Miracle-dz/UniFAR-FTUs huggingface dataset:
Smart Meter Privacy: Exploiting the Potential of Household Energy Storage Units

The Internet of Things (IoT) extends network connectivity and computing capability to physical devices. However, data from IoT devices may increase the risk of privacy violations. In this paper, we consider smart meters as a prominent early instance of the IoT, and we investigate their privacy protection solutions at customer premises. In particular, we design a load hiding approach that obscures household consumption with the help of energy storage units. For this purpose, we leverage the opportunistic use of existing household energy storage units to render load hiding less costly. We propose combining the use of electric vehicles (EVs) and heating, ventilating, and air conditioning (HVAC) systems to reduce or eliminate the reliance on local rechargeable batteries for load hiding. To this end, we formulate a Markov decision process to account for the stochastic nature of customer demand and use a ${Q}$ -learning algorithm to adapt the control policies for the energy storage units. We also provide an idealized benchmark system by formulating a deterministic optimization problem and deriving its equivalent convex form. We evaluate the performance of our approach for different combinations of storage units and with different benchmark methods. Our results show that the opportunistic joint use of EV and HVAC units can reduce the need of dedicated large-capacity or fast-charging-cycle batteries for load hiding.
A practical, intuitive brain–computer interface for communicating ‘yes’ or ‘no’ by listening

Objective. Previous work has shown that it is possible to build an EEG-based binary brain–computer interface system (BCI) driven purely by shifts of attention to auditory stimuli. However, previous studies used abrupt, abstract stimuli that are often perceived as harsh and unpleasant, and whose lack of inherent meaning may make the interface unintuitive and difficult for beginners. We aimed to establish whether we could transition to a system based on more natural, intuitive stimuli (spoken words ‘yes’ and ‘no’) without loss of performance, and whether the system could be used by people in the locked-in state. Approach. We performed a counterbalanced, interleaved within-subject comparison between an auditory streaming BCI that used beep stimuli, and one that used word stimuli. Fourteen healthy volunteers performed two sessions each, on separate days. We also collected preliminary data from two subjects with advanced amyotrophic lateral sclerosis (ALS), who used the word-based system to answer a set of simple yes–no questions. Main results. The N1, N2 and P3 event-related potentials elicited by words varied more between subjects than those elicited by beeps. However, the difference between responses to attended and unattended stimuli was more consistent with words than beeps. Healthy subjects’ performance with word stimuli (mean 77% ± 3.3 s.e.) was slightly but not significantly better than their performance with beep stimuli (mean 73% ± 2.8 s.e.). The two subjects with ALS used the word-based BCI to answer questions with a level of accuracy similar to that of the healthy subjects. Significance. Since performance using word stimuli was at least as good as performance using beeps, we recommend that auditory streaming BCI systems be built with word stimuli to make the system more pleasant and intuitive. Our preliminary data show that word-based streaming BCI is a promising tool for communication by people who are locked in.
Few-Shot Text Classification with Pre-Trained Word Embeddings and a Human in the Loop

Most of the literature around text classification treats it as a supervised learning problem: given a corpus of labeled documents, train a classifier such that it can accurately predict the classes of unseen documents. In industry, however, it is not uncommon for a business to have entire corpora of documents where few or none have been classified, or where existing classifications have become meaningless. With web content, for example, poor taxonomy management can result in labels being applied indiscriminately, making filtering by these labels unhelpful. Our work aims to make it possible to classify an entire corpus of unlabeled documents using a human-in-the-loop approach, where the content owner manually classifies just one or two documents per category and the rest can be automatically classified. This "few-shot" learning approach requires rich representations of the documents such that those that have been manually labeled can be treated as prototypes, and automatic classification of the rest is a simple case of measuring the distance to prototypes. This approach uses pre-trained word embeddings, where documents are represented using a simple weighted average of constituent word embeddings. We have tested the accuracy of the approach on existing labeled datasets and provide the results here. We have also made code available for reproducing the results we got on the 20 Newsgroups dataset.
Genetic predisposition, Western dietary pattern, and the risk of type 2 diabetes in men.

