Miracle-dz/UniFAR-FTUs huggingface dataset:
Racialization of News: Constructing and Challenging Professional Journalism as “White Media”

This article explores the persistent racialization of professional journalism, describing the implicit processes that define “mainstream” news as white media. We emphasize the whiteness of U.S. news as emanating from cultural practices of professional journalism and institutional forces shaping the journalistic field rather than simply the demographic characteristics of the newsroom workforce. In theorizing how news has been constructed as white, we describe the historical foundations of the cultural authority of news and point to how such racialized authority has always been subject to enduring challenge. We analyze the complex cultural and political challenges that the Black press in the United States has long represented to the power of white media and racism; the Black press represents an alternative practice of journalism, one that critiques traditional notions of objectivity and situates news as a voice for equality and social justice. We close by discussing the newly resurgent white nationalist media and recent controversies surrounding prominent black female journalists as examples highlighting the structural limitations of white professional news media. Ultimately, we seek to understand the emergent racial dynamics of the journalistic field and how objectivity and white racial power are challenged and reaffirmed in our contemporary mediascape.
Are the Tradeoffs between Performance and Robustness Intrinsic for Feedback Systems

A novel soybean cultivar, designated 916119311654, is disclosed. The invention relates to the seeds of soybean cultivar 916119311654, to the plants of soybean 916119311654 and to methods for producing a soybean plant produced by crossing the cultivar 916119311654 with itself or another soybean variety. The invention further relates to hybrid soybean seeds and plants produced by crossing the cultivar 916119311654 with another soybean cultivar.
IMPROVING THE PERFORMANCE OF GRID-TIED AC MICROGRIDS INCLUDING RENEWABLE GENERATION BY DISTRIBUTED ENERGY

Grid connection of renewable energy sources (RESs), such as wind and solar, is becoming today an important form of distributed generation (DG). The penetration of these DG units in electrical microgrids (MGs) is growing rapidly, enabling reaching high percentage of installed generating capacity. However, the fluctuating and intermittent nature of these renewable generations causes fluctuations of power flow that can significantly affect the stability and operation of the electrical grid. This condition is worsened by to the low inertia present in the microgrid; thus requiring to have available additional fast-response spinning reserve. To overcome these problems, distributed energy storage (DES) systems based on advanced technologies, such as superconducting magnetic energy storage (SMES), supercapacitors and flywheels, arise as a potential alternative in order to balance any instantaneous mismatch in the active power of the microgrid. This paper proposes the use of these new DES technologies for the stabilization and control of the power flow of AC microgrids with RESs. In this sense, the design and implementation of the proposed DES systems is described. Moreover, the dynamic behavior of the presented models and their impact on the microgrid performance are evaluated by computer simulation.
The Unconventional Secretory Machinery of Fibroblast Growth Factor 2

Unconventional secretory proteins represent a subpopulation of extracellular factors that are exported from eukaryotic cells by mechanisms that do not depend on the endoplasmic reticulum and the Golgi complex. Various pathways have been implicated in unconventional secretion including those involving intracellular membrane‐bound intermediates and others that are based on direct protein translocation across plasma membranes. Interleukin 1β (IL1β) and fibroblast growth factor 2 (FGF2) are classical examples of unconventional secretory proteins with IL1β believed to be present in intracellular vesicles prior to secretion. By contrast, FGF2 represents an example of a non‐vesicular mechanism of unconventional secretion. Here, the author discusses the current knowledge about the molecular machinery being involved in FGF2 secretion. To reveal both differential and common requirements, this review further aims at a comprehensive comparison of this mechanism with other unconventional secretory processes. In particular, a potentially general role of tyrosine phosphorylation as a regulatory signal in unconventional protein secretion will be discussed.
Trajectory Algorithm for Localization of a Mobile Beacon in Wireless Sensor Networks

After the general principle and the calculation process of some localization algorithms of wireless sensor network being introduced and analyzed, a novel algorithm of mobile beacon trajectory is proposed in this paper. Since a static trajectory does not work well in non-uniform, and a virtual force trajectory has some defects like worse stability, a combination trajectory is proposed, which is a new dynamic trajectory. Simulation results demonstrate that this trajectory can obtain a higher coverage rate. And simulation results of the algorithm based on RSSI with this trajectory demonstrate that the proposed method, which can obtain about 10% coverage rate under the situation that whether the unknown nodes distribute uniformly or not, is efficient and flexible.
Reducing instruction fetch cost by packing instructions into register windows

