Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning in the Aftermath of Extreme Floods: Community Damage and Stakeholder Perceptions of Future Risk

Policy learning in the aftermath of extreme events can happen as a result of changes in beliefs, attitudes, behaviors, and perceptions of stakeholders acting in a coordinated manner. Understanding the factors that impact these beliefs may prove critical in understanding policy learning and change, since these can mean the difference between ongoing flood vulnerability as a consequence of extreme weather events rather than long-term resilience. Data from in-depth interviews, stakeholder surveys, public meeting documents, and community demographics were used to analyze stakeholder processes and risk perceptions in seven Colorado communities that were flooded in 2013. Differences in extent of damages and resource capacity have led to a diversity of venues and participatory processes to manage flood recovery across the case communities. The results of the stakeholder survey suggest that perceptions of problem severity are linked to past flood experiences, type of expertise and job position. Taken together, these results suggest who participates in flood recovery processes, specifically their position and field of expertise, may influence how flood risks are perceived at the community level.
Design and implementation of an improved constant-Gm bias circuit

Automatic frequency tuning circuit of on-chip Gm-C filter has so many problems such as complex structure, high power consumption and low precision. In order to solve these problems, an improved constant-Gm bias circuit was designed in this paper. The circuit has been analyzed theoretically in detail. Simulation results show that the circuit simplified the structure of frequency tuning circuit, circuit area and power dissipation are reduced, and the deviation is less than 6.5% under different process corners and large temperature variation range. The circuit was applied to the 7th order low pass filter for wireless sensor chips.
IGF-I and IGF-II protect cultured hippocampal and septal neurons against calcium-mediated hypoglycemic damage

Insulin and insulin-like growth factors I and II (IGF-I and IGF-II) have recently been shown to have biological activity in central neurons, but their normal functions and mechanisms of action in the brain are unknown. Since central neurons are particularly vulnerable to hypoglycemia that results from ischemia or other insults, we tested the hypothesis that growth factors can protect central neurons against hypoglycemic damage in vitro. IGF-I and IGF-II (3–100 ng/ml) each prevented glucose deprivation-induced neuronal damage in a dose- dependent manner in rat hippocampal and septal cell cultures. High concentrations of insulin (greater than 1 microgram/ml) also protected neurons against hypoglycemic damage. Epidermal growth factor did not protect against hypoglycemic damage. Both IGFs and insulin were effective when administered 24 hr before or immediately following the onset of glucose deprivation. Direct measurements of intraneuronal calcium levels and manipulations of calcium influx demonstrated that calcium influx and sustained elevations in intraneuronal calcium levels mediated the hypoglycemic damage. IGF-I and IGF-II each prevented the hypoglycemia-induced elevations of intraneuronal free calcium. Studies with excitatory amino acid receptor antagonists and calcium channel blockers indicated that NMDA receptors did, and L-type calcium channels did not, play a major role in hypoglycemic damage. Taken together, these findings indicate that IGFs can stabilize neuronal calcium homeostasis and thereby protect against hypoglycemic damage.
Finite surgeries on three-tangle pretzel knots

We classify Dehn surgeries on (p, q, r) pretzel knots that result in a manifold of finite funda- mental group. The only hyperbolic pretzel knots that admit non-trivial finite surgeries are ( 2,3,7) and ( 2,3,9). Agol and Lackenby's 6-theorem reduces the argument to knots with small indices p, q, r. We treat these using the Culler-Shalen norm of the SL(2, C)-character variety. In particular, we introduce new techniques for demonstrating that boundary slopes are detected by the character variety.
Using virtual machines to do cross-layer damage assessment

In this paper, we present an approach that uses virtual machines to do ``out-of-the-box'' cross-layer damage assessment, an indispensable part of security/risk management. To resolve the conflict between fine-grained damage assessment and the response time requirements of service requests, we present a new production environment damage assessment architecture. We have implemented a major portion of the architecture and done preliminary evaluation. Contributions of our system include combining instruction and OS level taint tracking, and efficient ``what-if'' damage assessment methods.
Use of Dynamic Programming for Dense Depth Estimation from Uncalibrated Image Sets