BACKGROUND A widely held hypothesis is that interactions between genetic predisposition and Western-type lifestyle contribute to the epidemic of type 2 diabetes (T2D). No study has tested this hypothesis. OBJECTIVE The objective was to assess whether established genetic variants, mainly from genomewide association studies, modify dietary patterns in predicting diabetes risk. DESIGN We determined 10 polymorphisms in a prospective, nested, case-control study of 1196 diabetic and 1337 nondiabetic men. A genetic risk score (GRS) was generated by using an allele counting method. Baseline dietary intakes were collected by using a semiquantitative food-frequency questionnaire. We used factor analysis to derive Western and "Prudent" dietary patterns from 40 food groups. RESULTS A significant interaction (P = 0.02) was observed between the GRS and Western dietary pattern. The multivariable odds ratios (ORs) of T2D across increasing quartiles for the Western dietary pattern were 1.00, 1.23 (95% CI: 0.88, 1.73), 1.49 (1.06,2.09), and 2.06 (1.48, 2.88) among men with a high GRS (>/=12 risk alleles; P for trend = 0.01). The Western dietary pattern was not associated with diabetes risk among those with a lower GRS. In addition, we found that intakes of processed meat, red meat, and heme iron, which characterized the Western dietary pattern, showed significant interactions with GRS in relation to diabetes risk (P for interaction = 0.029, 0.02, and 0.0004, respectively). The diet-diabetes associations were more evident among men with a high GRS (>/=12) than in those with a low GRS. CONCLUSION Genetic predisposition may synergistically interact with a Western dietary pattern in determining diabetes risk in men.
Modelling the plastic deformation of iron

The 1/2 screw dislocations in bcc iron are studied by atomistic simulations. An analytical yield criterion captures correctly the non-Schmid plastic behavior. A model Peierls potential develops a link between the atomistic modeling at 0 K and the thermally activated dislocation motion. All predicted features agree well with experimental observations. This work establishes a consistent bottom-up model that provides an insight into the microscopic origins of the plastic behavior of bcc iron. Umfang: XII, 146 S. Preis: €40.00 | £37.00 | $70.00
Cooperative Semantic Information Processing for Literature-Based Biomedical Knowledge Discovery

I represent that my thesis or dissertation and abstract are my original work. Proper attribution has been given to all outside sources. I understand that I am solely responsible for obtaining any needed copyright permissions. I have obtained and attached hereto needed written permission statements(s) from the owner(s) of each third-party copyrighted matter to be included in my work, allowing electronic distribution (if such use is not permitted by the fair use doctrine). I hereby grant to The University of Kentucky and its agents the non-exclusive license to archive and make accessible my work in whole or in part in all forms of media, now or hereafter known. I agree that the document mentioned above may be made available immediately for worldwide access unless a preapproved embargo applies. The document mentioned above has been reviewed and accepted by the student's advisor, on behalf of the advisory committee, and by the Director of Graduate Studies (DGS), on behalf of the program; we verify that this is the final, approved version of the student's dissertation including all changes required by the advisory committee. The undersigned agree to abide by the statements above. By Given that data is increasing exponentially everyday, extracting and understanding the information, themes and relationships from large collections of documents is more and more important to researchers in many areas. In this paper, we present a cooperative semantic information processing system to help biomedical researchers understand and discover knowledge in large numbers of titles and abstracts from PubMed query results. Our system is based on a prevalent technique, topic modeling, which is an unsuper-vised machine learning approach for discovering the set of semantic themes in a large set of documents. In addition, we apply a natural language processing technique to transform the " bag-of-words " assumption of topic models to the " bag-of-important-phrases " assumption and build an interactive visualization tool using a modified, open-source, Topic Browser. In the end, we conduct two experiments to evaluate the approach. The first, evaluates whether the " bag-of-important-phrases " approach is better at identifying semantic themes than the standard " bag-of-words " approach. This is an empirical study in which human subjects evaluate the quality of the resulting topics using a standard " word intrusion test " to determine whether subjects can identify a word (or phrase) that does not belong in the topic. The second is a qualitative empirical study to …
Cell Sheet Tissue Engineering for Heart Failure 3 Hidekazu Sekine , Tatsuya Shimizu , and Teruo Okano

In recent years, regenerative medicine using cells for treating tissue defects has been in the spotlight as a new treatment for severe heart failure. Direct injection of bone marrow-derived cells and isolated skeletal myoblasts has already been used clinically as a method to improve cardiac function by regenerating cardiac muscle cells andbloodvessels. The research on reconstructing functional three-dimensional (3D) cardiac grafts using tissue engineeringmethods has also nowbeen addressed as a treatment for the next generation. Our laboratory has proposed an original tissue engineering technology called “cell sheet engineering” that stacks cell sheets to reconstruct functional 3D tissues. Transplantation of cell sheets has already shown it can cure damaged hearts, and it seems clear that the field of cell sheet tissue engineering can offer realistic treatment for patients with severe cardiac disorders.
Predicting interactions between individuals with structural and dynamical information

