Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital Leafleting: Extracting Structured Data from Multimedia Online Flyers

Marketing materials such as flyers and other infographics are a vast online resource. In a number of industries, such as the commercial real estate industry, they are in fact the only authoritative source of information. Companies attempting to organize commercial real estate inventories spend a significant amount of resources on manual data entry of this information. In this work, we propose a method for extracting structured data from free-form commercial real estate flyers in PDF and HTML formats. We modeled the problem as text categorization and Named Entity Recognition (NER) tasks and applied a supervised machine learning approach (Support Vector Machines). Our dataset consists of more than 2,200 commercial real estate flyers and associated manually entered structured data, which was used to automatically create training datasets. Traditionally, text categorization and NER approaches are based on textual information only. However, information in visually rich formats such as PDF and HTML is often conveyed by a combination of textual and visual features. Large fonts, visually salient colors, and positioning often indicate the most relevant pieces of information. We applied novel features based on visual characteristics in addition to traditional text features and show that performance improved significantly for both the text categorization and NER tasks.
Integrating symbolic execution with sensornet simulation for efficient bug finding

High-coverage testing of sensornet applications is vital for pre-deployment bug cleansing, but has previously been difficult due to the limited set of available tools. We integrate the KleeNet symbolic execution engine with the COOJA network simulator to allow for straight-forward and intuitive high-coverage testing initiated from a simulation environment. A tight coupling of simulation and testing helps detect, narrow down, and fix complex interaction bugs in an early development phase. We demonstrate the seamless transition between COOJA simulation and KleeNet symbolic execution. Our framework enables future research in how high-coverage testing tools could be used in cooperation with simulation tools.
Loss of cancellous bone mass and connectivity in ovariectomized rats can be restored by combined treatment with parathyroid hormone and estradiol.

To evaluate the potential use of a combination of antiresorption and bone formation-promoting agents as a treatment for postmenopausal osteoporosis, we examined the effects of combined and separate administration of estrogen (17 beta-estradiol, 30 micrograms/kg per d, s.c.) and parathyroid hormone (rPTH [1-34], 40 micrograms/kg per d, s.c.) on the proximal tibia of ovariectomized (Ovx) rats. The treatments lasted for 4 wk and were initiated 1, 3, and 5 wk after surgery. Ovx resulted in rapid loss of cancellous bone volume (Cn-BV/TV) as well as trabecular connectivity, as determined by two dimensional strut analysis. When administered in a preventive mode, treatment beginning 1 wk post-Ovx, estrogen or PTH treatment alone preserved Cn-BV/TV and trabecular connectivity, and combined estrogen and PTH treatment caused a 40% increment in Cn-BV/TV while maintaining comparable trabecular connectivity with that seen in the Sham-operated animals. When administered in a curative mode to rats with established osteoporosis, treatments beginning 3 or 5 wk post-Ovx, estrogen or PTH treatment alone prevented further loss of connectivity and Cn-BV/TV, whereas the combined treatment resulted in as much as a 300% improvement in one of the parameters of trabecular connectivity, node to node strut length, and a 106% increase in Cn-BV/TV, with respect to the bone status at the initiation of treatment. The beneficial effects of this combined treatment derive from estrogen's ability to prevent accelerated bone resorption and, simultaneously, PTH's promotion of bone formation. These data demonstrate, in an animal model, that therapies can be devised to cure the skeletal defects associated with established osteoporosis.
Expression and Function of Chloride Transporters during Development of Inhibitory Neurotransmission in the Auditory Brainstem

Glycine and GABA, the dominant inhibitory neurotransmitters in the CNS, assume a depolarizing role in early development, leading to increased cytoplasmic Ca2+ levels and action potentials. The effect is thought to be of some significance for maturation. The depolarization is caused by Cl− efflux, and chloride transporters contribute to the phenomenon by raising the intracellular Cl− concentration ([Cl−]i) above equilibrium, thereby generating an outward-directed electrochemical gradient for Cl−. In mature neurons, the [Cl−]i is reduced below equilibrium, thus rendering glycine activity hyperpolarizing. Here, we investigated the temporal expression of the K-Cl cotransporter KCC2 and the Na-K-Cl cotransporter NKCC1 in the lateral superior olive (LSO) of rats and mice. The two cation cotransporters normally extrude and accumulate Cl−, respectively. As evidenced by several methods, KCC2 mRNA was present in LSO neurons during both the depolarizing and hyperpolarizing periods. Western blots confirmed a constant level of KCC2 in the brainstem, and immunohistochemistry showed that the protein is diffusely distributed within neonatal LSO neurons, becoming integrated into the plasma membrane only with increasing age. The glycine reversal potential in KCC2 knock-out mice differed significantly from that determined in wild-type controls at postnatal day 12 (P12) but not at P3, demonstrating that KCC2 is not active in neonates, despite its early presence. NKCC1 mRNA was not detected during the depolarizing phase in the LSO, implying that this transporter does not contribute to the high [Cl−]i. Our results reveal major differences in the development of [Cl−]i regulation mechanisms seen in brainstem versus forebrain regions.
Complete and easy bidirectional typechecking for higher-rank polymorphism

