Miracle-dz/UniFAR-FTUs huggingface dataset:
Panning for gold: finding relevant semantic content for grounded language learning

One of the key challenges in grounded language acquisition is resolving the intentions of the expressions. Typically the task involves identifying a subset of records from a list of candidates as the correct meaning of a sentence. While most current work assume complete or partial independence between the records, we examine a scenario in which they are strongly related. By representing the set of potential meanings as a graph, we explicitly encode the relationships between the candidate meanings. We introduce a refinement algorithm that first learns a lexicon which is then used to remove parts of the graphs that are irrelevant. Experiments in a navigation domain shows that the algorithm successfully recovered over three quarters of the correct semantic content.
Lyapunov Stability Analysis of a String Equation Coupled With an Ordinary Differential System

This paper considers the stability problem of a linear time invariant system in feedback with a string equation. A new Lyapunov functional is proposed using augmented states that enriches and encompasses the classical functionals of the literature. It results in tractable stability conditions expressed in terms of linear matrix inequalities. This methodology follows from the application of the Bessel inequality to the projections over the Legendre polynomials. Numerical examples illustrate the potential of our approach through three scenario: a stable ODE perturbed by the PDE, an unstable open-loop ODE, and an unstable closed-loop ODE stabilized by the PDE.
Measurement Methods for Ganymede's Librations from Laser Altimetry Observations

We investigate the potential for the Ganymede Laser Altimeter (GALA) on board the JUICE (JUpiter ICy moons Explorer) mission to measure the tidal deformations and the rotational state of Ganymede, in particular physical librations. A subsurface ocean would cause the upper ice shell to librate decoupled from its interior. Two distinct iterative least-squares inversion routines have been implemented to estimate simultaneously for the topography, the orientation of the rotation pole, the mean rotation and the libration amplitudes and tidal deformations using synthetic laser altimetry data. We follow the approach of a global expansion of spherical harmonics and as a second method the parametrization in 2D B-splines. Results indicate that a global ocean could only be found for a very thin ice shell thickness, if the ice shell is elastic and fully decoupled of the interior. However, to prove the non-existence of an ocean is difficult due to small libration amplitudes and their possibly ambiguous interpretation. In this case independent observations, such as the Love numbers, provide more unique constraints on the interior structure.
Model Free Optimal Control of Two Whole Buildings using Deep Q-networks

This study introduces an application of deep Q-networks (DQN) for whole building energy optimization. The DQN was coupled to an office building and a retail building: an EnergyPlus simulation model provided by the U.S. Department of Energy (DOE). The optimal controls can successfully minimize total building energy use with appropriate indoor CO2 concentrations maintained (less than 1,000 ppm). It was shown that the DQN can find balanced control actions between different energy consumers in the building such as chillers, pumps, and air handling units (AHUs), leading to a whole building global optimization.
Testing a Heuristic Algorithm for Finding a Maximum Clique on DIMACS and Facebook Graphs

In this paper we propose a new heuristic algorithm for solving a maximum clique search problem (MCP). While the proposed algorithm (called TrustCLQ) uses a general approach to solving MCP, it is almost independent of the order of vertices and does not exploit a partition of the graph into independent sets. The algorithm was tested on DIMACS library graphs which are often employed for testing MCP solution algorithms. TrustCLQ algorithm was compared with the well-known ILS heuristic algorithm (as well as with a standard algorithm from networkx library) on DIMACS data sets. Moreover, TrustCLQ algorithm has been tested on Facebook social graphs
Improving Video Activity Recognition using Object Recognition and Text Mining