An e cient algorithm to accurately estimate depth and occluded points from two or more uncalibrated views is presented. The basic concept is to apply a dynamic programming technique for displacement estimation, after estimating the exact positions of two corresponding epipolar lines. The ultimate goal is to be able to automatically generate accurate 3-D models from two or more views of a scene obtained from di erent viewing angles or even from standard monoscopic image sequences, without a-priori knowledge of the interior or exterior camera calibration parameters. Furthermore, the proposed approach provides a solution to the general correspondence problem for two or more uncalibrated camera views.
A Novel Neural Topic Model and Its Supervised Extension

Topic modeling techniques have the benefits of modeling words and documents uniformly under a probabilistic framework. However, they also suffer from the limitations of sensitivity to initialization and unigram topic distribution, which can be remedied by deep learning techniques. To explore the combination of topic modeling and deep learning techniques, we first explain the standard topic modelfrom the perspective of a neural network. Based on this, we propose a novel neural topic model (NTM) where the representation of words and documents are efficiently and naturally combined into a uniform framework. Extending from NTM, we can easily add a label layer and propose the supervised neural topic model (sNTM) to tackle supervised tasks. Experiments show that our models are competitive in both topic discovery and classification/regression tasks.
Journal of Theoretical and Applied Information Technology Optimization of Shunt Active Power Filter System Fuzzy Logic Controller Based on Ant Colony Algorithm

This paper presents the design of optimal fuzzy logic controller (FLC-ACO) – controlled shunt active power filter (SAPF) for harmonics compensation which is injected by nonlinear loads, such as rectifier equipment used in telecommunication system, power suppliers, domestic appliances, ect. Firstly, the controller designed according to Fuzzy logic rules is such that the system is fundamentally robust. In the second case, the optimal adjustment of the membership functions and normalization gains are realized by using Ant Colony Optimization. Simulations results show the best dynamic behaviour and performance control compared with traditional Fuzzy controller, as well as effective in reducing harmonic.
What future knowledge management users may expect

Globalization has placed businesses everywhere in new and different competitive situations where knowledgeable, effective behavior has come to provide the competitive edge. Enterprises have turned to explicit and systematic knowledge management (KM) to develop the intellectual capital needed to succeed. Further developments are expected to provide considerable benefits resulting from changes in the workplace and in management and operational practices. Changes will partly come from information technology and artificial intelligence developments. However, more important changes are expected in people‐centric practices to build, apply, and deploy knowledge and understanding for support of innovative and effective knowledge‐intensive work. Much remains to be done. Next generation KM methods will still be crude. Our understanding of knowledge and how people use it to work has a long way to go. We need a“theory of knowledge” and perhaps a new theory of the firm to create a solid foundation for future KM. Still, users can expect significant benefits from KM as it develops over the next decades.
A Two-Step Particle Filter for SLAM of Corridor Environment

The simultaneous localization and map building (SLAM) problem is critical to the research of mobile robot. Enabling robots to build the map of corridor environment is the basis for them to accomplish the tasks independently in the corridor environment. In this paper, the characteristic of the corridor environment is analyzed, the reason why it is hard to apply the general particle filter to solving the SLAM problem in the corridor environment is explored, and a novel two-step particle filter SLAM approach is presented. The experimental results demonstrate this approach can solve the correlation among states without the need of appending any artificial landmarks beforehand in the corridor environment in the process of SLAM
Redefining task interdependence in the context of supply networks

This paper clarifies the meaning of task interdependence and discusses its importance in heterarchic supply networks. Based on enactment theory (Weick, 1979) we develop the concept of enacted task interdependence and present a method for its empirical assessment. Empirical results from two forestry supply networks demonstrate the usefulness of the concept of enacted task interdependence. We find individuals to enact a composite of interdependencies in day-to-day activities. Further, individuals partly misperceive task interdependence which is likely to undermine coordination processes. Analyzing interdependence through an enactment lens helps to identify new approaches to collaboratively coordinate processes in heterarchic networks, e.g. through questioning and re-enacting task interdependencies.
Metaphors, stereotypes, and the linguistic picture of the world: impulses from the Ethnolinguistic School of Lublin

