Miracle-dz/UniFAR-FTUs huggingface dataset:
Targeted Deficiency of the Transcriptional Activator Hnf1α Alters Subnuclear Positioning of Its Genomic Targets

DNA binding transcriptional activators play a central role in gene-selective regulation. In part, this is mediated by targeting local covalent modifications of histone tails. Transcriptional regulation has also been associated with the positioning of genes within the nucleus. We have now examined the role of a transcriptional activator in regulating the positioning of target genes. This was carried out with primary β-cells and hepatocytes freshly isolated from mice lacking Hnf1α, an activator encoded by the most frequently mutated gene in human monogenic diabetes (MODY3). We show that in Hnf1a−/− cells inactive endogenous Hnf1α-target genes exhibit increased trimethylated histone H3-Lys27 and reduced methylated H3-Lys4. Inactive Hnf1α-targets in Hnf1a−/− cells are also preferentially located in peripheral subnuclear domains enriched in trimethylated H3-Lys27, whereas active targets in wild-type cells are positioned in more central domains enriched in methylated H3-Lys4 and RNA polymerase II. We demonstrate that this differential positioning involves the decondensation of target chromatin, and show that it is spatially restricted rather than a reflection of non-specific changes in the nuclear organization of Hnf1a-deficient cells. This study, therefore, provides genetic evidence that a single transcriptional activator can influence the subnuclear location of its endogenous genomic targets in primary cells, and links activator-dependent changes in local chromatin structure to the spatial organization of the genome. We have also revealed a defect in subnuclear gene positioning in a model of a human transcription factor disease.
Color difference between real faces and facial images from a sRGB monitor

As face recognition technology is conveniently accessible with only the use of a camera, it is a widely human-robot-interaction. In face recognition, skin color, as a cue, separates the targets from background depends on the color dissimilarity between targets and background. In this paper, we measure color difference between real faces and facial images from a sRGB monitor and verify similarity.
Modal logics for mereotopological relations

We present a complete axiomatization of a logic denoted by MTML (Mereo-Topological Modal Logic) based on the followin g set of mereotopological relations: part-of, overlap, underlap, contact, dual con- tact and interior part-of. We prove completeness theorems for MTML with respect to several classes of models including the standard topological mod- els over the set of regular-closed subsets of arbitrary topo logical spaces. We show that MTML possesses fmp with respect to a class of non-st andard models, which implies its decidability. In this way we propose also a solu- tion of the main open problem, formulated in (17) to find a decidable modal logic for topological relations.
On pairing constrained wireless devices based on secrecy of auxiliary channels: the case of acoustic eavesdropping

Secure "pairing" of wireless devices based on auxiliary or out-of-band (OOB) - audio, visual or tactile - communication is a well-established research direction. Lack of good quality interfaces on or physical access to certain constrained devices (e.g., headsets, access points, medical implants) makes pairing a challenging problem in practice. Prior work shows that pairing of constrained devices based on authenticated OOB (A-OOB) channels can be prone to human errors that eventually translate into man-in-the-middle attacks. An alternative and more usable solution is to use OOB channel(s) that are authenticated as well as secret (AS-OOB). AS-OOB pairing can be achieved by simply transmitting the key or a short password over the AS-OOB channel, avoiding potential serious human errors. A higher level goal of this paper is to analyze the security of AS-OOB pairing. More specifically, we take a closer look at three notable prior AS-OOB pairing proposals and challenge the direct or indirect assumption upon which the security of these proposals relies, i.e., the secrecy of underlying or associated audio channels. The first proposal (IMD Pairing [9]) uses a low frequency audio channel to pair an implanted RFID tag with an external reader. The second proposal (PIN-Vibra [20]) uses an automated vibrational channel to pair a mobile phone with a personal RFID tag. The third proposal (BEDA [22]) uses vibration (or blinking) on one device and manually synchronized button pressing on the other device. In particular, we demonstrate the feasibility of eavesdropping over acoustic emanations associated with these methods. Based on our results, we conclude that these methods provide a weaker level of security compared to what was originally assumed or is desired for the pairing operation.
Aerosol Properties from Surface Radiation Observations: Anomalous Clear-Sky Absorption May be Due to Small, Highly Absorbing Aerosols

