Miracle-dz/UniFAR-FTUs huggingface dataset:
Column-specific context extraction for web tables

Relational Web tables have become an important resource for applications such as factual search and entity augmentation. A major challenge for an automatic identification of relevant tables on the Web is the fact that many of these tables have missing or non-informative column labels. Research has focused largely on recovering the meaning of columns by inferring class labels from the instances using external knowledge bases. The table context, which often contains additional information on the table's content, is frequently considered as an indicator for the general content of a table, but not as a source for column-specific details. In this paper, we propose a novel approach to identify and extract column-specific information from the context of Web tables. In our extraction framework, we consider different techniques to extract directly as well as indirectly related phrases. We perform a number of experiments on Web tables extracted from Wikipedia. The results show that column-specific information extracted using our simple heuristic significantly boost precision and recall for table and column search.
X-ray Dark-Field Vector Radiography—A Novel Technique for Osteoporosis Imaging

Abstract X-ray dark-field vector radiography (XVR) has emerged as an imaging technique which can efficiently yield dark-field scatter images of high quality, even with conventional X-ray tube sources. The XVR yields direction-dependent information about the X-ray scattering of the trabecular bone microstructure without the requirement of resolving the micrometer size structures directly causing the scattering. In this pilot study, we demonstrated that XVR-based degree of anisotropy correlated with femoral bone strength in the context of osteoporosis.
Remote Sensing as Pest Forecasting Model in Agriculture

Technologies based on plant protection research for major pests are of utmost important to attain sustainability in agriculture (Pratap et al., 2000). The yield losses due to pest population can be suppressed to be greater extent if their incidence/occurrence is known well in advance so that timely adoption of remedial measures is possible. This led to a concept of „forecasting‟, a prediction or estimate of future trend which involves all the activities in ascertaining and notifying the growers of community that conditions are sufficiently favourable for certain insect pests, that application of control measures will result in economic gain or on the other hand and just as important that the amount expected is unlikely to be enough to justify the expenditure of time, energy and money for International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 7 Number 03 (2018) Journal homepage: http://www.ijcmas. com
Establishment of an Efficient Sugarcane Transformation System via Herbicide-Resistant CP4-EPSPS Gene Selection

Sugarcane (Saccharum spp.), a major cash crop that is an important source of sugar and bioethanol, is strongly influenced by the impacts of biotic and abiotic stresses. The intricate polyploid and aneuploid genome of sugarcane has shown various limits for conventional breeding strategies. Nonetheless, biotechnological engineering currently offers the best chance of introducing commercially significant agronomic features. In this study, an efficient Agrobacterium-mediated transformation system that uses the herbicide-resistant CP4-EPSPS gene as a selection marker was developed. Notably, all of the plants that were identified by PCR as transformants showed significant herbicide resistance. Additionally, this transformation protocol also highlighted: (i) the high yield of transgenic lines from calli (each gram of calli generated six transgenic lines); (ii) improved selection; and (iii) a higher transformation efficiency. This protocol provides a reliable tool for a routine procedure for the generation of resilient sugarcane plants.
Influence of local carrying capacity restrictions on stochastic predator–prey models

We study a stochastic lattice predator–prey system by means of Monte Carlo simulations that do not impose any restrictions on the number of particles per site, and discuss the similarities and differences of our results with those obtained for site-restricted model variants. In accord with the classic Lotka–Volterra mean-field description, both species always coexist in two dimensions. Yet competing activity fronts generate complex, correlated spatio-temporal structures. As a consequence, finite systems display transient erratic population oscillations with characteristic frequencies that are renormalized by fluctuations. For large reaction rates, when the processes are rendered more local, these oscillations are suppressed. In contrast with the site-restricted predator–prey model, we also observe species coexistence in one dimension. In addition, we report results on the steady-state prey age distribution.
Association of Korean Healthy Eating Index and Sleep Duration with Obesity in Korean Adults: Based on the 7th Korea National Health and Nutrition Examination Survey 2016–2018