Recognizing activities in real-world videos is a challenging AI problem. We present a novel combination of standard activity classification, object recognition, and text mining to learn effective activity recognizers without ever explicitly labeling training videos. We cluster verbs used to describe videos to automatically discover classes of activities and produce a labeled training set. This labeled data is then used to train an activity classifier based on spatio-temporal features. Next, text mining is employed to learn the correlations between these verbs and related objects. This knowledge is then used together with the outputs of an off-the-shelf object recognizer and the trained activity classifier to produce an improved activity recognizer. Experiments on a corpus of YouTube videos demonstrate the effectiveness of the overall approach.
The UTexas system for TAC 2019 SM-KBP Task 3: Hypothesis detection with graph convolutional networks

Events of geopolitical interest can unfold quickly and are frequently surrounded by uncertainty. This can largely be attributed to the emergence of information across various modalities and media in different languages, and is frequently compounded by the presence of conflicting claims. The crash of flight MH17 in Ukraine in 2014 serves as a good example. After the crash, a multitude of explanations arose, including Russia-affiliated aggression, Ukranian military aggression, an act of terror, and mechanical failure. The Streaming Multimedia Knowledge Base Population (SM-KBP) track at TAC 2019 continues to address the challenges of this scenario in three tasks. Task 1 is the extraction of relevant information from multi-modal and multi-lingual sources. Task 2 is the aggregation of document-level knowledge into a multi-document knowledge graph, including cross-document coreference resolution. Task 3 is to probe the resulting knowledge graph for information relevant to a provided statement of information need (SIN), entailing the separation of conflicting claims and the construction of internally consistent narratives (represented as subgraphs of the multi-document knowledge graph called hypotheses). The UTexas team participated in Task 3, hypothesis generation. We view hypothesis generation as a one-class clustering task (Bekkerman and Crammer, 2008) of detecting further coherent entities and statements surrounding a hypothesis seed. The UTexas system for TAC 2019 uses a hybrid system in which a neural model proposes statements for inclusion in the hypothesis cluster, and a rule-based filter that rejects statements based on either domain knowledge or domain-independent well-formedness criteria.
Estimativa de coeficientes de correlao e trilha em geraes segregantes de trigo hexaplide

The knowledge of how the agronomical traits are correlated in segregating populations is of primary importance for plant breeders, specially when one considers indirect selection through a character easily measurable or with high heritability correlated to the trait of interest with lower heritability. Agronomical traits in F2 and F3 generations of four wheat crosses, were evaluated on 2004 agricultural year in the Centro Agropecuario da Palma, of the Centro de Genomica e Fitomelhoramento - FAEM/UFPel, located in Capao do Leao country in Rio Grande do Sul State. The present work had the objectives to estimate for evaluated traits: a) genotypic, phenotypic and environmental correlation coefficients between the traits; and b) path coefficients for grain yield. For all populations, the single correlations indicated that the selection of higher yielding plants can be performed by indirect selection for number of fertile tillers per plant. The path analysis showed that it was possible to identify the indirect effect of the trait number of fertile tillers the as cause of the correlation between other traits (ear length x grain yield, spikelet number per ear x grain yield and number of ear grain x grain yield). Thus suggesting that the number of fertile tillers is the major trait to select for in order to obtain gains on grain yield.
The Construction of Minimum Variables Set for Energy Prediction Models of Office Buildings

A variety of building energy prediction models have been widely used in building performance optimization. While the high data requirements of most models present a barrier to effective building performance management. At the same time, the shortage of reasonable variable selection has resulted in reduced credibility and generalization of most model results. This study aims to deeply explore the indispensable variables of building energy prediction and put forward the concept of minimum variables sets for reasonably accurate building energy prediction in the condition of limited data information. With application of feature dimension reduction methods to a building performance database, the hierarchical minimum variables sets are constructed for multiple building energy prediction objects. By means of two kinds of energy models, the accuracy of the minimum variables sets is analysed and evaluated to provide theoretical basis and data support for practically appropriate trade-off of limited data and acceptable requisition of building energy prediction.
Polygon Laplacian Made Simple