Instruction packing is a combination compiler/architectural approach that allows for decreased code size, reduced power consumption and improved performance. The packing is obtained by placing frequently occurring instructions into an instruction register file (IRF). Multiple IRF entries can then be accessed using special packed instructions. Previous IRF efforts focused on using a single 32-entry register file for the duration of an application. This paper presents software and hardware extensions to the IRF supporting multiple instruction register windows to allow a greater number of relevant instructions to be available for packing in each function. Windows are shared among similar functions to reduce the overall costs involved in such an approach. The results indicate that significant improvements in instruction fetch cost can be obtained by using this simple architectural enhancement. We also show that using an IRF with a loop cache, which is also used to reduce energy consumption, results in much less energy consumption than using either feature in isolation.
Factors Influencing Online and Post-purchase Behavior and Construction of Relevant Models

ABSTRACT This study is attempted to combine the decomposition theory of planned behavior with the theories of relationship quality and product involvement to establish a complete model for the explanation of factors influencing online investment and post-purchase behavior. The SEM causal model was used to verify the capability of the model to explain the online investment and post-purchase behavior of consumers. Consumers in the top four largest cities in Taiwan who invest in financial products via banks were selected, and the number of research subjects was determined by the population of each city. The stratified random sampling method was used for the survey and 274 valid questionnaires were returned. The SEM model was used for data verification. In the preliminary fit, the financial support of family members has the highest influence on the decision of consumers (subjective norm), the incorrectness of product information announced by service providers is perceived by consumers as the highest risk (perceived risk), and the attractiveness of products is the most important variable to arouse the interest of consumers to buy (product involvement). As for the actual behavior, personal requirements for investment are the most decisive factor for consumers to take actions. The correctness of products is the most influential factor for the gap of perceived service quality. Among the parameters under the post-purchase behavior, the external response and internal response are most decisive for consumers to take actions after they have made a purchase. As for the internal fit, the subjective norm to actual behavior, perceived risk to actual behavior, subjective norm to post-purchase behavior, and gap of perceived service quality to post-purchase behavior reach the significant level and the overall goodness-of-fit of the research model was satisfactory.
Speech Based Machine Learning Models for Emotional State Recognition and PTSD Detection

SPEECH BASED MACHINE LEARNING MODELS FOR EMOTIONAL STATE RECOGNITION AND PTSD DETECTION Debrup Banerjee Old Dominion University, 2017 Director: Dr. Jiang Li Recognition of emotional state and diagnosis of trauma related illnesses such as posttraumatic stress disorder (PTSD) using speech signals have been active research topics over the past decade. A typical emotion recognition system consists of three components: speech segmentation, feature extraction and emotion identification. Various speech features have been developed for emotional state recognition which can be divided into three categories, namely, excitation, vocal tract and prosodic. However, the capabilities of different feature categories and advanced machine learning techniques have not been fully explored for emotion recognition and PTSD diagnosis. For PTSD assessment, clinical diagnosis through structured interviews is a widely accepted means of diagnosis, but patients are often embarrassed to get diagnosed at clinics. The speech signal based system is a recently developed alternative. Unfortunately, PTSD speech corpora are limited in size which presents difficulties in training complex diagnostic models. This dissertation proposed sparse coding methods and deep belief network models for emotional state identification and PTSD diagnosis. It also includes an additional transfer learning strategy for PTSD diagnosis. Deep belief networks are complex models that cannot work with small data like the PTSD speech database. Thus, a transfer learning strategy was adopted to mitigate the small data problem. Transfer learning aims to extract knowledge from one or more source tasks and apply the knowledge to a target task with the intention of improving the learning. It has proved to be useful when the target task has limited high quality training data. We evaluated the proposed methods on the speech under simulated and actual stress database (SUSAS) for emotional state recognition and on two PTSD speech databases for PTSD diagnosis. Experimental results and statistical tests showed that the proposed models outperformed most state-of-the-art methods in the literature and are potentially efficient models for emotional state recognition and PTSD diagnosis.
Rapid Prototyping of Energy Management Applications with FRESH

Home energy management systems (HEMS) can reduce energy consumption while maintaining comfort. However, HEMS suffer from slow adoption and unproven energy savings. As intelligent automation and persuasive design features are increasingly added to HEM products, many research questions remain regarding their efficacy. We present the FRESH research platform to facilitate (i) the development of innovative features in HEM applications, and (ii) their evaluation in field deployments. FRESH has extensible modules for (a) sensing to acquire data from wireless hardware sensor nodes and online sources, and actuation to control custom devices and commercial-off-the-shelf appliances, (b) adaptive and intelligent modeling to test various machine learning and control algorithms, (c) flexible user interface to explore the design of features based on behavioral research, and (d) field experimentation with tools for user interaction logging, automatic context-aware surveys and messaging, and data visualization. We have deployed FRESH in residential apartments and commercial offices used as test beds. We demonstrate the versatility of FRESH with four application use cases: environmental monitoring, non-intrusive load monitoring (NILM), an elevator energy display, and a smart thermostat. We have used FRESH to iterate on sensor nodes, develop and evaluate intelligent algorithms and models, and design mobile and web user interfaces to investigate HEM usability.
Safety Assessment of Bacteroides uniformis CECT 7771 Isolated from Stools of Healthy Breast-Fed Infants