This article discusses possibilities for an elaboration of cognitive linguistic metaphor theory that takes into account the sociocultural situatedness of language and cognition. The approach of the Ethnolinguistic School of Lublin, linking anthropological with cognitive perspectives on language, is introduced. The objectives of the article are i) to introduce this line of research, well-known in linguistics in Eastern Europe, but little known in the “Western”, English speaking scientific discourse; ii) to illustrate the usefulness of particular ideas within this approach for metaphor analysis in a corpus study of the metaphorical understanding of system transformation in German public discourse in the late 1980s and early 1990s; and iii) to discuss diverging elaborations of the notion of experience in cognitive linguistics, contrasting the Ethnolinguistic School of Lublin with Conceptual Metaphor Theory. In diesem Artikel werden Moglichkeiten einer Ausarbeitung der kognitiv-linguistischen Metapherntheorie besprochen, mit dem Ziel, die soziokulturelle Situiertheit von Sprache und Kognition starker in den Mittelpunkt zu stellen. Der Ansatz der Lubliner Ethnolinguistischen Schule, die anthropologische und kognitive Perspektiven auf Sprache verbindet, wird vorgestellt. Die Ziele dieses Artikels sind es, i) diesen Ansatz in den englischsprachigen kognitiv-linguistischen Diskurs einzufuhren; ii) die Fruchtbarkeit einiger Ideen dieses Ansatzes fur die Metaphernanalyse zu illustrieren, und zwar durch eine Korpusanalyse des Verstandnisses der Systemtransformation in Deutschland in den spaten achtziger und fruhen neunziger Jahren des 20. Jahrhunderts im deutschen offentlichen Diskurs; und iii) verschiedene Ausarbeitungen des Erfahrungsbegriffs in der Kognitiven Linguistik zu diskutieren, wobei die Lubliner Ethnolinguistische Schule der Theorie der konzeptuellen Metapher gegenuber gestellt wird.
A classification of near-horizon geometries of extremal vacuum black holes

We consider the near-horizon geometries of extremal, rotating black hole solutions of the vacuum Einstein equations, including a negative cosmological constant, in four and five dimensions. We assume the existence of one rotational symmetry in four dimensions (4D), two commuting rotational symmetries in five dimensions (5D), and in both cases nontoroidal horizon topology. In 4D we determine the most general near-horizon geometry of such a black hole and prove it is the same as the near-horizon limit of the extremal Kerr-AdS4 black hole. In 5D, without a cosmological constant, we determine all possible near-horizon geometries of such black holes. We prove that the only possibilities are one family with a topologically S1×S2 horizon and two distinct families with topologically S3 horizons. The S1×S2 family contains the near-horizon limit of the boosted extremal Kerr string and the extremal vacuum black ring. The first topologically spherical case is identical to the near-horizon limit of two different black.
An Intelligent Technique for GeneratingEquivalent TT Circuits Using GeneticAlgorithm

The TT filter is a basic building block in many analog signal processing applications. Genetic algorithm applications in analog design circuits play an important role with promising results. This paper introduces the TT_GA Technique. It is an intelligent technique for generating equivalent TT Circuits using genetic algorithm realization with Nullor and Pathological elements. The goal is to generate equivalent Second Generation Current Conveyor (CCII) as well as equivalent Transconductance Amplifier (TA) circuits. This Technique generates 8 different TT circuits using CCII circuits. Also, it generates 16 circuits using TA. Some of thegenerated TTcircuits and the conventional op-amp realized filtersare simulated using SPICEand compared to each other’s.A standard CMOS model of 0.25 μm is used.By applying the TT_GA Technique we can get all possible solutions programmatically.
Latent Space Approaches to Social Network Analysis

