Miracle-dz/UniFAR-FTUs huggingface dataset:
Referring-Expression Generation Using a Transformation-Based Learning Approach

A natural language generation system must generate expressions that allow a reader to identify the entities to which they refer. This paper describes the creation of referring-expression (RE) generation models developed using a transformation-based learning approach. We present an evaluation of the learned models and compare their performance to the performance of a baseline system, which always generates full noun phrase REs. When compared to the baseline system, the learned models produce REs that lead to more coherent natural language documents and are more accurate and closer in length to those that people use.
Smooth Kronecker: Solving the Combing Problem in Kronecker Graphs

Graphs and graph-processing have become increasingly important. This has led to an explosion in the development of graph-processing systems, each of which is evaluated relative to its predecessors. In the absence of a large corpus of real-world graphs, synthetic generators provide an easy way to construct graphs of varying sizes. The most widely used generator is the Kronecker generator. Unfortunately, the Kronecker generator was not designed to produce graphs for benchmarking and when used in this fashion, it is problematic in two ways. First, the fraction of zero-degree vertices scales with the graph size, dramatically reducing the effective size of the connected graph. Second, the generator produces a vertex degree distribution not found in real world settings. We demonstrate these phenomena and present the Smooth Kronecker Generator, which remedies the vertex degree distribution problem without changing the statistical properties of the graph.
WARDER: Towards E ective Spreadsheet Defect Detection by Validity-based Cell Cluster Re nements

Nowadays spreadsheets are very popular and being widely used. However, they can be prone to various defects and cause severe consequences when end users poorly maintain them. Our research communities have proposed various techniques for automated detection of spreadsheet defects, but they commonly fall short of e ectiveness, either due to their limited scope or relying on strict patterns. In this article, we discuss and improve one state-of-the-art technique, CUSTODES, which exploits spreadsheet cell clustering and defect detection to extend its scope and make its detection patterns adaptive to varying spreadsheet styles. Still, CUSTODES can be prone to problematic clustering when accidentally involving irrelevant cells, leading to a largely reduced detection precision. Regarding this, we present WARDER to rene CUSTODES’s spreadsheet cell clustering based on three extensible validity-based properties. Experimental results show that WARDER could improve the precision by 19.1% on spreadsheet cell clustering, which contributed to a precision improvement of 23.3∼24.3% for spreadsheet defect detection, as compared to CUSTODES (F-measure increased from 0.71 to 0.79∼0.82). WARDER also exhibited satisfactory results on another practical large-scale spreadsheet corpus VEnron2, improving the defect detection precision by 10.7∼21.2% over CUSTODES.
Goal Formation through Interaction in the Situation Calculus: A Formal Account Grounded in Behavioral Science

Goal reasoning has been attracting much attention in AI recently. In this paper, we consider how an agent changes its goals as a result of interaction with humans and peers. In particular, we draw upon a model developed in Behavioral Science, the Elementary Pragmatic Model (EPM) . We show how the EPM principles can be incorporated into a sophisticated theory of goal change based on the Situation Calculus. The resulting logical theory supports agents with a wide variety of relational styles, including some that we may consider irrational or creative. This lays the foundations for building autonomous agents that interact with humans in a rich and realistic way, as required by advanced Human-AI collaboration applications
On the Recoverability of Persistent Memory Systems

Persistent memory (PM) technologies [2, 6, 10] are expected to provide durability similar to the flash memory with latencies comparable to DRAM. These unique characteristics bring new challenges for system design. Among others, PM-based systems have to carefully order and persist writes to the memory so that the system states are always recoverable upon failures. This is nontrivial due to the subtle behavior of modern cache and memory subsystem [7, 8]. To make PM easier to use, great efforts have been made to optimize different layers/components of systems (e.g., file systems [5, 11, 12], libraries [4, 9], databases [3]). These PM-aware systems generally include sophisticated designs to achieve high performance while maintaining high recoverability. Nevertheless, the evaluation of these systems is unbalanced: while various benchmarks have been used to demonstrate the performance gain (including the performance of recovery), there is little measurement of the recoverability guarantee, largely because of a lack of effective methodology.
Chasing CMB Photons Through the Nonlinear Universe a

