Miracle-dz/UniFAR-FTUs huggingface dataset:
Sextant: a unified node and event localization framework using non-convex constraints

Determining node and event locations is a canonical task for many wireless network applications. Yet dedicated infrastructure for determining position information is expensive, energy-consuming, and simply unavailable in many deployment scenarios. This paper presents an accurate, cheap and scalable framework, called Sextant, for determining node position and event location in sensor networks. Sextant operates by setting up and solving a system of geographic constraints based on connectivity information from the underlying communication network. Sextant achieves high accuracy by enabling non-convex constraints to be used to refine position estimates. It represents position estimates as potentially non-contiguous collections of points. This general representation enables Sextant to use _negative information_, that is, information on where a node or event is not located, to refine location estimates. Sextant unifies both node and event detection within the same general framework. It can provide high precision without dedicated localization hardware by aggressively extracting constraints from the link layer, representing areas precisely with Bézier-enclosed polygons and probability distributions, and using event detection to refine node position estimates. A compact representation and a fully distributed implementation make the framework practical for resource-limited devices. The framework has been implemented, deployed and tested on laptops, PDAs and Mica-2 motes. Physical experiments show that a large number (98%) of the nodes in a network can determine their positions based on a small number (30%) of landmark nodes and that a large number (90%) of events can be located with low median error.
Nanoparticle/Biopolymer Systems for Medical Applications

In this thesis, commercially available basal lamina hydrogels are analyzed to study the effect of the biochemical composition of these gels on their biophysical properties. In addition, a thermoresponsive methylcellulose/mucin hybrid wound gel is developed where the methylcellulose compensates for the mechanical limitation of the anti-microbial mucins. Finally, to achieve orchestrated drug release from a hydrogel two molecular mechanisms are combined: i.e. a build-up of osmotic pressure by the depletion of a control molecule and triggered disaggregation of nanoparticle clusters by synthetic DNA sequences.
Investigation of vitamin D receptor polymorphisms in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) patients manifest aberrations in the vitamin D endocrine system, with a vitamin D deficiency. Genetic investigations have identified those proteins which link vitamin D to ALS pathology: major histocompatibility complex class II molecules, toll‐like receptors, poly(ADP ribose) polymerase‐1, haeme oxygenase‐1, the reduced form of nicotinamide adenine dinucleotide phosphate and calcium‐binding proteins. Vitamin D additionally impacts ALS through cell‐signalling mechanisms: glutamate, matrix metalloproteinases, the Wnt/β‐catenin signalling pathway, mitogen‐activated protein kinase pathways, prostaglandins, reactive oxygen species and nitric oxide synthase, but its role has been only poorly investigated.
Isolation of membrane vesicles from prokaryotes: a technical and biological comparison reveals heterogeneity

ABSTRACT Prokaryotes release membrane vesicles (MVs) with direct roles in disease pathogenesis. MVs are heterogeneous when isolated from bacterial cultures so Density Gradient Centrifugation (DGC) is valuable for separation of MV subgroups from contaminating material. Here we report the technical variability and natural biological heterogeneity seen between DGC preparations of MVs for Mycobacterium smegmatis and Escherichia coli and compare these DGC data with size exclusion chromatography (SEC) columns. Crude preparations of MVs, isolated from cultures by ultrafiltration and ultracentrifugation were separated by DGC with fractions manually collected as guided by visible bands. Yields of protein, RNA and endotoxin, protein banding and particle counts were analysed in these. DGC and SEC methods enabled separation of molecularly distinct MV populations from crude MVs. DGC banding profiles were unique for each of the two species of bacteria tested and further altered by changing culture conditions, for example with iron supplementation. SEC is time efficient, reproducible and cost effective method that may also allow partial LPS removal from Gram-negative bacterial MVs. In summary, both DGC and SEC are suitable for the separation of mixed populations of MVs and we advise trials of both, coupled with complete molecular and single vesicle characterisation prior to downstream experimentation.
Effect of Mn on corrosion and thermal behaviour of AlCr1.5CuFeNi2Mnx high-entropy alloys

There are numerous multicomponent alloys reported in the past resulting in a single phase with promising mechanical properties at high temperature. However, investigation on the corrosion properties and thermal conductivity of the novel high-entropy alloy (HEAs) are yet to be explored. The present study focuses on the effect of Mn on the corrosion and thermal behaviour of AlCr1.5CuFeNi2Mnx (x = 0, 0.25, 0.5, 0.75,1 in molar ratio) HEAs. The HEAs reported so far are mostly fabricated by arc melting process whereas, in this study, AlCr1.5CuFeNi2Mnx HEAs ingots were cast by high temperature vacuum induction furnace under inert gas environment. The electrochemical-corrosion behaviour of the as-cast alloys was studied using a potentiodynamic-polarization method immersed in 3.5 wt. %NaCl solution (open to air) at room temperature. It was observed from the polarization curve that the corrosion resistance was found to increase on the addition of higher amount of Mn in base alloy. The thermal conductivity of the HEAs was determined at 300 K, 350 K, 400 K, 450 K and 500 K. It was found that the thermal conductivity of the alloys decreases with the addition of the Mn content whereas the thermal conductivity is found to increase with the increase in the temperature of the alloys.
Sequence Prediction with Unlabeled Data by Reward Function Learning

