus and other brain structures were rather thought to be regulated by CREB residing within the same brain structures. Overall, these results provide further evidence for the pivotal role of CREB in serotonergic neurons in maintaining mechanisms of antidepressant drug action by regulation of BDNF levels.
MAP-Based Active User and Data Detection for Massive Machine-Type Communications

With the advent of the Internet of things, massive machine-type communications (mMTC) have become one of the most important requirements for next generation communication systems. In the mMTC scenarios, grant-free nonorthogonal multiple access on the transmission side and compressive sensing-based multi-user detection (CS-MUD) on the reception side are a promising solution because many users sporadically transmit small data packets at low rates. In this paper, we propose a novel CS-MUD algorithm for the active user and data detection in the mMTC systems. The proposed scheme consists of the maximum a posteriori probability (MAP) based active user detector (MAP-AUD) and the MAP-based data detector (MAP-DD). By exchanging the extrinsic information between MAP-AUD and MAP-DD, the proposed algorithm improves the active user detection performance and the reliability of the data detection. In addition, we extend the proposed algorithm to exploit group sparsity. By jointly processing the multiple received data with common activity, the proposed algorithm dramatically enhances the active user detection performance. We show by numerical experiments that the proposed algorithm achieves a substantial performance gain over existing algorithms.
The Streptococcus pneumoniae yefM-yoeB and relBE Toxin-Antitoxin Operons Participate in Oxidative Stress and Biofilm Formation

Type II (proteic) toxin-antitoxin systems (TAs) are widely distributed among bacteria and archaea. They are generally organized as operons integrated by two genes, the first encoding the antitoxin that binds to its cognate toxin to generate a harmless protein–protein complex. Under stress conditions, the unstable antitoxin is degraded by host proteases, releasing the toxin to achieve its toxic effect. In the Gram-positive pathogen Streptococcus pneumoniae we have characterized four TAs: pezAT, relBE, yefM-yoeB, and phD-doc, although the latter is missing in strain R6. We have assessed the role of the two yefM-yoeB and relBE systems encoded by S. pneumoniae R6 by construction of isogenic strains lacking one or two of the operons, and by complementation assays. We have analyzed the phenotypes of the wild type and mutants in terms of cell growth, response to environmental stress, and ability to generate biofilms. Compared to the wild-type, the mutants exhibited lower resistance to oxidative stress. Further, strains deleted in yefM-yoeB and the double mutant lacking yefM-yoeB and relBE exhibited a significant reduction in their ability for biofilm formation. Complementation assays showed that defective phenotypes were restored to wild type levels. We conclude that these two loci may play a relevant role in these aspects of the S. pneumoniae lifestyle and contribute to the bacterial colonization of new niches.
Turkish Coreference Resolution

This paper presents the state-of-the-art results in Turkish coreference resolution (CR) which is a task of determining sets of mentions which identify the same real-world entity (e.g. a person, a place, a thing, an event). The proposed system uses support vector machines and solves the CR task with a mention-pair model that basically accepts mention couples and decides on whether they are coreferential with each other or not. The results are evaluated on Marmara Turkish Coreference Corpus by using well-known evaluation metrics (viz. MUC, B3, BLANC and LEA). The introduced approach obtains F1 scores of 90.68% (MUC), 86.89% (B3), 85.13% (BLANC) and 78.34% (LEA) yielding an improvement of 9.12, 16.06, 13.08 and 12.57 percentage points respectively over a recent baseline system on Turkish CR. The paper introduces the system setup (SVM parameters and negative sampling strategy) as well as the selected features and analyzes the impact of these features on the Turkish CR task.
Highly-Sensitive Microwave Sensors Based on Open Complementary Split Ring Resonators (OCSRRs) for Dielectric Characterization and Solute Concentration Measurement in Liquids

Differential permittivity sensors based on a pair of uncoupled microstrip lines, each one loaded with an open complementary split ring resonator (OCSRR), are proposed in this paper. The sensing principle is based on the measurement of the cross-mode insertion loss, very sensitive to asymmetric loading. Thus, by loading one of the OCSRRs with the reference sample, and the other one with the sample under test (SUT), the difference in the complex permittivity between both samples generates an asymmetry that gives rise to mode conversion. From the measurement of the cross-mode transmission coefficient, the dielectric properties of the SUT can be determined, provided those of the reference sample are well known. It is shown that by adding fluidic channels on top of the OCSRRs, the proposed sensor is useful for the measurement of the complex dielectric constant of liquids, and experimental results in mixtures of ethanol and deionized (DI) water and methanol in DI water, as a function of the ethanol/methanol content, are provided. Due to the high sensitivity of the proposed differential sensor to detect small perturbations (asymmetries), the structure is also of interest for the accurate measurement of solute concentrations in liquid solutions. In this paper, the structure is applied to monitor sodium content in aqueous solutions, and it is found that sodium concentrations as small as 0.25 g/L can be resolved.
TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game