Background Bacteroides uniformis CECT 7771 is a potential probiotic strain, originally isolated from the stools of healthy breast-feed infants. The strain showed pre-clinical efficacy in a mouse obesity model. The objective of this study was to evaluate its potential toxicity and translocation ability after acute oral administration to mice. Methods and Findings A safety study was conducted in immunocompetent and immunosuppressed C57BL-6 mice. Both mouse groups (n = 10 per group) were fed orally 2 x 109 colony forming units (cfu)/day of B. uniformis CECT 7771 or placebo by gavage for 6 days. Throughout this time, feed and water intake and body weight were monitored. Afterwards, mice were sacrificed and biological samples were collected to analyze blood and urine biochemistry, inflammatory and immune markers; gut mucosal histology and bacterial translocation to peripheral tissues. The results demonstrated that acute ingestion of this Bacteroides strain had no adverse effects on the animals’ general health status or food intake, nor did it affect biochemical indicators of liver, kidney and pancreatic function or gut mucosal histology. Findings also demonstrated that administration did not lead to bacterial translocation to blood, liver or mesenteric lymph nodes. B. uniformis CECT 7771 also downregulated gene and protein expression (iNOS and PPAR-γ) and inflammatory cytokines induced by immunosuppression. Conclusions The findings indicate that the acute oral consumption of B. uniformis CECT 7771 does not raise safety concerns in mice. Further studies in humans should be conducted.
Higher-Order CRF Structural Segmentation of 3D Reconstructed Surfaces

In this paper, we propose a structural segmentation algorithm to partition multi-view stereo reconstructed surfaces of large-scale urban environments into structural segments. Each segment corresponds to a structural component describable by a surface primitive of up to the second order. This segmentation is for use in subsequent urban object modeling, vectorization, and recognition. To overcome the high geometrical and topological noise levels in the 3D reconstructed urban surfaces, we formulate the structural segmentation as a higher-order Conditional Random Field (CRF) labeling problem. It not only incorporates classical lower-order 2D and 3D local cues, but also encodes contextual geometric regularities to disambiguate the noisy local cues. A general higher-order CRF is difficult to solve. We develop a bottom-up progressive approach through a patch-based surface representation, which iteratively evolves from the initial mesh triangles to the final segmentation. Each iteration alternates between performing a prior discovery step, which finds the contextual regularities of the patch-based representation, and an inference step that leverages the regularities as higher-order priors to construct a more stable and regular segmentation. The efficiency and robustness of the proposed method is extensively demonstrated on real reconstruction models, yielding significantly better performance than classical mesh segmentation methods.
It's input that matters most, not age

The splendid review provided by Mayberry and Kluender ( 2017 ) confirms Lenneberg's 1967 hypothesis that a Critical Period (CP) exists for the establishment of a native language. However, Lenneberg's extension of the CP hypothesis to the later learning of an L2, based on his observation that most individuals who learn an L2 after about the age of 13 speak it with a foreign accent (FA), is doubtful.
Long Memory, the 'Taylor Effect' and Intraday Volatility in Commodity Futures Markets

This paper investigates long-term dependence in commodity futures markets. Using daily futures returns on cocoa, coffee and sugar, we show that FIGARCH models are able to adequately describe both the long and the short run characteristics of commodity market volatility. The paper also considers three measures of risk n squared returns, absolute returns and ranges (intraday volatility) - and finds that they exhibit the long memory property. The range shows the strongest autocorrelation structure. Moreover, there is evidence of the so-called iTaylor effecti.
Domain term relevance through tf-dcf

—This paper proposes a new index for the relevance of terms extracted from domain corpora. We call it term frequency, disjoint corpora frequency ( tf-dcf ), and it is based on the absolute term frequency of each term tempered by its frequency in other (contrasting) corpora. Conceptual differences and mathematical computation of the proposed index are discussed in respect with other similar approaches that also take the frequency in contrasting corpora into account. To illustrate the efﬁciency of the tf-dcf index, this paper evaluates the application of this index and other similar approaches.
Knowledge discovery in virtual community texts: Clustering virtual communities