The discrete Laplace‐Beltrami operator for surface meshes is a fundamental building block for many (if not most) geometry processing algorithms. While Laplacians on triangle meshes have been researched intensively, yielding the cotangent discretization as the de‐facto standard, the case of general polygon meshes has received much less attention. We present a discretization of the Laplace operator which is consistent with its expression as the composition of divergence and gradient operators, and is applicable to general polygon meshes, including meshes with non‐convex, and even non‐planar, faces. By virtually inserting a carefully placed point we implicitly refine each polygon into a triangle fan, but then hide the refinement within the matrix assembly. The resulting operator generalizes the cotangent Laplacian, inherits its advantages, and is empirically shown to be on par or even better than the recent polygon Laplacian of Alexa and Wardetzky [AW11] — while being simpler to compute.
Potential field guide for humanoid multicontacts acyclic motion planning

We present a motion planning algorithm that computes rough trajectories used by a contact-points planner as a guide to grow its search graph. We adapt collision-free motion planning algorithms to plan a path within the guide space, a submanifold of the configuration space included in the free space in which the configurations are subject to static stability constraint. We first discuss the definition of the guide space. Then we detail the different techniques and ideas involved: relevant C-space sampling for humanoid robot, task-driven projection process, static stability test based on polyhedral convex cones theory's double description method. We finally present results from our implementation of the algorithm.
Impact of Ownership Structure on Capital Structure- Empirical Evidence From Listed Firms in Vietnam

This research examines whether and to what degree does ownership structure affect the capital structure, using a universal sample of Vietnamese listed firms from 2009 to 2015. We consider the ownership structure in two dimensions: ownership concentration and ownership types. Our paper is among the few to deploy Herfindahl index of all major shareholders, institutional shareholders, and even foreign shareholders as proxies for ownership concentration. The results depict a positive and significant impact of ownership concentration on the overall capital structure of the companies. Further tests indicate that the effect of ownership structure is stronger for short-term debt and bank debt and weaker or even not significant for long-term debt. Despite the fact that the ownership of state, institutional investors, and foreign investors do not significantly affect the capital structure ratio, we find a non-linear relationship between managerial ownership and the structure of overall debt and bank debt.
Using a Multimethod Approach to Research Enterprise Systems Implementations

: This paper explores the use of multimethod research design. With the development and legitimacy of both qualitative and quantitative research, the combination of both types is expanding. In this paper we present how we have explored the multimethod approach by using an example domain in a step-by-step manner, learning about the strengths and weaknesses of this approach. The context is a doctoral research project whose aim is to study critical success factors for Enterprise Resource Planning (ERP) implementation projects.
Exploring the integration of simulation and deep learning models for urban building energy modelling and retrofit analysis

As the world rapidly urbanizes, cities will need to be created and expanded to accommodate their growing populations – putting immense pressure on engineers, scientists and policymakers to improve the efficiency of the energy intensive built environment. One of the key barriers to improving the energy efficiency of cities is the ability to accurately model and characterize the energy performance of their buildings. While simulation-based methods have been developed to help predict the energy consumption of urban buildings, they are limited in their ability to quickly evaluate the effects of various design or retrofit scenarios. New data-driven methods are emerging to model building energy usage, but their lack of an underlying physics-based engine limits applicability and interpretability for assessing design or retrofit scenarios. In this paper, we employ the use of an integrated simulation and data-driven method (i.e., Data-driven Urban Energy Simulation or DUE-S) to model a large-scale retrofit policy on a case study of 52 buildings in a Californian city. Our results indicate that the DUE-S model is able to capture the energy impacts that the urban context has on buildings that undergo retrofits as well as those that do not. Our primary contribution is to demonstrate the merits of combining physics-based building simulation methods with new data-driven machine learning methods (i.e., transfer learning) to assess the impact of various design and retrofit scenarios across a large urban area and in turn spawn future research at the intersection of simulation and data science. In the end, realizing deep energy savings from urban buildings will require new tools that are both accurate and interpretable enough to inform decision-making for a variety of urban sustainability stakeholders regarding early stage designs, energy efficiency retrofits and environmental policymaking.
On the Proper Treatment of Quantifiers in Probabilistic Logic Semantics