Reinforcement learning (RL), which has been successfully applied to sequence prediction, introduces reward as sequence-level supervision signal to evaluate the quality of a generated sequence. Existing RL approaches use the ground-truth sequence to define reward, which limits the application of RL techniques to labeled data. Since labeled data is usually scarce and/or costly to collect, it is desirable to leverage large-scale unlabeled data. In this paper, we extend existing RL methods for sequence prediction to exploit unlabeled data. We propose to learn the reward function from labeled data and use the predicted reward as pseudo reward for unlabeled data so that we can learn from unlabeled data using the pseudo reward. To get good pseudo reward on unlabeled data, we propose a RNN-based reward network with attention mechanism, trained with purposely biased data distribution. Experiments show that the pseudo reward can provide good supervision and guide the learning process on unlabeled data. We observe significant improvements on both neural machine translation and text summarization.
Parallel mining of dependencies

The problem of extracting functional dependencies (FDs) from databases has a long story dating back to the 90's. Still, efficient solutions taking into account both material evolution, namely the advent of multicore machines, and the amount of data that are to be mined, are still needed. In this paper we propose a parallel algorithm which, upon small modifications, extracts (i) the minimal keys, (ii) the minimal exact FDs, (iii) the minimal approximate FDs and (iv) the Conditional functional dependencies (CFDs) holding in a table. Under some natural conditions, we prove a theoretical speed up of our solution with respect to a baseline algorithm which follows a depth first search strategy. Since mining most of these dependencies require a procedure for computing the number of distinct values (NDV) which is a space consuming operation, we show how sketching techniques for estimating the exact value of NDV can be used for reducing both memory consumption as well as communications overhead when considering distributed data while guaranteeing a certain quality of the result. Our solution is implemented and some experimental results are reported here showing the efficiency and scalability of our proposal. Most notably, the theoretical speed ups are confirmed by the experiments.
Accretion Disks and the Lyman Continuum Polarization of QSOs

Hubble Space Telescope observations of some QSOs show a strong, abrupt increase in polarization at rest wavelength about 750 Å. The closeness of the polarization rise to the H I Lyman edge suggests a connection, but the displacement to shorter wavelengths and the shape of the polarization rise require explanation. We have computed the polarized spectrum of a thermally emitting accretion disk around a supermassive black hole, including the effects of the relativistic transfer function. The local stellar atmosphere spectra show a blueshifted polarization rise in the Lyman continuum, as found by Blaes & Agol in 1996. However, the relativistic transfer function adds an additional blueshift of sufficient magnitude that the model cannot explain the observations. We show that a good fit results if the emitted radiation is assumed to have a sharp increase in polarized flux at the Lyman edge in the rest frame of the orbiting gas. Relativistic effects then cause the observed polarization to rise sharply at a wavelength substantially less than 912 Å. The blueshift depends on the angular momentum of the black hole and the inclination of the disk. A good fit to PG 1630+377 results from a simple model with a dimensionless angular momentum a* ≡ cJ/M2 = 0.5, and an observer viewing angle μo ≡ cos θo = 0.1. Smaller values of a* give insufficient blueshifts, and values close to a* = 0.9982 require unrealistically large polarizations in the rest frame of the gas. An intermediate value of a* might result from coalescence of black holes, successive accretion events, or electromagnetic extraction of angular momentum from the hole.
Distrust, race, and research.

BACKGROUND Investigators have voiced concerns that distrust of research and the medical community impedes successful recruitment of African Americans into clinical research. OBJECTIVES To examine possible differences in distrust by race and to determine to what extent other sociodemographic factors explain any racial differences in distrust. METHODS We analyzed data from 527 African American and 382 white respondents of a national telephone survey on participation in clinical research. Our main outcome measure was a 7-item index of distrust. RESULTS African American respondents were more likely than white respondents not to trust that their physicians would fully explain research participation (41.7% vs 23.4%, P<.01) and to state that they believed their physicians exposed them to unnecessary risks (45.5% vs 34.8%, P<.01). African American respondents had a significantly higher mean distrust index score than white respondents (3.1 vs 1.8, P<.01). After controlling for other sociodemographic variables in a logistic regression model, race remained strongly associated with a higher distrust score (prevalence odds ratio, 4.7; 95% confidence interval, 2.9-7.7). CONCLUSIONS Even after controlling for markers of social class, African Americans were less trusting than white Americans. Racial differences in distrust have important implications for investigators as they engage African Americans in research.
CDAS: A Crowdsourcing Data Analytics System