Network models are widely used to represent relational information among interacting units. In studies of social networks, recent emphasis has been placed on random graph models where the nodes usually represent individual social actors and the edges represent the presence of a specified relation between actors. We develop a class of models where the probability of a relation between actors depends on the positions of individuals in an unobserved “social space. ” We make inference for the social space within maximum likelihood and Bayesian frameworks, and propose Markov chain Monte Carlo procedures for making inference on latent positions and the effects of observed covariates. We present analyses of three standard datasets from the social networks literature, and compare the method to an alternative stochastic blockmodeling approach. In addition to improving on model fit for these datasets, our method provides a visual and interpretable model-based spatial representation of social relationships and improves on existing methods by allowing the statistical uncertainty in the social space to be quantified and graphically represented.
Bundle Methods for Regularized Risk Minimization

A wide variety of machine learning problems can be described as minimizing a regularized risk functional, with different algorithms using different notions of risk and different regularizers. Examples include linear Support Vector Machines (SVMs), Gaussian Processes, Logistic Regression, Conditional Random Fields (CRFs), and Lasso amongst others. This paper describes the theory and implementation of a scalable and modular convex solver which solves all these estimation problems. It can be parallelized on a cluster of workstations, allows for data-locality, and can deal with regularizers such as L1 and L2 penalties. In addition to the unified framework we present tight convergence bounds, which show that our algorithm converges in O(1/e) steps to e precision for general convex problems and in O(log (1/e)) steps for continuously differentiable problems. We demonstrate the performance of our general purpose solver on a variety of publicly available data sets.
Relationship between cardiopulmonary fitness and depressive symptoms in cardiac rehabilitation patients with coronary artery disease.

OBJECTIVE To identify independent predictors of depressive symptoms in a cohort of patients with coronary artery disease entering cardiac rehabilitation. DESIGN Cross-sectional cohort study. PATIENTS AND METHODS Consecutive patients entering a cardiac rehabilitation and secondary prevention programme underwent screening for depressive symptoms using the Center for Epidemiological Studies Depression (CES-D) scale and cardiopulmonary fitness testing to quantify peak oxygen consumption. RESULTS Of the 366 patients with coronary artery disease, 22.3% reported at least mild (CES-D > or = 16) and 10.4% reported significant (CES-D > or = 23) depressive symptoms. Antidepressant medications were being used by 6.3% of patients. Sociodemographic, cardiopulmonary and cardiac characteristics, and medical co-morbidities previously associated with depression accounted for 14.7% of the variance in a multiple linear regression model (F = 8.713, p < 0.001) predicting CES-D scores. Significant independent predictors of CES-D scores were lower peak oxygen consumption, younger age, female sex, lower maximum diastolic blood pressure, angina pectoris and antidepressant use. CONCLUSION Reduced physical fitness, younger age, female sex and ischaemic symptoms of coronary artery disease predict higher depressive symptoms in patients entering cardiac rehabilitation.
Large-Margin Multi-Label Causal Feature Learning

In multi-label learning, an example is represented by a descriptive feature associated with several labels. Simply considering labels as independent or correlated is crude; it would be beneficial to define and exploit the causality between multiple labels. For example, an image label 'lake' implies the label 'water', but not vice versa. Since the original features are a disorderly mixture of the properties originating from different labels, it is intuitive to factorize these raw features to clearly represent each individual label and its causality relationship.Following the large-margin principle, we propose an effective approach to discover the causal features of multiple labels, thus revealing the causality between labels from the perspective of feature. We show theoretically that the proposed approach is a tight approximation of the empirical multi-label classification error, and the causality revealed strengthens the consistency of the algorithm. Extensive experimentations using synthetic and real-world data demonstrate that the proposed algorithm effectively discovers label causality, generates causal features, and improves multi-label learning.
CREB overexpression in dorsal CA1 ameliorates long-term memory deficits in aged rats