Detailed observations of the Cosmic Microwave Background (CMB) have renewed the interest in studying anisotropies of the CMB that are due to non-linear structures. Previous studies have considered the effect of a single, spherically symmetric density inhomogeneity on the CMB anisotropy using either a Swiss cheese thin-shell approximation5 or Tolman-Bondi solution^. ^^^^^^^ Other studies have parametrized the time dependence of the correlation function" or considered the effect of a single, non-symmetric structure. " The effect on the CMB of multiple, non-trivially distributed matter structures, as well as the complete and unambiguous continuous linkage between the linear, weakly clustered and fully nonlinear epochs, has not been taken into consideration. Furthermore, even for single structures, the peculiar motion of matter structures has been ignored when considering late-time non-linear effects on the CMB. Our study addresses the signatures in the microwave sky from time-varying gravitational potentials associated with non-linear density inhomogeneities in the universe without any of the above simplifications; in particular, we wish to find the correlation between these gravitationally induced anisotropies with the underlying density field and proper motion of the structures that photons encounter along their paths. Anisotropies in the CMB from time-varying potential are not only due to intrinsic changes in the density field of large scale structures, namely the ReesSciama effect,' but also due to the proper bulk motion of structures. " We consider two unbiased 0 = 1.0,h = 0.75 Cold Dark Matter (CDM) models, 124h-' Mpc and 248h-1 Mpc on a side. The typical void size in these models is N 30h-' Mpc and N 60h-' Mpc, respectively. The temperature anisotropies of the CMB associated to gravitational potentials and proper motions are given by13714
Rational vs Byzantine Players in Consensus-based Blockchains

We analyze from the game theory point of view Consensus-based blockchains when participants exhibit rational or Byzantine behavior. Our work is the first to model the Byzantine-consensus based blockchains as a committee coordination game. Our first contribution is to offer a game-theoretical methodology to analyze equilibrium interactions between Byzantine and rational committee members in Consensus-based blockchains. Byzantine participants seek to inflict maximum damage to the system, while rational participants best-respond to maximize their expected net gains. Our second contribution is to derive conditions under which consensus properties are satisfied or not in equilibrium. When the number of votes required for a decision is lower than the proportion of Byzantine participants, invalid blocks are accepted in equilibrium. When the number of votes needed is large, equilibrium can involve coordination failures, in which no block is ever accepted. However, when the cost of accepting invalid blocks is large, there exists an equilibrium in which blocks are accepted if and only if they are valid.
Phenotypic Variation in Metamorphosis and Paedomorphosis in the Salamander Ambystoma talpoideum

Phenotypic variation in metamorphosis and paedomorphosis in the salamander Ambystoma talpoideum was examined to determine its environmental or genetic basis. Eight artificial ponds were maintained, four at each of two environmental treatments: constant water level, to simulate fish-free permanent breeding ponds, and gradual drying out, to simulate temporary breeding ponds. Two populations of salamanders were used, derived from two breeding ponds having different frequencies of paedomorphosis. The water level in the drying treatment was lowered during the last 10 wk of the experimental period with no apparent differences in water chemistry parameters between treatments and only a slight change in water temperature during the last 2 wk. The effects of water level were potentially confounded by those of water temperature, density of larvae, and amount food. Population differences in the frequency of metamorphosis and paedomorphosis could potentially represent genetic differences resulting from the different selective regimes that individuals encounter in breeding ponds varying in drying frequency. 35 references, 3 figures, 4 tables.
Ecological controls on post‐fire vegetation assembly at multiple spatial scales in eastern North American boreal forests