Short sleep duration has been linked to an increased obesity risk, and emerging evidence suggests that diet quality potentially influences this association. This cross-sectional study aimed to examine the association of obesity with sleep duration and diet quality in adults. The participants comprised 10,967 adults (4623 men and 6344 women) aged 19–64 years who participated in the 7th National Health and Nutrition Examination Survey (2016–2018). Sleep duration was categorized into adequate (≥7 h) and insufficient (<7 h). Diet quality was evaluated using the Korean Healthy Eating Index (KHEI), with scores ranging from 0 to 100, based on 14 dietary components. Obesity was associated with higher rates of insufficient sleep in women but not in men. After adjusting for covariates, the obesity risk in women with insufficient sleep was approximately 1.3 times higher than that in women with adequate sleep (odds ratio [95% confidence interval] = 1.270 [1.058–1.525]), and this association was exclusively observed in the “KHEI ≤ median score” group (men, 59.95; women, 63.30). In conclusion, enhanced diet quality may act as an effect modifier in the association between insufficient sleep and a high obesity risk in women. These findings suggest that the association between sleep duration and obesity risk is potentially modified by dietary quality in adult women. Future studies with larger sample sizes and a prospective or interventional design are warranted to augment current knowledge regarding the association of diet quality/dietary patterns, and sleep duration with obesity.
SONIC SHAPES : VISUALIZI G VOCAL EXPRESSION

Sound has been an overlooked modality in visualization. Why? Because it is ephemeral. We experience it as it happens, often in community with others. Then, the sound is gone. Furthermore, sound in human communication is multidimensional and includes the semantic meaning of words, the meaning of expressive verbal gestures (paralingual and prosodic components), the nonvocal gestures, and relational gestures. Even though we are able to record sound and play it back, we typically focus more on the semantic meaning of words. In this paper, we describe a voice analytics toolkit and present visualizations that focus on the relational and expressive verbal gestures in speech. By making the overlooked channels of human communication visible and persistent, we make it possible to see beneath the surface of our words. This insight will potentially enable the development of new applications for speech therapy, the quantization and visualization of vocal trends common to speakers with medical conditions such as autism spectrum disorder (ASD), the characterization and visualization of communication patterns common in different kinds of relationships and cultures, and the development of new kinds of creative, multimodal works.
Caribbean Kinship as Instituted Process

ABSTRACT Islands are colonized by organized groups of people. This paper examines the ways in which the economy of colonizing groups is embedded in kinship institutions. This perspective emerged from substantivist and institutional economics, which both recognize economizing as embedded in kinship. Moreover, the formalist/substantivist debate highlighted the differences between empirical and theoretical perspectives, and the importance of societal norms in structuring rational decision-making (albeit the former emphasized profit maximization and the latter material want-satisfaction). None of these approaches fully integrated kinship analysis in their approach. This paper examines the Early Ceramic Age Caribbean (ECAC) from the perspective of kinship as an institution. The framework is drawn from ethnographic studies of matrilineal kinship because this is the descent system that most closely approximates evidence for the ECAC. The goal of this paper is to demonstrate certain insights gained from the investigation of kinship in the pre-Columbian Caribbean.
A Web-accessible Framework for Automated Storage with Compression and Textural Classification of Malaria Parasite Images

Malaria being one of the serious health burdens especially in Indian population is conventionally diagnosed by expert pathologists through microscopic observation of stained peripheral blood smears. In order to provide rapid and efficient healthcare support to the common people at rural areas where experts are not (often) available, there is indeed a requirement of developing web-enabled healthcare system. In view of this, in this study, a web-accessible framework for automated storage of compressed microscopic images and texture-based screening of malaria parasite has been developed to provide rapid and efficient diagnosis even at remote public health clinics. It consists of (a) automated storage of microscopic images followed by JPEG image compression for faster transmission; (b) watershed transform based erythrocyte segmentation followed by image preprocessing; (c) texture feature extraction and selection; and (d) supervised classification and validation. Here, total 76 textures are extracted from segmented erythrocytes. Twenty six significant features are selected by using SVM based recursive feature elimination (SVM-RFE) method. Thereafter, supervised classifiers viz. Naive Baye’s approach, C4.5 and NBTree are considered for six-class classification problem and their performance are compared. From the result, it has been found that NBTRee classifier provides higher accuracy to classify P. vivax and P. falciparum (sensitivity: 99.0%, specificity: 99.8%) with different stages viz. ring, gametocytes and scizon under our developed web-accessible framework.
Heterogeneous Multi-Channel Wireless Networks: Scheduling and Routing Issues

We consider multi-channel networks where nodes may be equipped with heterogeneous radios, each potentially capable of operation on a limited portion of the total availa ble spectrum. Moreover even the channels may not all be identical; they may possibly have different propagation ch aracteristics, and may support different sets of transmiss ion rates. Much prior research on multi-channel networks has assumed identical channels and radio capabilities. However heterogeneity of channels and radios introduces a host of new issues that must be handled. In recent theoretical work we considered asymptotic transport capacity of multi-channel networks subject to switching constraints. This constitutes a class of instances involving heterogeneous r adios, albeit identical channels. We leverage some of the insights obtained from our theoretical results, and now con sider a more general model involving heterogeneity of radios and channels for networks of realistic scale. We id entify the key issues that differentiate heterogeneous multi-channel networks, and describe a design framework for routing and channel/interface assignment.
MsHDZ23, a Novel Miscanthus HD-ZIP Transcription Factor, Participates in Tolerance to Multiple Abiotic Stresses