As a format for describing the meaning of natural language sentences, probabilistic logic combines the expressivity of first-order logic with the ability to handle graded information in a principled fashion. But practical probabilistic logic frameworks usually assume a finite domain in which each entity corresponds to a constant in the logic (domain closure assumption). They also assume a closed world where everything has a very low prior probability. These assumptions lead to some problems in the inferences that these systems make. In this paper, we show how to formulate Textual Entailment (RTE) inference problems in probabilistic logic in a way that takes the domain closure and closed-world assumptions into account. We evaluate our proposed technique on three RTE datasets, on a synthetic dataset with a focus on complex forms of quantification, on FraCas and on one more natural dataset. We show that our technique leads to improvements on the more natural dataset, and achieves 100% accuracy on the synthetic dataset and on the relevant part of FraCas.
Zinc Supplementation in Cytokine Regulation During LPS-induced Sepsis in Rodent

Severe sepsis increased pro-inflammatory cytokines and low Zinc levels were also found in patients with sepsis.To explain the mechanisms of sepsis improvement after Zinc administration through cytokine regulations Total sample of 40 rodents was randomized into 4 groups with treatment of LPS in LPS group and LPS-Zinc and placebo in Control and Zinc group, then followed by blood sampling at the second hour to measure Zinc level by AAS. Furthermore, in the LPS-Zinc and Zinc group, Zinc was administered orally, while the control and LPS group were given placebo orally. The blood samples were then collected at the 8th, 24th, and 72nd hour to measure TNF-α, IL-6, IL-10, and TGF- by sandwich-ELISA method and the Zinc content was also measured at the 72nd hour using AAS. One-way Anova, Kruskall Wallis, Mann-Whitney, paired t-tests, and Wilcoxon Signed Rank Test were employed as statistical analyses. There were some decreases in the level of TNF-α and IL-6 plasma and an elevated levels of IL10 and TGF- in the LPS-Zinc group compared to the LPS group. Administration of Zinc in sepsis improve sepsis condition through decreased pro-inflammatory cytokines (TNF-α and IL-6) and increased anti-inflammatory cytokines (IL-10 and TGF-). Experimental article (J Int Dent Med Res 2020; 13(1): 46-50)
Belgeo, 2 | 2009

The daily commute is still the main source of traffic congestion. Despite transport research emphasis on commuters, the work end of home to work travel receives less attention. However, employers influence the commute behaviour of employees in different ways. The Belgian database Home-to-Work-Traffic (HTWT) contains information on accessibility, work regimes and mobility management initiatives of 7460 worksites of large employers in Belgium. In a spatial multilevel regression model both contextual and worksite factors are incorporated to investigate the share of cycling employees. While controlling for different economic, physical and other factors, the provision of bicycles by the employer seems to be successful, while the effect of bicycle facilities is less clear, partly due to the fact that bicycle facilities are more popular in less cycle-friendly areas. Het dagelijkse pendelverkeer is nog steeds de voornaamste bron van verkeerscongestie. Ondanks de nadruk die transportonderzoek legt op dit pendelverkeer, blijft de werkkant van het woonwerkverkeer onderbelicht. Nochtans beïnvloeden werkgevers het pendelgedrag van hun werknemers op verschillende manieren. De Belgische databank woon-werkverkeer bevat informatie over de bereikbaarheid, de arbeidstijden en mobility management maatregelen van 7460 werklocaties van grote werkgevers gevestigd in België. In een ruimtelijk multilevel regressiemodel zijn zowel omgevingsals werklocatiefactoren opgenomen om het aandeel fietsende werknemers te verklaren. Rekening houdend met economische, fysische en andere factoren, lijkt het actief aanbieden van fietsen door de werkgever een succesvolle maatregel, terwijl het effect van de klassieke fietsvoorzieningen minder duidelijk is, onder meer omdat deze vooral terug te vinden zijn in minder fietsvriendelijke gebieden.
Machine Reading Comprehension with Abstention Veriﬁcation