AbstractQuestion: In ﬁre-prone boreal forests, to what extent does ﬁre severity inﬂu-ence understorey plant community assembly? What are the abiotic and bioticfactors controlling understorey community regeneration at regional, landscapeandlocalscales? Location:Blackspruce-dominatedborealforestineasternCanada.Methods: A taxonomic and a trait-based approach were combined to evaluatethe relative inﬂuence of habitat characteristics, burn severity and biotic pro-cessesonunderstoreyregenerationatmultiplespatialscales.Samplingofunder-storey vegetation cover was carried out in 133 recently burned plots located in14 different ﬁres across a 600-km transect. The spatial hierarchy for samplingconsisted of ﬁve ﬁre regime zones (regional scale), two to four ﬁres within eachzone (landscape) and two to four toposequences within each ﬁre (local). Theenvironmentalcontrolof ﬁreseverityand habitatcharacteristicson understoreyregeneration was assessed using a canonical redundancy analysis (RDA) andvariation partitioning. We investigated environmental and biotic ﬁlters of spe-cies traits at different scales by modelling trait–spatial assemblages with Moraneigenvectormaps(MEMs). Results: In spite of the large variability of environmental conditions covered byour sampling design, low depths of burn were measured in the large majority ofthe studied sites. Incomplete consumption of the forest ﬂoor is frequentlyobservedineasternborealforestscharacterizedbylongﬁrecycles. Insitubiologi-cal legacies persisted through the low-severity ﬁres, which conserved the pre-ﬁre relationships between plant communities and their environment. As aresult,ﬁreseveritywasneithertheuniquenorthedominantcontrolonpost-ﬁreregeneration. Habitat characteristics explained a three times higher proportionofvariation in species composition.Biotic controls on trait assemblage increasedatthetwoﬁnerscales. Conclusions:Severityalone cannotpredict understorey vegetationassembly atdifferentscalesinthelow-severityﬁreregimecharacteristicof theeasternNorthAmericanborealforest.IntroductionIn boreal forest, understorey vegetation is a key ecosystemcompartment that exerts strong control on both above-ground and below-ground ecosystem processes (Messieret al. 1998; Nilsson & Wardle 2005; Kolari et al. 2006). The heterogeneous and often dense vegetation layermodulates light transmission, soil temperature andevapotranspiration, which in turn affect nutrient cycling,germination, growth and survival of young plant cohorts(Oechel & Van Cleve 1986; Messier et al. 1998; Aubinet al.2000;Nilssonet al.2000). In boreal forests of North America, wildﬁre is one of themain natural disturbance agents that structure the vegeta-tionmosaicatthelandscapescale,andthatinitiateandter-minate succession at the stand scale. In this biome, ﬁre
Role of Thermal Environment in Habitat Selection by Male White-Tailed Deer during Summer in Texas, USA

Thermal cover may influence habitat selection by white-tailed deer Odocoileus virginianus in subtropical climates with hot summers. We 1) tested the hypothesis that thermal environment is more important in habitat selection at midday during summer than forage quality or quantity and concealment cover and 2) determined whether operative temperature, vegetation height, or woody plant canopy cover (or some combination of these) explain habitat selection at midday. We predicted that during crepuscular periods and at night habitat use increases with increasing forage quality and quantity and concealment cover and is unrelated to thermal environment. Male white-tailed deer were fitted with GPS collars to determine resources selected within habitats during June and July 2008 and 2009. A generalized linear mixed model using logistic regression was used to estimate resource selection functions. We used the first principal component in a principal components analysis (PCA) of forage standing crop, crude protein, and acid detergent fiber (ADF) to create a ‘forage index’. This index and vegetation height, operative temperature and concealment cover, together with their interactions with activity period, were used to develop a priori candidate models. Akaike weights were used to compare candidate models. A model that included the forage index, vegetation height, operative temperature, concealment cover and their interactions with activity period was the best model out of 97 candidate models for explaining habitat selection by adult male white-tailed deer. Male white-tailed deer selected areas with taller vegetation in morning and midday activity periods but selected shorter vegetation during evening and nighttime. Forage quality was important in habitat selection in all activity periods. Male white-tailed deer did not select areas with greater concealment cover during any activity period. A combination of operative temperature, vegetation height, and woody plant canopy cover predicted midday habitat use better than any of these three variables alone. Thermoregulatory behavior in male white-tailed appears to include a combination of seeking cooler environments during midday but at the same time using areas with greater forage quality.
Yesterday's Reward is Today's Punishment: Contrast Effects in Human Feedback to Reinforcement Learning Agents