Bidirectional typechecking, in which terms either synthesize a type or are checked against a known type, has become popular for its scalability (unlike Damas-Milner type inference, bidirectional typing remains decidable even for very expressive type systems), its error reporting, and its relative ease of implementation. Following design principles from proof theory, bidirectional typing can be applied to many type constructs. The principles underlying a bidirectional approach to polymorphism, however, are less obvious. We give a declarative, bidirectional account of higher-rank polymorphism, grounded in proof theory; this calculus enjoys many properties such as eta-reduction and predictability of annotations. We give an algorithm for implementing the declarative system; our algorithm is remarkably simple and well-behaved, despite being both sound and complete.
G9a drives hypoxia-mediated gene repression for breast cancer cell survival and tumorigenesis

Significance The hypoxic microenvironment in solid tumors is known to increase the aggressiveness of cancer cells by enhancing proliferative and metastatic potential and reduces the efficacy of radiation and chemotherapy. Previous studies have shown that G9a protein accumulates in hypoxic conditions. However, neither a detailed molecular mechanism nor its functional role has been elucidated so far. This study investigates the role G9a plays in gene expression in hypoxic conditions and its impact on tumor growth. G9a inhibition studies and the metaanalysis of G9a-suppressed gene signature using several breast cancer gene expression databases revealed that G9a is a potential therapeutic target in breast cancer. G9a is an epigenetic regulator that methylates H3K9, generally causing repression of gene expression, and participates in diverse cellular functions. G9a is genetically deregulated in a variety of tumor types and can silence tumor suppressor genes and, therefore, is important for carcinogenesis. Although hypoxia is recognized to be an adverse factor in tumor growth and metastasis, the role of G9a in regulating gene expression in hypoxia has not been described extensively. Here, we show that G9a protein stability is increased in hypoxia via reduced proline hydroxylation and, hence, inefficient degradation by the proteasome. This inefficiency leads to an increase in H3K9me2 at its target promoters. Blocking the methyltransferase activity of G9a inhibited cellular proliferation and migration in vitro and tumor growth in vivo. Furthermore, an increased level of G9a is a crucial factor in mediating the hypoxic response by down-regulating the expression of specific genes, including ARNTL, CEACAM7, GATA2, HHEX, KLRG1, and OGN. This down-regulation can be rescued by a small molecule inhibitor of G9a. Based on the hypothesis that the changes in gene expression would influence patient outcomes, we have developed a prognostic G9a-suppressed gene signature that can stratify breast cancer patients. Together, our findings provide an insight into the role G9a plays as an epigenetic mediator of hypoxic response, which can be used as a diagnostic marker, and proposes G9a as a therapeutic target for solid cancers.
High quality triangulation of implicit surfaces

We present a new high quality tessellation method for implicit surfaces in this paper. The approach can handle arbitrary implicit functions and dynamic implicit surfaces based on skeletal primitives. We first samples the implicit surface uniformly using particle fission and floating, then reconstructs a triangular mesh from the sample points using ball pivoting algorithm (BPA). Finally, we subdivide the reconstructed surface using a 1 to 4 subdivision scheme to obtain the high quality implicit surface tessellation.
Robust audiovisual integration using semicontinuous hidden Markov models

Describe an improved method of integrating audio and visual information in a hidden Markov model (HMM) based audio-visual automatic speech recognition (ASR) system. The method uses a modified semi-continuous HMM (SCHMM) for integration and recognition. Our results show substantial improvements over earlier integration methods at high noise levels. Our integration method relies on the assumption that, as environmental conditions deviate from those under which training occurred, the underlying probability distributions also change. We use phoneme-based SCHMMs for classification of isolated words. The probability models underlying the standard SCHMM are Gaussian; thus, low probability estimates tend to be associated with high confidences (small differences in the feature values cause large proportional differences in probabilities, when the values are in the tail of the distribution). Therefore, during classification, we replace each Gaussian with a scoring function which looks Gaussian near the mean of the distribution but has a heavier tail. We report results comparing this method with an audio-only system and with previous integration methods. At high noise levels, the system with modified scoring functions shows a better than 50% improvement; however, recognition does suffer when noise is low. Methods which can adjust the relative weight of the audio and visual information can still potentially outperform the new method, provided that a reliable way of choosing those weights can be found.
Effect of intrahippocampal administration of vitamin C and progesterone on learning in a model of multiple sclerosis in rats.