Some complex problems, such as image tagging and natural language processing, are very challenging for computers, where even state-of-the-art technology is yet able to provide satisfactory accuracy. Therefore, rather than relying solely on developing new and better algorithms to handle such tasks, we look to the crowdsourcing solution -- employing human participation -- to make good the shortfall in current technology. Crowdsourcing is a good supplement to many computer tasks. A complex job may be divided into computer-oriented tasks and human-oriented tasks, which are then assigned to machines and humans respectively. To leverage the power of crowdsourcing, we design and implement a Crowdsourcing Data Analytics System, CDAS. CDAS is a framework designed to support the deployment of various crowdsourcing applications. The core part of CDAS is a quality-sensitive answering model, which guides the crowdsourcing engine to process and monitor the human tasks. In this paper, we introduce the principles of our quality-sensitive model. To satisfy user required accuracy, the model guides the crowdsourcing query engine for the design and processing of the corresponding crowdsourcing jobs. It provides an estimated accuracy for each generated result based on the human workers' historical performances. When verifying the quality of the result, the model employs an online strategy to reduce waiting time. To show the effectiveness of the model, we implement and deploy two analytics jobs on CDAS, a twitter sentiment analytics job and an image tagging job. We use real Twitter and Flickr data as our queries respectively. We compare our approaches with state-of-the-art classification and image annotation techniques. The results show that the human-assisted methods can indeed achieve a much higher accuracy. By embedding the quality-sensitive model into crowdsourcing query engine, we effectively reduce the processing cost while maintaining the required query answer quality.
Using Alert Verification to Identify Successful Intrusion Attempts

ABSTRACT Intrusion detection systems monitor protected networks and attempt to identify evidence of malicious activity. When an attack is detected, an alert is produced, and, possibly, a countermeasure is executed. A perfect intrusion detection system would be able to identify all the attacks without raising any false alarms. In addition, a countermeasure would be executed only when an attack is actually successful. Unfortunately false alarms are commonplace in intrusion detection systems, and perfectly benign events are interpreted as malicious. In addition, non-relevant alerts are also common. These are alerts associated with attacks that were not successful. Such alerts should be tagged appropriately so that their priority can be lowered. The process of identifying alerts associated with successful attacks is called alert verification. This paper describes the different issues involved in alert verification and presents a tool that performs real-time verification of attacks detected by an intrusion detection system. The experimental evaluation of the tool shows that verification can dramatically reduce both false and non-relevant alerts.
Toward Reliable Estimates of Abundance: Comparing Index Methods to Assess the Abundance of a Mammalian Predator

Due to time and financial constraints indices are often used to obtain landscape-scale estimates of relative species abundance. Using two different field methods and comparing the results can help to detect possible bias or a non monotonic relationship between the index and the true abundance, providing more reliable results. We used data obtained from camera traps and feces counts to independently estimate relative abundance of red foxes in the Black Forest, a forested landscape in southern Germany. Applying negative binomial regression models, we identified landscape parameters that influence red fox abundance, which we then used to predict relative red fox abundance. We compared the estimated regression coefficients of the landscape parameters and the predicted abundance of the two methods. Further, we compared the costs and the precision of the two field methods. The predicted relative abundances were similar between the two methods, suggesting that the two indices were closely related to the true abundance of red foxes. For both methods, landscape diversity and edge density best described differences in the indices and had positive estimated effects on the relative fox abundance. In our study the costs of each method were of similar magnitude, but the sample size obtained from the feces counts (262 transects) was larger than the camera trap sample size (88 camera locations). The precision of the camera traps was lower than the precision of the feces counts. The approach we applied can be used as a framework to compare and combine the results of two or more different field methods to estimate abundance and by this enhance the reliability of the result.
Micro-inverter based on quasi-Z-source inverter integrating switchless voltage equalizer for photovoltaic panels under partial shading