Autonomous agents promise users of a personalized future, allowing them to direct their attention to tasks most meaningful to them. However, the demands of personalization stand unfulfilled by current agent training paradigms such as machine learning, which require many orders of data to train agents on a single task. In sequential decision making domains, Reinforcement Learning (RL) enables this need, when a priori training of desired behaviors is intractable. Prior work has leveraged user input to train agents by mapping them to numerical reward signals. However, recent approaches have identified inconsistent human feedback as a bottle-neck to achieving best-case performance. In this work, we present empirical evidence to show that human perception affected by contrast effects distorts their feedback to Reinforcement Learning agents. Through a set of studies involving 900 participants from Amazon Mechanical Turk who were asked to give feedback to RL agents, we show that participants significantly underrate an agent’s actions after being exposed to an agent of higher competence on the same task. To understand the significance of this effect on agent performance during training, we then simulate trainers that underrate actions of an agent based on past performance – creating a systematically skewed feedback signal – integrated into an actor-critic framework. Our results show that agent performance is reduced by up to 98% in the presence of systematic skews in human feedback in Atari environments. Our work provides a conceptual understanding of a source of inconsistency in human feedback, thus informing the design of human-agent interactions.
Genetic Deep Reinforcement Learning for Mapless Navigation

We consider Deep Reinforcement Learning (DRL) approaches to devise mapless navigation strategies for mobile platforms. We pro-pose a Genetic Deep Reinforcement Learning (GDRL) method that combines Genetic Algorithms (GA) with discrete and continuous action space DRL approaches. The goal of GDRL is to reduce the sensitivity of DRL approaches to their hyper-parameter tuning and to provide robust exploration strategies. We evaluate GDRL in combination with Rainbow and Proximal Policy Optimization (PPO) in two navigation scenarios: i) a wheeled robot avoiding obstacles in an indoor environment and ii) a water drone that must reach a predefined location in presence of waves. Our empirical evaluation demonstrates that GDRL outperforms state-of-the-art DRL and GA methods as well as a previous hybrid approach.
Extracellular Metabolites Produced by a Novel Strain Bacillus alveiNRC-14: 6. Heat-Shock Stress Induces Production of Antimicrobial Metabolites

Heat shock and other proteotoxic stresses cause accumulation of nonnative proteins that trigger activation of heat shock protein genes. The strain Bacillus alveiNRC-14 was found to produce antimicrobial metabolites when exposed to a heat shock.The present study investigated the influence of cultural and environmental parameters affecting growth and antimicrobial metabolites produced by the novel strain Bacillus alveiNRC-14. The antimicrobial metabolites exhibited a broad spectrum of in vitro antimicrobial activity against some human pathogens. Antimicrobial metabolites by the strain are highly produced with chitin, chitosan or related substrates as compared to other tested carbon sources. Fungal mycelium (1%) and ammonium sulfate (0.2%) were found to be the most suitable carbon and nitrogen sources for optimum growth and production, at pH 6.0 and 40Cfor 48h, under shaking conditions. The antimicrobial metabolites inhibited the growth of some human bacterial and fungal pathogens. The metabolites are heat-stable and remained active after sterilization at 121°C for 15 min. The antimicrobial metabolite activity is stable over a wide range of pH values (4–10) with a maximum between 5 and 8. This is the first report on production of antimicrobial metabolites by the novel strain Bacillus alveiNRC-14.
Inefficient Collective Households: Cooperation and Consumption