Capturing both the structural and temporal aspects of interactions is crucial for many real world datasets like contact between individuals. Using the link stream formalism to capture the dynamic of the systems, we tackle the issue of activity prediction in link streams, that is to say predicting the number of links occurring during a given period of time and we present a protocol that takes advantage of the temporal and structural information contained in the link stream. Using a supervised learning method, we are able to model the dynamic of our system to improve the prediction. We investigate the behavior of our algorithm and crucial elements affecting the prediction. By introducing different categories of pair of nodes, we are able to improve the quality as well as increase the diversity of our prediction.
Intraocular Vascular Endothelial Growth Factor Levels in Pachychoroid Neovasculopathy and Neovascular Age-Related Macular Degeneration.

Purpose To investigate the difference in intraocular vascular endothelial growth factor (VEGF) concentration between pachychoroid neovasculopathy and neovascular age-related macular degeneration (nAMD) and its associations with responses to three monthly anti-VEGF injections as an initial treatment for the two conditions. Methods This study included nine eyes with treatment-naïve pachychoroid neovasculopathy and 21 eyes with treatment-naïve nAMD. Before the initial intravitreal anti-VEGF injection, aqueous humor samples were collected and the concentration of VEGF was measured using enzyme-linked immunosorbent assay. The concentration was compared between the two conditions, and its associations with responses to anti-VEGF therapy were investigated. Results The mean VEGF concentration in pachychoroid neovasculopathy was significantly lower than that in nAMD (63.4 ± 17.8 pg/ml and 89.8 ± 45.0 pg/ml, respectively; P = 0.035). The VEGF concentration was associated with the presence or absence of drusen (β = 0.503, P = 0.004). After anti-VEGF therapy, 6 (66.7%) of 9 eyes with pachychoroid neovasculopathy and 17 (81.0%) of 21 eyes with nAMD achieved dry macula (P = 0.640). Dry macula at 3 months and 12 months was significantly associated with a low VEGF concentration in pachychoroid neovasculopathy (P = 0.013 and P = 0.042, respectively), but not in nAMD (P = 0.108 and P = 0.219). Conclusions The mean VEGF concentration in pachychoroid neovasculopathy was lower than that in nAMD, suggesting that the way in which VEGF is involved in angiogenesis may differ between pachychoroid neovasculopathy and nAMD.
A single-shot line-scanning spatially dispersed short coherence interferometer using Fourier transform profilometry

Single-shot inspection at nanoscale resolution is a problematic challenge for providing on-line inspection of manufacturing techniques such as roll-to-roll processes where the measurand is constantly moving. An example of such a measurement challenge is defect detection on vapor barrier films formed by depositing an aluminum oxide layer several tens of nanometres thick on a flexible polymer substrate. Effective detection and characterisation of defects in this layer requires a single-shot approach with nanometre scale vertical resolution. This paper describes a line-scanning interferometer where a short coherence light source having a 25 nm linewidth source is spatially dispersed across the measurand thus encoding spatial position along a profile by wavelength. Phase shift interferometry (PSI) can be used to decode phase and thus height information, but requires multiple image captures. In order to realise single-shot measurement which is more suitable for online applications, a Fourier transform profilometry (FTP) approach is necessary. This paper explores the implementation of the FTP approach and presents a comparison of the measurement capability of FTP with the previously reported PSI method.
JAK2/IDH-mutant–driven myeloproliferative neoplasm is sensitive to combined targeted inhibition

Patients with myeloproliferative neoplasms (MPNs) frequently progress to bone marrow failure or acute myeloid leukemia (AML), and mutations in epigenetic regulators such as the metabolic enzyme isocitrate dehydrogenase (IDH) are associated with poor outcomes. Here, we showed that combined expression of Jak2V617F and mutant IDH1R132H or Idh2R140Q induces MPN progression, alters stem/progenitor cell function, and impairs differentiation in mice. Jak2V617F Idh2R140Q–mutant MPNs were sensitive to small-molecule inhibition of IDH. Combined inhibition of JAK2 and IDH2 normalized the stem and progenitor cell compartments in the murine model and reduced disease burden to a greater extent than was seen with JAK inhibition alone. In addition, combined JAK2 and IDH2 inhibitor treatment also reversed aberrant gene expression in MPN stem cells and reversed the metabolite perturbations induced by concurrent JAK2 and IDH2 mutations. Combined JAK2 and IDH2 inhibitor therapy also showed cooperative efficacy in cells from MPN patients with both JAK2mut and IDH2mut mutations. Taken together, these data suggest that combined JAK and IDH inhibition may offer a therapeutic advantage in this high-risk MPN subtype.
Let's talk (ironically) about the weather: Modeling verbal irony