A growing body of evidence indicates that standard models overestimate the diffuse shortwave radiation in clear-sky conditions. The magnitude of the difference (9% to 40% of the diffuse observations, depending on aerosol loading) suggests that the discrepancy is not primarily due to measurement uncertainties. It has been proposed (Kato et al. 1997) that the excess absorption is due to an unidentified gas. But another possibility is the presence of highly absorptive aerosols. In this paper the second possibility is investigated. We base our comparisons on observations of direct and diffuse shortwave radiation obtained at the Southern Great Plain (SGP) site of the Atmospheric Radiation Measurement (ARM). These data are compared to modeling results for a range of aerosol parameters.
Antenna design for UWB radar detection application

This master thesis has focused on the aspect of UWB antenna design for the impulse based UWB radar which is built on CMOS technology. The design of new UWB antenna faces some challenges. The antenna should be compact, preferably conformal, low cost for manufacture, and have good electrical performance, such as good matching, directional radiation performance over a wide band, good time response, etc. Keeping these goals in mind a compact and directional antipodal Vivaldi antenna was built. The antenna radiating structure is in the form of two exponentially tapered arms that lie on the opposite sides of the substrate. The antenna operates over the frequency band from 2.7 GHz to more than 12 GHz. It exhibits a directive radiation performance with a peak gain which is between 3.2dBi and 10.2dBi in the specified band. The time domain performance of the antenna shows negligible distortion of the pulse which makes it suitable to use with impulse based UWB radar. The antenna has linear phase response over the entire UWB frequency range and hence constant group delay which is vital for transmission and reception of sub-nanosecond pulses. Due to its planar profile, physically compact size, wide impedance bandwidth, directive performance over a wide bandwidth antipodal Vivaldi antenna is a good candidate for use with Novelda UWB radar chip and other UWB integrated circuits and portable electronics. The antenna is being used for ranging, one and two dimensional tracking along with Novelda R2A UWB radar chip.
An unsupervised data-driven method to discover equivalent relations in large Linked Datasets

This article addresses a number of limitations of state-of-the-art methods of Ontology Alignment: 1) they primarily address concepts and entities while relations are less well-studied; 2) many build on the assumption of the 'well-formedness' of ontologies which is unnecessarily true in the domain of Linked Open Data; 3) few have looked at schema heterogeneity from a single source, which is also a common issue particularly in very large Linked Dataset created automatically from heterogeneous resources, or integrated from multiple datasets. We propose a domain- and language-independent and completely unsupervised method to align equivalent relations across schemata based on their shared instances. We introduce a novel similarity measure able to cope with unbalanced population of schema elements, an unsupervised technique to automatically decide similarity threshold to assert equivalence for a pair of relations, and an unsupervised clustering process to discover groups of equivalent relations across different schemata. Although the method is designed for aligning relations within a single dataset, it can also be adapted for cross-dataset alignment where sameAs links between datasets have been established. Using three gold standards created based on DBpedia, we obtain encouraging results from a thorough evaluation involving four baseline similarity measures and over 15 comparative models based on variants of the proposed method. The proposed method makes significant improvement over baseline models in terms of F1 measure (mostly between 7% and 40%), and it always scores the highest precision and is also among the top performers in terms of recall. We also make public the datasets used in this work, which we believe make the largest collection of gold standards for evaluating relation alignment in the LOD context.
Using Advice to In uence Problem-Solving

Di culties in developing fully automated AI problem-solving systems have prompted interest in interaction methodologies that support increased user involvement in the solutiongeneration process. This paper considers the merits of interactive problem solvers grounded in the metaphor of advice-taking. An advicetaking system allows users to specify high-level characteristics at runtime of both the desired solution and the problem-solving process to be employed for the task at hand, with the underlying problem-solver applying those directives to guide solution construction. Advisability enables users to in uence the behavior and output of problemsolving systems by making recommendations in terms that are natural and meaningful for them. This paper presents a model of advice-taking and characterizes applications for which advisability is best suited. In addition, it describes an ongoing e ort to develop an advisable planner that marries an advice-taking interface to state-of-theart planning technology.
Shaping Behavior With Adaptive Morphology

Robots that exploit their embodiment promise to be more robust, energy-efficient, and adaptable. However, the majority of systems designed in this way are only capable of exploiting their embodiment when performing a single task in a single environment. For such robots to be capable of adapting to a range of tasks or environments, they must be capable of adjusting their morphology online and have an understanding of how adjustments in both control and morphology affect their behavior. We introduce the concept of the control-morphology (CM) space and the variable stiffness swimmer, a multisegment pendular robot with adaptive joint stiffness. This system allows us to perform an initial investigation into how navigating the CM space affects the behavior of a robot. We show that the behavior of the system is not only the result of selecting a particular location within the CM space, but also the route taken to arrive at that point. We also demonstrate locations within the space where a shift in behavior can be caused entirely by smooth online changes in morphology.
Development of an advanced, continuous mild gasification process for the production of co-products