We propose a model of consumption inefficiency in collective households. Inefficiency depends on a “cooperation factor” , which can also affect both the allocation of resources within a household and the utility of household members. Households are conditionally efficient, conditioning on the value of the cooperation factor. This lets us exploit convenient modeling features of efficient households (like not needing to specify the bargaining process), while still accounting for, and measuring the dollar cost of, inefficient levels of cooperation.
Interprétation de Mouvements Temps Réel

This paper proposes a new method for real-time human motion recognition using Motion Capture (MoCap) data. In particular, our method uses examplars to learn actions, without the need for learning intra class variations. We have formalized streamed recognizable actions, coming from an online MoCap engine, into a motion graph, similar to an animation motion graph. This graph is used as an automaton to recognize known actions as well as to add new ones. We have defined and used a spatio-temporal metric for similarity measurements to achieve more accurate feedbacks on classification. The proposed method has the advantage of being linear and incremental, making the recognition process very fast and the addition of a new action straightforward. Furthermore, actions can be recognized with a score, even before they are fully completed. Thanks to the use of a skeleton-centric coordinate system, our recognition method has become view-invariant. We have successfully tested our action recognition method on both synthetic and real data. The latter was obtained from live input videos using either our online markerless MoCap engine or the Kinect acquisition system.
ChartDialogs: Plotting from Natural Language Instructions

This paper presents the problem of conversational plotting agents that carry out plotting actions from natural language instructions. To facilitate the development of such agents, we introduce ChartDialogs, a new multi-turn dialog dataset, covering a popular plotting library, matplotlib. The dataset contains over 15,000 dialog turns from 3,200 dialogs covering the majority of matplotlib plot types. Extensive experiments show the best-performing method achieving 61% plotting accuracy, demonstrating that the dataset presents a non-trivial challenge for future research on this task.
Overview of Diagnosis and Treatment of Breast Cancer in Young Women

Breast cancer is the most frequently diagnosed neoplasm and the first cause of death in women, especially under the age of 40. Hereditary and genetic syndromes are more prevalent among young breast cancer patients and require genetic counseling. Young women also exhibit larger tumors, with more frequent nodal involvement and more aggressive features (triple negative, high grade and proliferation rate) than menopausal women. Fertility preservation and pregnancy-associated cancer are specific issues in this age group and need to be addressed accordingly.
Formulation and evaluation of novel herbal antidiabetic transdermal patch

The definition of controlled release is a technique in which active chemicals are made available to a specified target at a rate and duration designed to accomplish an intended effect. At present, synthetic drugs form a major line of the treatment in the management of diabetes and few synthetic pharmacologically active substances can currently be administered through transdermal patches and production is technically demanding the development and optimization of dermatological delivery system is a challenging task in respect to herbal drugs and research is ongoing to improve the systems and expand the indications. [1,2] A number of transdermal patches have been developed by various investigators to achieve controlled release for extended duration, for example, antihypertensive patch,[3] contraceptive patch containing estrogen,[4] antirheumatic patch. [5] ABSTRACT
Constraint-Based Subspace Clustering

In high dimensional data, the general performance of traditional clustering algorithms decreases. This is partly because the similarity criterion used by these algorithms becomes inadequate in high dimensional space. Another reason is that some dimensions are likely to be irrelevant or contain noisy data, thus hiding a possible clustering. To overcome these problems, subspace clustering techniques, which can automatically find clusters in relevant subsets of dimensions, have been developed. However, due to the huge number of subspaces to consider, these techniques often lack efficiency. In this paper we propose to extend the framework of bottom up subspace clustering algorithms by integrating background knowledge and, in particular, instance-level constraints to speed up the enumeration of subspaces. We show how this new framework can be applied to both density and distance based bottom-up subspace clustering techniques. Our experiments on real datasets show that instance-level constraints cannot only increase the efficiency of the clustering process but also the accuracy of the resultant clustering.