Conventional photovoltaic (PV) systems using a micro-inverter tend to be complex and costly as three separate converters (i.e., a boost converter, inverter, and voltage equalizer to prevent partial shading issues) are necessary. This paper proposes a novel micro-inverter integrating a voltage equalizer to achieve not only system-level but also circuit-level simplifications. The voltage can be boosted by the capacitor-assisted extended-boost quasi-Z-source inverter (CAEB q-ZSI), while the partial shading issues can be precluded by the switchless voltage equalizer based on the voltage multiplier that is driven by a square wave voltage generated in the q-ZSI. The derivation procedure and fundamental operation analysis are explained and discussed. The prototype for a standard 72-cell PV panel consisting of three substrings connected in series was built, and the equalization test was performed emulating a partial shading condition. The extractable maximum power from the PV panel was significantly increased by the prototype, demonstrating the efficacy of the proposed integrated micro-inverter.
The Role of Dioxin Receptor in Mammary Development and Carcinogenesis

Abstract : The aromatic hydrocarbon (or dioxin) receptor (AhR) which is a ligand-activated basic helix- loop-helix (bHLH) transcription factor, mediates the toxic responses of dioxin. The research in this proposal is testing the hypothesis that the dioxin receptor (AhR) plays a central role in regulating the mammary epithelial network during the gland development, as well as regulating major events in mammary carcinogenesis. Following on preliminary observation of the dramatic upregulation of this receptor in advanced human breast carcinoma (HBc) cell lines, we proposed to address the question of whether the AhR overexpression alone is sufficient for transforming normal mammary epithelia, and whether it is causally associated transformation, using two genetic approaches. The AhR expression will be blocked in high tumorigenic HBC cell lines by siRNA technology to demonstrate a direct role of the AhR in modifying the progression of metastasis. To directly address the effect of increased expression of AhR, the human AhR cDNA will be stably transfected and over-expressed in a normal mammary epithelia and in non-tumorigenic human breast cell line. The development of metastatic phenotypes in the AhR-transformed lines will be assayed as their ability for anchorage-independent growth in soft agar media and for inducing tumors in nude mouse.
A Tree-to-String Phrase-based Model for Statistical Machine Translation

Though phrase-based SMT has achieved high translation quality, it still lacks of generalization ability to capture word order differences between languages. In this paper we describe a general method for tree-to-string phrase-based SMT. We study how syntactic transformation is incorporated into phrase-based SMT and its effectiveness. We design syntactic transformation models using unlexicalized form of synchronous context-free grammars. These models can be learned from source-parsed bitext. Our system can naturally make use of both constituent and non-constituent phrasal translations in the decoding phase. We considered various levels of syntactic analysis ranging from chunking to full parsing. Our experimental results of English-Japanese and English-Vietnamese translation showed a significant improvement over two baseline phrase-based SMT systems.
Efficacy of quetiapine in patients with bipolar I and II depression: a multicenter, prospective, open-label, observational study

Purpose To evaluate and compare the therapeutic efficacy of quetiapine in bipolar I and II depression patients in the clinical setting. Patients and methods This was an 8-week, multicenter, open-label, observational study for bipolar depression. The dosage of quetiapine was flexible, and concomitant medications were permitted on clinician’s judgments. A total of 1097 patients were enrolled, and 764 bipolar depression patients who exhibited good therapeutic compliance (>75% compliance rate) were analyzed. Results Clinical Global Impression – Bipolar scale and Montgomery–Asberg Depression Rating Scale scores were significantly improved at weeks four and eight compared with the baseline scores. At the end of the 8-week study, the response rate was 58.9%, and the remission rate was 42.1%. However, there were no significant differences in the response and remission rates between bipolar I and II disorder (BD-I and BD-II) patients (response rate 60.1% versus 56.3%; remission rate 44.5% versus 37.0%). Montgomery–Asberg Depression Rating Scale score at baseline (β = 0.612, P < 0.001), duration of current episode (β = −0.152, P = 0.001), and presence of remission on previous episode (β = 0.111, P = 0.012) were significantly associated with improvements in depressive symptoms. Fatigue (16.0%), somnolence (14.9%), and manic/hypomanic switching (0.6% at week four, 0.3% at week eight) were observed throughout the study period. Conclusion The results of this study suggest that quetiapine improves depressive symptoms in BD-I and BD-II patients with a minimal incidence of manic switching. The therapeutic efficacy of quetiapine increased with time. Quetiapine could be an effective and safe modality for the treatment of BD-I and BD-II.
A unique polar representation of the hyperanalytic signal

The hyperanalytic signal is the straight forward generalization of the classical analytic signal. It is defined by a complexification of two canonical complex signals, which can be considered as an inverse operation of the Cayley-Dickson form of the quaternion. Inspired by the polar form of an analytic signal where the real instantaneous envelope and phase can be determined, this paper presents a novel method to generate a polar representation of the hyperanalytic signal, in which the continuously complex envelope and phase can be uniquely defined. Comparing to other existing methods, the proposed polar representation does not have sign ambiguity between the envelope and the phase, which makes the definition of the instantaneous complex frequency possible.