The objective of this project is to develop a continuous mild gasification process to convert highly caking coals to coal liquids, char and coke for near term commercial application. Task 3, Bench-Scale Char Upgrading Study, has been underway since September 1989. In char upgrading studies, ``green`` uncured char briquettes have been prepared and calcined in 20-pound batches to evaluate the effects of char, binders, and heating conditions on final coke properties. A total of 150. formulations have been tested thus far in this work. Work on Task 4, Process Development Unit (PDU) Mild Gasification Study, has been in progress since February 1991, with the completion of a Continuous Mild Gasification Unit (CMGU) with a design rate of 1000 lb. /hr. Since start-up of the CMGU, there have been 72 runs with a variety of operating conditions and coal types.
Development of a service management scheme for semiconductor factory management systems

There are hundreds of equipment in a semiconductor factory. In order to increase equipment utilization and improve product quality yield, more reliable services provided by equipment managers are expected. Therefore, it is essential to develop a reliable service management scheme (SMS) for semiconductor factory management systems. In this work, the SMS is developed by applying the Jini and contract technologies. A generic evaluator (GE) is also developed in the SMS to possess error-detection and function-replacement. The GE can also maintain the credit values of all the equipment managers. With the SMS, the process status and parameters of equipment managers in service are stored periodically. If an equipment manager malfunctions, the GE detects and confirms it and sends an event notice to the factory manager. Then, the factory manager finds a spare equipment manager and transfers the backup process status and parameters to the spare equipment manager. Finally, the spare equipment manager can continue monitor the manufacturing process starting from the point where the malfunctioned one left off. Evaluation of reliability improvement and an illustrative example of the SMS are also presented.
RNA polymerase III-specific general transcription factor IIIC contains a heterodimer resembling TFIIF Rap30/Rap74

Transcription of tRNA-encoding genes by RNA polymerase (Pol) III requires the six-subunit general transcription factor IIIC that uses subcomplexes τA and τB to recognize two gene-internal promoter elements named A- and B-box. The Schizosaccharomyces pombe τA subcomplex comprises subunits Sfc1, Sfc4 and Sfc7. The crystal structure of the Sfc1/Sfc7 heterodimer reveals similar domains and overall domain architecture to the Pol II-specific general transcription factor TFIIF Rap30/Rap74. The N-terminal Sfc1/Sfc7 dimerization module consists of a triple β-barrel similar to the N-terminal TFIIF Rap30/Rap74 dimerization module, whereas the C-terminal Sfc1 DNA-binding domain contains a winged-helix domain most similar to the TFIIF Rap30 C-terminal winged-helix domain. Sfc1 DNA-binding domain recognizes single and double-stranded DNA by an unknown mechanism. Several features observed for A-box recognition by τA resemble the recognition of promoters by bacterial RNA polymerase, where σ factor unfolds double-stranded DNA and stabilizes the non-coding DNA strand in an open conformation. Such a function has also been proposed for TFIIF, suggesting that the observed structural similarity between Sfc1/Sfc7 and TFIIF Rap30/Rap74 might also reflect similar functions.
Pre-trained Deep Neural Network using Sparse Autoencoders and Scattering Wavelet Transform for Musical Genre Recognition

Research described in this paper tries to combine the approach of Deep Neural Networks (DNN) with the novel audio features extracted using the Scattering Wavelet Transform (SWT) for classifying musical genres. The SWT uses a sequence of Wavelet Transforms to compute the modulation spectrum coefficients of multiple orders, which has already shown to be promising for this task. The DNN in this work uses pre-trained layers using Sparse Autoencoders (SAE). Data obtained from the Creative Commons website jamendo.com is used to boost the well-known GTZAN database, which is a standard benchmark for this task. The final classifier is tested using a 10-fold cross validation to achieve results similar to other state-of-the-art approaches.
A Hybrid Gene Selection Technique Using Improved Mutual Information and Fisher Score for Cancer Classification Using Microarrays