Automatic knowledge discovery from texts (KDT) is proving to be a promising method for businesses today to deal with the overload of textual information. In this paper, we first explore the possibilities for KDT to enhance communication in virtual communities, and then we present a practical case study with real-life Internet data. The problem in the case study is to manage the very successful virtual communities known as 'clubs' of the largest Dutch Internet Service Provider. It is possible for anyone to start a club about any subject, resulting in over 10,000 active clubs today. At the beginning, the founder assigns the club to a predefined category. This often results in illogical or inconsistent placements, which means that interesting clubs may be hard to locate for potential new members. The ISP therefore is looking for an automated way to categorize clubs in a logical and consistent manner. The method used is the so-called bag-of-words approach, previously applied mostly to scientific texts and structured documents. Each club is described by a vector of word occurrences of all communications within that club. Latent Semantic Indexing (LSI) is applied to reduce the dimensionality problem prior to clustering. Clustering is done by the Within Groups Clustering method using a cosine distance measure appropriate for texts. The results show that KDT and the LSI method can successfully be applied for clustering the very volatile and unstructured textual communication on the Internet.
MATRIX at the TREC 2005 Robust Track

In the TREC 2005 robust retrieval track, we tested our adaptive retrieval model that automatically switches between the 2-Poisson model/adaptive vector space model and our initial predictive probabilistic context-based model depending on some query characteristics. Our 2-Poisson model uses the BM11 term weighting scheme with passage retrieval and pseudo-relevance feedback. The context-based model incorporates the term locations in a document for calculating the term weights. By doing this, different term weights are assigned to the same query term depending on its context and location in the document. We also use WordNet in the term selection process when doing pseudo-rele vance feedback. The performance of our model is comparable to the median among all participants in the robust track on the whole query set including the title, descriptive and long queries.
Mortgage Default and Low Downpayment Loans: The Costs of Public Subsidy

This paper presents a unified model of the default and prepayment behavior of homeowners in a proportional hazard framework. The model uses the option-based approach to analyze default and prepayment and considers these two interdependent hazards as competing risks. The results indicate the sensitivity of default to the initial loan-to-value ratio of the loan and the course of housing equity. The latter is a measure of the extent to which the default option is in the money. The results also indicate the importance of trigger events, namely unemployment and divorce, in affecting prepayment and default behavior. The empirical results are used to analyze the costs of a current policy proposal -- stimulating homeownership by offering low downpayment loans. We simulate default probabilities and costs on zero-downpayment loans and compare them to conventional loans with conventional underwriting standards. The results indicate that if zero-downpayment loans were priced as if they were mortgages with ten percent downpayments, then the additional program costs would be two to four percent of funds made available -- when housing prices increase steadily. If housing prices remained constant, the costs of the program would be much larger indeed. Our estimates suggest that additional program costs could be between $74,000 and $87,000 per million dollars of lending. If the expected losses from such a program were not priced at all, the losses from default alone could exceed ten percent of the funds made available for loans.
Robust stability of interval neural networks with multiple delays

In this paper, a new criterion is established for global robust asymptotic stability of a class of interval neural networks with multiple constant delays via the Lyapunov-Krasovskii stability theory and the linear matrix inequality (LMI) approach. A numerical example is also given to show the effectiveness of our results.
Connectedness, awareness and social presence

Introduction This paper discusses the concept of 'connectedness', exploring its relationship with the concepts of 'social presence' and 'awareness', and arguing that although 'social presence' and 'awareness' have received most focus in previous research, 'connectedness' is, in fact, a more fundamental concept. 'Connectedness' is potentially a key concept in the analysis of communication and the development of communication technology.
REGULARIZED SOLUTION TO THE FREDHOLM INTEGRAL EQUATION OF THE FIRST KIND WITH NOISY DATA y

In this paper, we use a modifled Tikhonov regularization method to solve the Fredholm integral equation of the flrst kind. Under the as- sumption that measured data are contaminated with deterministic errors, we give two error estimates. The convergence rates can be obtained under the suitable choices of regularization parameters and the number of mea- sured points. Some numerical experiments show that the proposed method is efiective and stable.
Dense surface point distribution models of the human face

In this paper we show how a dense surface model of the human face can be built from a population of examples. A technique that combines active shape models (ASMs) with iterative closest point (ICP) can be used to fit the model to new faces. The model is built by aligning the surfaces using a sparse set of hand-placed landmarks, then using thin-plate spline warping to make a dense correspondence with a base mesh. All of the mesh vertices are then used as landmarks to build a 3D point distribution model. The dense surface point distribution model is more sensitive than the landmark model to correlated facial characteristics such as gender, age and the presence of congenital abnormalities.