Starcraft II (SC2) is widely considered as the most challenging Real Time Strategy (RTS) game. The underlying challenges include a large observation space, a huge (continuous and infinite) action space, partial observations, simultaneous move for all players, and long horizon delayed rewards for local decisions. To push the frontier of AI research, Deepmind and Blizzard jointly developed the StarCraft II Learning Environment (SC2LE) as a testbench of complex decision making systems. SC2LE provides a few mini games such as MoveToBeacon, CollectMineralShards, and DefeatRoaches, where some AI agents have achieved the performance level of human professional players. However, for full games, the current AI agents are still far from achieving human professional level performance. To bridge this gap, we present two full game AI agents in this paper - the AI agent TStarBot1 is based on deep reinforcement learning over a flat action structure, and the AI agent TStarBot2 is based on hard-coded rules over a hierarchical action structure. Both TStarBot1 and TStarBot2 are able to defeat the built-in AI agents from level 1 to level 10 in a full game (1v1 Zerg-vs-Zerg game on the AbyssalReef map), noting that level 8, level 9, and level 10 are cheating agents with unfair advantages such as full vision on the whole map and resource harvest boosting. To the best of our knowledge, this is the first public work to investigate AI agents that can defeat the built-in AI in the StarCraft II full game.
Attention and amplification in the hybrid media system: The composition and activity of Donald Trump’s Twitter following during the 2016 presidential election

Building on studies of the hybrid media system and attention economy, we develop the concept of amplification to explore how the activities of social media–based publics may enlarge the attention paid to a given person or message. We apply the concept to the 2016 US election, asking who constituted Donald Trump’s enormous Twitter following and how that following contributed to his success at attracting attention, including from the mainstream press. Using spectral clustering based on social network similarity, we identify key publics that constituted Trump’s Twitter following and demonstrate how particular publics amplified his social media presence in different ways. Our discussion raises questions about how algorithms “read” metrics to guide content on social media platforms, how journalists draw on social media metrics in their determinations of news value and worthiness, and how the process of amplification relates to possibilities of citizen action through digital communication.
Fast Decomposed Energy Flow in Large-Scale Integrated Electricity–Gas–Heat Energy Systems

In this paper, a new decomposing strategy is proposed to solve the power flow problem in the large-scale multienergy carrier (MEC) systems, including gas, electrical, and heating subnetworks. This strategy has been equipped with a novel noniterative method named holomorphic embedding (HE) to solve the energy flow of the electrical subnetwork. Moreover, it benefits from the less-computational graph method for solving the energy flows of the heating subnetwork. The HE method unlike initial-guess iterative methods guarantees to find the power flow solution, if there is a solution. In addition, it finds only the operational power flow solution without concern about the convergence of the solution. In the proposed strategy, the decomposing method decouples various energy flows of subnetworks without losing the major benefits of the simultaneous analysis of the subnetworks and losing accuracy. Moreover, the proposed decomposing strategy has more reliability and faster computation time than the Newton–Raphson technique. In order to demonstrate the efficiency and superiority of the proposed decomposing strategy on solving large-scale MEC systems, the strategy is tested on three large-scale case studies.
Retinoic acid receptor α expression exerts an anti-apoptosis effect on PC12 cells following oxygen-glucose deprivation

It has been established that the primary form of neuron death following hypoxic ischemic brain damage is apoptosis. Imbalances in the expression of genes in the B-cell lymphoma 2 (Bcl-2) family located in the mitochondrion, and in the expression of their encoded proteins, are key events in the mitochondrial apoptotic pathway, which lead to damage of cellular structure and function. The present study aimed to explore the regulatory effect of retinoic acid receptor α (RAR-α) on the apoptosis of PC12 cells induced by oxygen-glucose deprivation (OGD) in the retinoic acid signaling pathway. Recombinant adenovirus RAR-α small interfering RNA (Ad-siRAR-α) was used to transduce PC12 cells, and the efficiency of RAR-α expression inhibition was detected by semi-quantitative reverse transcription polymerase chain reaction (RT-PCR). An empty adenovirus vector was transfected in PC12 cells, which were used as the control. Flow cytometry with Annexin V-propidium iodide (PI) and fluorescence probe JC-1 staining was used to detect the apoptosis rate and mitochondrial transmembrane potential (MMP), respectively, of PC12 cells after transduction with Ad-siRAR-α. Furthermore, the expression levels of key genes in the RAR-α and mitochondrial apoptotic pathway, Bcl-2 and Bcl-2-associated protein (Bax) were analyzed by RT-quantitative (q)PCR and western blot analysis. RAR-α mRNA expression was observed to be decreased in PC12 cells following OGD-induced injury, and this decrease can be reversed by 4 µmol/l ATRA treatment. After 36 h transfection with Ad-siRAR-α, RAR-α gene expression was significantly inhibited compared with the control (P<0.05). The results of Annexin V-PI, fluorescence probe JC-1 staining and flow cytometry demonstrated that the apoptosis rate significantly increased and MMP significantly decreased in OGD-induced PC12 cells following transduction with Ad-siRAR-α compared with the control (both P<0.05). RT-qPCR and western blot analysis indicated that Bax expression was significantly increased and Bcl-2 expression was significantly decreased in PC12 cells transduced with Ad-siRAR-α after OGD-induced injury at the mRNA and protein level (P<0.05). In conclusion, Ad-siRAR-α transduction could promote apoptosis in OGD-induced PC12 cells. This suggests that the expression of Bax and Bcl-2 in the mitochondrial apoptosis signaling pathway is, at least in part, mediated by RAR-α expression, thereby indicating that RAR-α expression exerts an anti-apoptotic effect on OGD-damaged PC12 cells.