The homeodomain-leucine zipper (HD-ZIP) transcription factors, representing one of the largest plant-specific superfamilies, play important roles in the response to various abiotic stresses. However, the functional roles of HD-ZIPs in abiotic stress tolerance and the underlying mechanisms remain relatively limited in Miscanthus sinensis. In this study, we isolated an HD-ZIP TF gene, MsHDZ23, from Miscanthus and ectopically expressed it in Arabidopsis. Transcriptome and promoter analyses revealed that MsHDZ23 responded to salt, alkali, and drought treatments. The overexpression (OE) of MsHDZ23 in Arabidopsis conferred higher tolerance to salt and alkali stresses compared to wild-type (WT) plants. Moreover, MsHDZ23 was able to restore the hb7 mutant, the ortholog of MsHDZ23 in Arabidopsis, to the WT phenotype. Furthermore, MsHDZ23-OE lines exhibited significantly enhanced drought stress tolerance, as evidenced by higher survival rates and lower water loss rates compared to WT. The improved drought tolerance may be attributed to the significantly smaller stomatal aperture in MsHDZ23-OE lines compared to WT. Furthermore, the accumulation of the malondialdehyde (MDA) under abiotic stresses was significantly decreased, accompanied by dramatically enhanced activities in several antioxidant enzymes, including superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) in the transgenic plants. Collectively, these results demonstrate that MsHDZ23 functions as a multifunctional transcription factor in enhancing plant resistance to abiotic stresses.
Does Algorithmic Trading Improve Liquidity?

Algorithmic trading has sharply increased over the past decade. Equity market liquidity has improved as well. Are the two trends related? For a recent five-year panel of New York Stock Exchange (NYSE) stocks, we use a normalized measure of electronic message traffic (order submissions, cancellations, and executions) as a proxy for algorithmic trading, and we trace the associations between liquidity and message traffic. Based on within-stock variation, we find that algorithmic trading and liquidity are positively related. To sort out causality, we use the start of autoquoting on the NYSE as an exogenous instrument for algorithmic trading. Previously, specialists were responsible for manually disseminating the inside quote. As stocks were phased in gradually during early 2003, the manual quote was replaced by a new automated quote whenever there was a change to the NYSE limit order book. This market structure change provides quicker feedback to traders and algorithms and results in more message traffic. For large-cap stocks in particular, quoted and effective spreads narrow under autoquote and adverse selection declines, indicating that algorithmic trading does causally improve liquidity.
WebSets : Unsupervised Information Extraction approach to Extract Sets of Entities from the Web [ Extended Abstract ]

We propose an unsupervised information extraction system, which exploits the structured information in the form of HTML tables to build meaningful sets of entities belonging to certain categories. Due to redundancy on the Web, we believe that entities belonging to important categories will frequently co-occur in table columns. We present a clustering algorithm to cluster such frequently occurring entities into meaningful sets. Then we find candidate category names for each set, using Hearst patterns like “X such as Y”, “X including Y”. Experimental results on four different datasets show that our method can extract meaningful sets of entities (with avg. cluster precision of 97-99%). It also proposes reasonable category names for them. We present an application of this method to enhance an existing knowledge base. Experiments show that our method improves the coverage of existing categories with 80-90% accuracy. It also suggests new categories that can be added to the knowledge base.
Landmark Extraction from Echocardiography Sequence Based on Corner Detection Algorithms Using Gradient Vector Matcher

Landmark extraction is used as the first step of many vision tasks such as tracking, image registration, localization, image matching and recognition. Furthermore, landmarks are used to reduce the data flow and consequently the computational costs. In this paper we extracted landmarks from echocardiography sequence, our algorithm is based on corner extraction, and then we evaluate our algorithm with applying some test. For this purpose, we evaluated the detectors according to their repeatability, stability and landmark localization under changes in noise.
New Scheme to Identify Intrusion Outliers by Machine Learing Technique