In this project, we aim to build a robust question answering system using the SQuAD 2.0 dataset for training and evaluation. The primary focus of our project is to explore non-recurrent solutions for SQuAD built off of the QANet architecture. We implemented the QANet model from the ground up with emphasis on modular implementation and computational efﬁciency. We further enhanced it with an abstention module and variable-sized ﬁlters in convolutional layers. Additionally, we trained various Reader and Veriﬁer models and integrated those to achieve better performance metrics. We used EM and F1 scores, as well as answer versus no-answer classiﬁcation accuracy, to evaluate the performance of our model. Our custom QANet-based models achieved well-placed scores on the non-PCE leaderboard.
Bacterial inactivation by engineered water nanostructures generated by electrospraying

Herein we present a novel method for microbial disinfection that utilizes the formation of unique Engineered Water Nanostructures (EWNS) generated via electrospraying. The physico-chemical properties of the EWNS such as their size distribution, surface charge, and reactive oxygen species were assessed. The potential of the generated EWNS to inactivate bacteria in air and on surfaces was assessed both quantitatively and qualitatively. Serratia Marcescens (gram-negative) bacteria were used for both the surface and for the air inactivation experiments. Our results show that the EWNS have a size of approximately 20 nm, which is very stable, and carry an average charge of 10 electrons per particle. The results from the surface inactivation of Serratia Marcescens showed that there is more than a 2-log10 reduction. Similarly the results from the air inactivation showed 50% bacterial removal at steady state.
Maritime Traffic Surveillance with SENTINEL-1 High Resolution Images

Automatic ship detection in synthetic aperture radar (SAR) images is a promising subject for maritime surveillance that exploits the characteristics inherent to SAR technology. Since the radars are active devices that do not depend on sunlight and use wavelengths with high atmospheric penetration, the acquisition of images day and night and in virtually all weather conditions becomes possible. Sentinel-1A is the first of a constellation of two European Synthetic Aperture Radar satellites, and it is operational since 3 October 2014. The data is provided free of charges by the European Space Agency (ESA) and the European Commission within the Copernicus Program. The data set used to implement and test this algorithm consists in 32 images of the same area, near the port of Singapore, acquired between October 2014 and January 2017. This is a harbor known by its intense commercial traffic, where there is also high activity of small fishing boats, and a constant presence of large floating platforms, probably involved in drainage and maintenance of the port bathymetry. The algorithm is based on a wavelet filter variation that can provide a very smooth image, reducing speckle effects and allowing a precise detection of the targets. It can be used in small computers and take seconds to process large areas. The accuracy of the results makes possible the use of morphological criteria to filter the candidates detected, in addition to the usual radiometric criteria. The quality of the initial detection of targets is compared with the detection achieved with two of the most popular algorithms used in ship detection with similar processing times, SUMO and LM. The concept of false positives does not apply, as all the candidates are (most probably) ships, but of dimensions not relevant in this case study. Each image has its own land/sea mask, defining the area of interest (AOI) to work, because there are small variations in the field of view of the SAR, as well as in the image itself: a few images include strips of saturated pixels where discrimination is not possible, and this areas are eliminated from the AOI to save processing resources. This work intends to make available a quick and efficient algorithm implemented in Matlab for SENTINEL-1 images of level 1 with a pixel nearly square of 10 m, directly downloaded from ESA site (https://scihub.copernicus.eu/dhus/#/home), which can be used at any facility with usual informatics support. Visual interpretation was used to validate the results; validation based on ground truth data acquired by VMS/AIS installed in the considered ships and harbor was not in reach for this amount of data and time spanning.