PURPOSE The purpose of this study was to evaluate the effect of intrahippocampal injection of vitamin C and progesterone, alone or in combination, on passive avoidance learning (PAL) in multiple sclerosis. METHODS Sixty- three male wistar rats were divided into nine groups (n=7) as following: control (saline), lesion, vitamin C (0.2, 1, 5 mg/kg), progesterone (0.01, 0.1, 1 µg/µl) and combination therapy. Lesion was induced by intrahippocampal injection of ethidium bromide. In combination therapy, animals were treated with vitamin C (5 mg/kg) plus progesterone (0.01 mg/kg). Animals in experimental groups received different treatments for 7 days, and then all groups were tested for step through latency (STL). RESULTS Our results showed that intrahippocampal injection of ethidium bromide destroys PAL significantly (p<0.001). Treatment with vitamin C (5mg/kg) significantly (p<0.05) improved PAL. Lower doses of progesterone did not affect latency but dose of 1 µg/µl significantly (p<0.05) increased STL. In combination therapy group STL was significantly (p<0.05) more than in the lesion group, although it was not significantly different from the vitamin C group. CONCLUSION Based on our results, we concluded that intrahippocampal injection of vitamin C improves memory for PAL, but progesterone alone or in combination with vitamin C had no improving effects on memory.
A holobiont birth narrative: the epigenetic transmission of the human microbiome

This essay plans to explore, expand, and re-tell the human birth narrative. Usually, human birth narratives focus on the origins of a new individual, focusing on the mother and fetus. This essay discusses birth as the origin of a new community. For not only is the eukaryotic body being reproduced, but so also are the bodies of its symbiotic microbes and so is the set of relationships between these organic components. Several parts of the new narrative are surprising: (1) bacterial symbionts might cause some of the characteristics of pregnancy and prepare a symbiotic community for transfer; (2) the first bacterial colonizers of the mammalian organism my enter the fetus prior to the lysing of the amniotic membrane and birth; (3) the same signals that often cause immunological attack against a microbe may serve under these conditions to signal homeostatic stability between symbiont and host; and (4) the mother may actively provide substances that promote the growth and settlement of helpful bacteria. The birth of the holobiont exemplifies principles of co-evolution, co-development, niche construction, and scaffolding. Birth is nothing less than the passage from one set of symbiotic relationships to another.
Shape Saliency Modulates Contextual Processing in the Human Lateral Occipital Complex

Visual context influences our perception of target objects in natural scenes. However, little is known about the analysis of context information and its role in shape perception in the human brain. We investigated whether the human lateral occipital complex (LOC), known to be involved in the visual analysis of shapes, also processes information about the context of shapes within cluttered scenes. We employed an fMRI adaptation paradigm in which fMRI responses are lower for two identical than for two different stimuli presented consecutively. The stimuli consisted of closed target contours defined by aligned Gabor elements embedded in a background of randomly oriented Gabors. We measured fMRI adaptation in the LOC across changes in the context of the target shapes by manipulating the position and orientation of the background elements. No adaptation was observed across context changes when the background elements were presented in the same plane as the target elements. However, adaptation was observed when the grouping of the target elements was enhanced in a bottom-up (i.e., grouping by disparity or motion) or top-down (i.e., shape priming) manner and thus the saliency of the target shape increased. These findings suggest that the LOC processes information not only about shapes, but also about their context. This processing of context information in the LOC is modulated by figureground segmentation and grouping processes. That is, neural populations in the LOC encode context information when relevant to the perception of target shapes, but represent salient targets independent of context changes.
Vicarious traumatisation: risk and resilience among crisis support volunteers in a community organisation

This study aims to understand the risks of vicarious trauma and possibilities of resilience among volunteers at an organisation that supports survivors of intimate-partner violence. The analysis identifies the dynamics of the organisation and the risk of vicarious traumatisation that the volunteers faced. The participants explored the personal self-care techniques they found effective and the organisational support available. The findings indicate that there was some degree of recognition of the risk by individual support workers, and also some attempts by the organisation to offer support, but these were undermined by a prevailing stigma among volunteers associated with admitting and showing vulnerability and inadequacy. Such denialism has a negative impact on both volunteers and organisational support structures, and also indirectly affects the clients seeking support. This analysis supports the importance of dealing with vicarious traumatisation in the training of volunteers and makes recommendations for the ongoing functioning of organisations, in order to facilitate effective recognition, non-stigmatised acceptance, and management of vicarious traumatisation among crisis support workers.
A Tree-Based Data Perturbation Approach for Privacy-Preserving Data Mining