In network administration, computers and network systems need to be protected against malicious attacks. The success of an intrusion detection system depends on the selection of the appropriate features in detecting the intrusion activity. The selection of unnecessary features may cause computational issues and reduce the accuracy of detection. In the existing work, a novel detection approach is used through a one-class learning algorithm based on support vector machine classification. It can be also used in a Bayesian framework to estimate the posterior class probabilities of test data with unknown class. This algorithm can detect the system anomalies and monitor the health of a system. It does not allow updating the training data with new information. Therefore, the accuracy of the algorithm is low for the test data. The proposed work aims to improve the performance of attack detection and to reduce the false-alarm rate using hybrid classifier. This approach effectively identifies the set of attacks such as Denial of Service, Probe, and User to Root and Remote to Local attacks. In addition, an Experimental evaluation is carried out to compare the performance of existing classifier with the proposed Decision tree-Bayesian network classifier.
Bronchopleural Fistula after Lobectomy for Lung Cancer: How to Manage This Life-Threatening Complication Using Both Old and Innovative Solutions

Simple Summary Bronchopleural fistula after lobectomy for lung cancer is a rare and life-threatening complication. Recognizing and managing this complication in time is often very complex for the clinicians and surgeons. Several surgical and endoscopic therapeutic approaches were described for the management of broncho-pleural fistula during the time period under study; however, the various instituted treatments depend much more on the experiences of individual centers than on a codified algorithm. Indeed, at present, there are no codified guidelines in the literature for its treatment. On the basis of our 25 years of experience, we tried to outline a diagnostic and therapeutic algorithm to help clinicians in the management of this feared complication Abstract Backgrounds: Our goal is to evaluate the correct management of broncho-pleural fistula (BPF) after lobectomy for lung cancer. Methods: We retrospectively reviewed our 25-years’ experience and reported our strategies and our diagnostic algorithm for the management of post-lobectomy broncho-pleural fistula. Results: Five thousand one hundred and fifty (5150) patients underwent lobectomy for lung cancer in the period between 1998 and 2023. A total of 44 (0.85%) out of 5150 developed post-operative BPF. In 11 cases, BPF was solved by non-invasive treatment. In nine cases, direct surgical repair of the bronchial stump allowed BPF resolution. In 14 cases, a completion intervention was performed. In six cases, we performed open window thoracostomy (OWT) after lobectomy; in two cases, the BPF was closed by percutaneous injection of an n-butyl cyanoacrylate glue mixture. In two cases, no surgical procedure was performed because of the clinical status of the patient at the time of fistula developing. Thirty-day and ninety-day mortality from fistula onset was, respectively, 18.2% (eight patients) and 22.7% (ten patients). Thirty-day and ninety-day mortality after completion pneumonectomy (12 patients) was, respectively, 8.3% (one patient) and 16.6% (two patients). Conclusions: The correct management of BPF depends on various factors: timing of onset, size of the fistula, anatomic localization, and the general condition of the patient. In the case of failure of various initial therapeutic approaches, completion intervention or OWT could be considered.
Recent advancement in studies of Deccan traps and its basement; Carbonatites and Kimberlites - An Indian Perspective in last five years

We made an attempt to provide a status report on volcanic rocks and mantle derived rocks with a special focus on studies related to Deccan flood basalt, carbonatites and kimberlites from India, during last five years. The important problems related to the Deccan volcanism include 1. Origin of Deccan Trap whether plume related or impact induced or triggered volcanism (2) the details of composition, the internal structure and age distribution and (3) the relation between the large igneous provinces and major mass extinction. Carbonatites are mantle derived rocks, which are helpful in modelling the Earth’s interior. They vary in age from Archean to recent, and are found mostly on continents and thus provide valuable information about the evolution of the sub-continental mantle through time. This article also reviews most of the research contributions on Indian carbonatites of the last five years. Building on existing information on their modes of occurrences, field dispositions, chronology, petrology and geochemistry, we use the recent data to provide a comprehensive view on the origin and evolution of these carbonatites.
Learning to Estimate Pose by Watching Videos

In this paper we propose a technique for obtaining coarse pose estimation of humans in an image that does not require any manual supervision. While a general unsupervised technique would fail to estimate human pose, we suggest that sufficient information about coarse pose can be obtained by observing human motion in multiple frames. Specifically, we consider obtaining surrogate supervision through videos as a means for obtaining motion based grouping cues. We supplement the method using a basic object detector that detects persons. With just these components we obtain a rough estimate of the human pose. With these samples for training, we train a fully convolutional neural network (FCNN)[20] to obtain accurate dense blob based pose estimation. We show that the results obtained are close to the ground-truth and to the results obtained using a fully supervised convolutional pose estimation method [31] as evaluated on a challenging dataset [15]. This is further validated by evaluating the obtained poses using a pose based action recognition method [5]. In this setting we outperform the results as obtained using the baseline method that uses a fully supervised pose estimation algorithm and is competitive with a new baseline created using convolutional pose estimation with full supervision.