The molecular mechanisms underlying age-related cognitive deficits are not yet fully elucidated. In aged animals, a decrease in the intrinsic excitability of CA1 pyramidal neurons is believed to contribute to age-related cognitive impairments. Increasing activity of the transcription factor cAMP response element-binding protein (CREB) in young adult rodents facilitates cognition, and increases intrinsic excitability. However, it has yet to be tested if increasing CREB expression also ameliorates age-related behavioral and biophysical deficits. To test this hypothesis, we virally overexpressed CREB in CA1 of dorsal hippocampus. Rats received CREB or control virus, before undergoing water maze training. CREB overexpression in aged animals ameliorated the long-term memory deficits observed in control animals. Concurrently, cells overexpressing CREB in aged animals had reduced post-burst afterhyperpolarizations, indicative of increased intrinsic excitability. These results identify CREB modulation as a potential therapy to treat age-related cognitive decline. DOI: http://dx.doi. org/10.7554/eLife.19358. 001
A Theme-based Search Technique

The current search engines usually return a large number of irrelevant documents for a certain query. As a result, accessing such information and filtering out these documents can cause frustration and often result in waste of time and effort for the users while surfing the web. This is mainly because of the underlying techniques used in these engines. These techniques are mostly based in the frequency of the keywords of the query in the HTML code. In addition, issues such as dealing with classifying the pages found for a query according to previous visits along with features needed to make intelligent decisions regarding the access patterns of the users are not considered. This work presents an intelligent search engine, called ORCA that returns the most relevant documents for user's queries. This search engine analyses the queries and builds themes (models) to be used when the engine is confronted with similar queries. The intelligent component is used for constructing a model of the user behavior and using that model to fetch and even prefetch information and documents considered of interest to the user. It uses both latent semantic analysis and web page feature selection for clustering web pages. Latent semantic analysis is used to find the semantic relations between keywords, and between documents.
Multimodal Hand and Foot Gesture Interaction for Handheld Devices

We present a hand-and-foot-based multimodal interaction approach for handheld devices. Our method combines input modalities (i.e., hand and foot) and provides a coordinated output to both modalities along with audio and video. Human foot gesture is detected and tracked using contour-based template detection (CTD) and Tracking-Learning-Detection (TLD) algorithm. 3D foot pose is estimated from passive homography matrix of the camera. 3D stereoscopic and vibrotactile are used to enhance the immersive feeling. We developed a multimodal football game based on the multimodal approach as a proof-of-concept. We confirm our systems user satisfaction through a user study.
Influence of Global Atmospheric Change on the Feeding Behavior and Growth Performance of a Mammalian Herbivore, Microtus ochrogaster

Global atmospheric change is influencing the quality of plants as a resource for herbivores. We investigated the impacts of elevated carbon dioxide (CO2) and ozone (O3) on the phytochemistry of two forbs, Solidago canadensis and Taraxacum officinale, and the subsequent feeding behavior and growth performance of weanling prairie voles (Microtus ochrogaster) feeding on those plants. Plants for the chemical analyses and feeding trials were harvested from the understory of control (ambient air), elevated CO2 (560 µl CO2 l−1), and elevated O3 (ambient × 1.5) rings at the Aspen FACE (Free Air CO2 Enrichment) site near Rhinelander, Wisconsin. We assigned individual voles to receive plants from only one FACE ring and recorded plant consumption and weanling body mass for seven days. Elevated CO2 and O3 altered the foliar chemistry of both forbs, but only female weanling voles on the O3 diet showed negative responses to these changes. Elevated CO2 increased the fiber fractions of both plant species, whereas O3 fumigation elicited strong responses among many phytochemical components, most notably increasing the carbon-to-nitrogen ratio by 40% and decreasing N by 26%. Consumption did not differ between plant species or among fumigation treatments. Male voles were unaffected by the fumigation treatments, whereas female voles grew 36% less than controls when fed O3-grown plants. These results demonstrate that global atmospheric change has the potential to affect the performance of a mammalian herbivore through changes in plant chemistry.