Due to growing concerns about the privacy of personal information, organizations that use their customers' records in data mining activities are forced to take actions to protect the privacy of the individuals. A frequently used disclosure protection method is data perturbation. When used for data mining, it is desirable that perturbation preserves statistical relationships between attributes, while providing adequate protection for individual confidential data. To achieve this goal, we propose a kd-tree based perturbation method, which recursively partitions a data set into smaller subsets such that data records within each subset are more homogeneous after each partition. The confidential data in each final subset are then perturbed using the subset average. An experimental study is conducted to show the effectiveness of the proposed method
Tradeoff Between Stability and Multispecificity in the Design of Promiscuous Proteins

Natural proteins often partake in several highly specific protein-protein interactions. They are thus subject to multiple opposing forces during evolutionary selection. To be functional, such multispecific proteins need to be stable in complex with each interaction partner, and, at the same time, to maintain affinity toward all partners. How is this multispecificity acquired through natural evolution? To answer this compelling question, we study a prototypical multispecific protein, calmodulin (CaM), which has evolved to interact with hundreds of target proteins. Starting from high-resolution structures of sixteen CaM-target complexes, we employ state-of-the-art computational methods to predict a hundred CaM sequences best suited for interaction with each individual CaM target. Then, we design CaM sequences most compatible with each possible combination of two, three, and all sixteen targets simultaneously, producing almost 70,000 low energy CaM sequences. By comparing these sequences and their energies, we gain insight into how nature has managed to find the compromise between the need for favorable interaction energies and the need for multispecificity. We observe that designing for more partners simultaneously yields CaM sequences that better match natural sequence profiles, thus emphasizing the importance of such strategies in nature. Furthermore, we show that the CaM binding interface can be nicely partitioned into positions that are critical for the affinity of all CaM-target complexes and those that are molded to provide interaction specificity. We reveal several basic categories of sequence-level tradeoffs that enable the compromise necessary for the promiscuity of this protein. We also thoroughly quantify the tradeoff between interaction energetics and multispecificity and find that facilitating seemingly competing interactions requires only a small deviation from optimal energies. We conclude that multispecific proteins have been subjected to a rigorous optimization process that has fine-tuned their sequences for interactions with a precise set of targets, thus conferring their multiple cellular functions.
Crystal Structure of Human Protein-tyrosine Phosphatase SHP-1*

SHP-1 is a cytosolic protein-tyrosine phosphatase that behaves as a negative regulator in eukaryotic cellular signaling pathways. To understand its regulatory mechanism, we have determined the crystal structure of the C-terminal truncated human SHP-1 in the inactive conformation at 2.8-Å resolution and refined the structure to a crystallographic R-factor of 24.0%. The three-dimensional structure shows that the ligand-free SHP-1 has an auto-inhibited conformation. Its N-SH2 domain blocks the catalytic domain and keeps the enzyme in the inactive conformation, which supports that the phosphatase activity of SHP-1 is primarily regulated by the N-SH2 domain. In addition, the C-SH2 domain of SHP-1 has a different orientation from and is more flexible than that of SHP-2, which enables us to propose an enzymatic activation mechanism in which the C-SH2 domains of SHPs could be involved in searching for phosphotyrosine activators.
Shortfall Risk Approximations for American Options in the Multidimensional Black-Scholes Model

We show that shortfall risks of American options in a sequence of multinomial approximations of the multidimensional Black-Scholes (BS) market converge to the corresponding quantities for similar American options in the multidimensional BS market with path-dependent payoffs. In comparison to previous papers we consider the multiassets case for which we use the weak convergence approach.
Supervised Term Weighting Metrics for Sentiment Analysis in Short Text

Term weighting metrics assign weights to terms in order to discriminate the important terms from the less crucial ones. Due to this characteristic, these metrics have attracted growing attention in text classification and recently in sentiment analysis. Using the weights given by such metrics could lead to more accurate document representation which may improve the performance of the classification. While previous studies have focused on proposing or comparing different weighting metrics at two-classes document level sentiment analysis, this study propose to analyse the results given by each metric in order to find out the characteristics of good and bad weighting metrics. Therefore we present an empirical study of fifteen global supervised weighting metrics with four local weighting metrics adopted from information retrieval, we also give an analysis to understand the behavior of each metric by observing and analysing how each metric distributes the terms and deduce some characteristics which may distinguish the good and bad metrics. The evaluation has been done using Support Vector Machine on three different datasets: Twitter, restaurant and laptop reviews.
Non-Verbal Feedback, Strategic Signaling and Non-Monetary Sanctioning: New Experimental Evidence from a Public Goods Games

Several experiments show that feedback transmission mechanisms mitigate opportunistic behavior in social dilemmas. The source of this effect, especially in a repeated interaction, nonetheless remains obscure. This study provides a novel empirical testbed for channels by which feedback may affect behavior in a repeated public goods game. One is related to strategic signaling. The other involves aversion to others' expressed disapproval. The presence of feedback is found to foster pro-social behavior. The data favour the non-monetary sanctioning explanation rather than the signaling hypothesis. (This abstract was borrowed from another version of this item.)