Feature Selection is significant in order to perform constructive classification in the area of cancer diagnosis. However, a large number of features compared to the number of samples makes the task of classification computationally very hard and prone to errors in microarray gene expression datasets. In this paper, we present an innovative method for selecting highly informative gene subsets of gene expression data that effectively classifies the cancer data into tumorous and non-tumorous. The hybrid gene selection technique comprises of combined Mutual Information and Fisher score to select informative genes. The gene selection is validated by classification using Support Vector Machine (SVM) which is a supervised learning algorithm capable of solving complex classification problems. The results obtained from improved Mutual Information and F-Score with SVM as a classifier has produced efficient results. Keywords—Gene selection, mutual information, Fisher score, classification, SVM.
Classifying smartphone screen ON/OFF state based on wifi probe patterns

WiFi enabled smartphones regularly send probe request messages to actively discover nearby access points. Previously, these probes have been exploited to understand users' mobility patterns and were also identified to be a privacy threat. To highlight the increased threats to user's privacy, we aim to identify the user behavior by characterizing the changes in probe patterns, which occur as an effect of user's smartphone usage. In this demo, we developed a browser based interactive front-end client, with a Node JS backend server, to interact with the sniffing hardware and to display realtime packets visualization with smartphone screen state predictions. We were able to detect the screen ON and OFF states by analyzing the probe patterns using a Decision-Tree classifier with accuracy ranging from 93% to 100% on specific smartphone models.
Strategies for Mixed-Initiative Conversation Management using Question-Answer Pairs

One of the biggest bottlenecks for conversational systems is large-scale provision of suitable content. In this paper, we present the use of content mined from online question-and-answer forums to automatically construct system utterances. Although this content is mined in the form of question-answer pairs, our system is able to use it to formulate utterances that drive a conversation, not just for answering user questions as has been done in previous work. We use a collection of strategies that specify how and when the question-answer pairs can be used and augmented with a small number of generic hand-crafted text snippets to generate natural and coherent system utterances. Our experiments involving 11 human participants demonstrated that this approach can indeed produce relatively natural and coherent interaction.
Clinical Epidemiology Dovepress Survival by Histologic Subtype in Stage Iv Nonsmall Cell Lung Cancer Based on Data from the Surveillance, Epidemiology and End Results Program

Background: The role of histology in the targeted management of nonsmall cell lung cancer (NSCLC) has garnered renewed attention in recent years. We provide contemporary population-based estimates of survival and an assessment of important prognostic factors in stage IV NSCLC by major histologic subtype. we stratified 51,749 incident stage IV NSCLC patients (1988–2003 with follow-up through 2006) by major histologic subtype. We used Kaplan–Meier and Cox proportional hazards methods to describe overall survival and the prognostic influence of select patient, tumor, and treatment characteristics for each histologic subgroup. Results: Survival was highest in patients with bronchioloalveolar adenocarcinoma (1-year survival: 29.1%) and lowest in those with large cell tumors (1-year survival: 12.8%). Diagnosis in later years, female gender, younger age, either Asian/Pacific Islander or Hispanic race/ethnicity, lower tumor grade, and surgery or beam radiation as part of first-line treatment were generally independently associated with a decreased risk of death, but the prognostic significance of some of these factors (age, ethnicity, tumor grade) varied according to histologic subtype. Conclusion: Findings demonstrate a poor prognosis across histologic subtypes in stage IV NSCLC patients but highlight differences in both absolute survival and the relative importance of select prognostic factors by histologic subclassification. More research using other sources of population-based data could help clarify the role of histology in the presentation, management, and prognosis of late-stage NSCLC.
Solving a Two-Dimensional Elliptic Model Problem with the Conjugate Gradient Method Using Matrix-Free SSOR Preconditioning in Matlab

The existing Preconditioned Conjugate Gradient method in Matlab can be optimized in terms of wall clock time and, more importantly, required storage space. The developed optimized algorithm was tested repeatedly on a two dimensional Poisson problem to ensure that it produced the same numerical solution as the original Matlab function. The algorithm was optimized in several stages: first a function was created that reused some vectors in the algorithm, next we developed a matrix-free method of computing matrix-vector multiplications with the system matrix, then a matrix-free method that implements preconditioning on the system matrix was derived, and finally all memory-saving techniques were combined in order to create a matrix-free Preconditioned Conjugate Gradient method. This superior algorithm computes the same numerical solution to our problem as Matlab’s original method, but requires less memory and less time. Ultimately, the goal of optimizing the algorithm was achieved and convergence results are presented to confirm the accuracy of the new method and demonstrate its superiority.