Verbal irony plays an important role in how we communicate and express opinions about the world. While there exist many theories and empirical findings about how people use and understand verbal irony, there is to our knowledge no formal model of how people incorporate shared background knowledge and linguistic information to communicate ironically. Here we argue that a computational approach previously shown to model hyperbole (Kao, Wu, Bergen, & Goodman, 2014) can also explain irony once we extend it to a richer space of affective subtext. We then describe two behavioral experiments that examine people’s interpretations of utterances in contexts that afford irony. We show that by minimally extending the hyperbole model to account for two dimensions of affect—valence and arousal—our model produces interpretations that closely match humans’. We discuss the implications of our model on informal theories of irony and its relationship to other types of nonliteral language understanding.
Effects of Dioxin Exposure on Thyroid Hormones of Populations Living in hot Spots of Dioxin Contamination in Vietnam

Dioxin is still long persistent in environment. The effect of Dioxin on thyroid function is inconsistent. Da Nang and Bien Hoa airbases have been hot spots for persistent contamination since 1961. Inhabitant living around these sites has been exposed to dioxin for a long time. Serum dioxin level of people was much higher than those in other sites. This study evaluates serum dioxin level using DR CALUX and thyroid function including T3, T4, FT3, FT4, TSH measurement. T3 level was found association with serum dioxin level. There were not any relation between T4, FT3, FT4 and TSH and dioxin level.
Global Burden of Norovirus and Prospects for Vaccine Development

Contributors and Reviewers Robert Atmar, Baylor College of Medicine Ralph Baric, University of North Carolina Mary Estes, Baylor College of Medicine Kim Green, NIH; National Institute of Allergy and Infectious Diseases Roger Glass, NIH; Fogarty International Center Aron Hall, Centers for Disease Control and Prevention Miren Iturriza-Gómara, University of Liverpool Cherry Kang, Christian Medical College Bruce Lee, Johns Hopkins University Umesh Parashar, Centers for Disease Control and Prevention Mark Riddle, Naval Medical Research Center Jan Vinjé, Centers for Disease Control and Prevention
1-1-2011 Information Surfing for Radiation Building

We develop a control scheme for a group of mobile sensors to map radiation over a given planar polygonal region. The advantage of this methodology is that it provides quick situational awareness regarding radiation levels, which is being updated and refined in real-time as more measurements become available. The control algorithm is based on the concept of information surfing, where navigation is done by following information gradients, taking into account sensing performance and the dynamics of the observed process. It is tailored to cases of weak radioactivity, where source signals may be buried in background. We steer mobile sensors to locations which are critical points of a function that quantifies the information content of the measured signal, while ∗Corresponding author the time-asymptotic properties of the selected information metric facilitate the stability of the group motion. Information surfing allows for reactive mobile sensor network behavior and adaptation to environmental changes, as well as human retasking. Computer simulations and experiments are conducted to verify the asymptotic behavior of the robot group, and its distributed sensing and mapping capabilities.
Detection of the Operating System Configuration Vulnerabilities with Safety Evaluation Facility

In this paper, we address to formal verification methodologies and the system analyzing facility to verify property of the operating systems safety. Using our technique it becomes possible to discover security drawbacks in any IT-system based on access control model of 'state machine' style. Through our case study of model checking in Sample Vulnerability Checking (SVC), we show how the evaluation tool can be applied in Microsoft Windows 2000 to specify and verify safety problem of system security.
A brain–computer interface using electrocorticographic signals in humans

Brain–computer interfaces (BCIs) enable users to control devices with electroencephalographic (EEG) activity from the scalp or with single-neuron activity from within the brain. Both methods have disadvantages: EEG has limited resolution and requires extensive training, while single-neuron recording entails significant clinical risks and has limited stability. We demonstrate here for the first time that electrocorticographic (ECoG) activity recorded from the surface of the brain can enable users to control a one-dimensional computer cursor rapidly and accurately. We first identified ECoG signals that were associated with different types of motor and speech imagery. Over brief training periods of 3–24 min, four patients then used these signals to master closed-loop control and to achieve success rates of 74–100% in a one-dimensional binary task. In additional open-loop experiments, we found that ECoG signals at frequencies up to 180 Hz encoded substantial information about the direction of two-dimensional joystick movements. Our results suggest that an ECoG-based BCI could provide for people with severe motor disabilities a non-muscular communication and control option that is more powerful than EEG-based BCIs and is potentially more stable and less traumatic than BCIs that use electrodes penetrating the brain.