Miracle-dz/UniFAR-FTUs huggingface dataset:
Mobile Phone Text Entry

The rapid growth of Short Message Service (SMS) text messaging has meant that a fast and efficient text input method is a very important aspect of a mobile phone interface. The best way to test a new method is an empirical evaluation, but this is a time consuming and complex task. An alternative would be to predict the performance of a new method using a prediction model. Previous prediction models and evaluations of current text entry methods are discussed. The previous models have been shown to be highly inaccurate. A new prediction technique is presented that uses pilot study data and text analysis instead of the complex mathematical formulas of previous techniques. Predictions for the multi-press with next and T9 input methods are compared to the actual performance results of an empirical evaluation. The empirical evaluation also compares the performance of the newer T9 system to that of the more common multi-press method. T9 was significantly faster when entering sentences containing only dictionary words. However, the two methods were not significantly different when entering a mix of sentences that contained dictionary and non-dictionary words. As T9 remembers non-dictionary words that a user enters, it was concluded that T9 was the better method. Subjective data reflected this with 60% of subjects stating that they preferred T9 over multi-press. The prediction technique was found to be too inaccurate to be useful to interface designers. The main failing of this and previous techniques appears to be the inability to accurately predict the mental preparation factor of the text entry actions. Further work to develop a method to calculate such values is needed.
Analysis and Calibration of Nonbinary-Weighted Capacitive DAC for High-Resolution SAR ADCs

This brief analyzes the effect of capacitor variation on the design of high-resolution nonbinary-weighted successive-approximation-register analog-to-digital converters in terms of radix, conversion steps, and accuracy. Moreover, the limitation caused by the one-side redundancy of the nonbinary-weighted network is addressed and a corresponding solution with a mathematical derivation is provided. In order to relax the mismatch requirement on the capacitor sizing while still ensuring enough linearity, a bottom-up weight calibration technique accounting for noise and offset errors is proposed, and its effectiveness is demonstrated. This calibration approach can be easily incorporated into a charge-redistribution converter without modifying its main architecture and conversion sequence.
First XMM-Newton observations of a cataclysmic variable I: Timing studies of OY Car

We present XMM-Newton observations of the eclipsing, disc accreting, cataclysmic variable OY Car which were obtained as part of the performance verification phase of the mission. The star was observed 4 days after an outburst and then again 5 weeks later when it was in a quiescent state. There is a quasi-stable modulation of the X-rays at ~2240 s, which is most prominent at the lowest energies. We speculate that this may be related to the spin period of the white dwarf. The duration of the eclipse ingress and egress in X-rays is 20-30 s. This indicates that the bulk of the X-ray emission originates from the boundary layer which has a negligible height above the surface of the white dwarf. The eclipse profile implies a white dwarf of mass $M_{1}=0.9{-}1.1$ $M_{\odot}$ and a secondary star of $M_{2}=0.08{-}0.11$ $M_{\odot}$.
Full Recognition of Massive Products Based on Property Set

In recent years, with the development of e-commerce, the number of online products increases rapidly. Faced with such a mass of data, we need to establish an efficient unified product standard. However, in terms of the whole e-commerce industry there lacks a unified standard which can coordinate with existing standards. In this paper, we put forward an effective solution to unifying products, which is then applied to business intelligence data analysis. The main work and contributions are as follows: 1) an effective solution to unifying product data, 2) a parallel data mining algorithms which solves the problem of identifying similar products from massive product data, 3) the framework is universal, although our project is based on Taobao's data cloud, it can also be applied to other e-commerce area.
Contrastive Learning and Neural Oscillations

The concept of Contrastive Learning (CL) is developed as a family of possible learning algorithms for neural networks. CL is an extension of Deterministic Boltzmann Machines to more general dynamical systems. During learning, the network oscillates between two phases. One phase has a teacher signal and one phase has no teacher signal. The weights are updated using a learning rule that corresponds to gradient descent on a contrast function that measures the discrepancy between the free network and the network with a teacher signal. The CL approach provides a general unified framework for developing new learning algorithms. It also shows that many different types of clamping and teacher signals are possible. Several examples are given and an analysis of the landscape of the contrast function is proposed with some relevant predictions for the CL curves. An approach that may be suitable for collective analog implementations is described. Simulation results and possible extensions are briefly discussed together with a new conjecture regarding the function of certain oscillations in the brain. In the appendix, we also examine two extensions of contrastive learning to time-dependent trajectories.
A temperature-sensitive NUP116 null mutant forms a nuclear envelope seal over the yeast nuclear pore complex thereby blocking nucleocytoplasmic traffic

NUP116 encodes a 116-kD yeast nuclear pore complex (NPC) protein that is not essential but its deletion (nup116 delta) slows cell growth at 23 degrees C and is lethal at 37 degrees C (Wente, S. R., M. P. Rout, and G. Blobel. 1992. J. Cell Biol. 119:705-723). Electron microscopic analysis of nup116 delta cells shifted to growth at 37 degrees C revealed striking perturbations of the nuclear envelope: a double membrane seal that was continuous with the inner and outer nuclear membranes had formed over the cytoplasmic face of the NPCs. Electron- dense material was observed accumulating between the cytoplasmic face of these NPCs and the membrane seal, resulting in "herniations" of the nuclear envelope around individual NPCs. In situ hybridization with poly(dT) probes showed the accumulation of polyadenylated RNA in the nuclei of arrested nup116 delta cells, sometimes in the form of punctate patches at the nuclear periphery. This is consistent with the electron microscopically observed accumulation of electron-dense material within the nuclear envelope herniations. We propose that nup116 delta NPCs remain competent for export, but that the formation of the membrane seals over the NPCs blocks nucleocytoplasmic traffic.
When Ethics and Law Clash a Look at the Ethical, Legal, and Moral Issues Arising from the Boston College Belfast Oral Project

Abstract This paper explores the ethical, legal, and moral Issues arising from the Boston College Belfast Oral Project initiated in 2001 across scholarly and non-scholarly sources. The researchers of the project entered into an understanding of confidentiality with the college for the project and the participants. An international subpoena request was served between the UK to the US government in 2011 requesting the project records. Bray (2011. “The Whole Story Behind the Boston College Subpoenas.” Chronicle of Higher Education 57 (41):B4–B5. http://chronicle.com/), Geoghegan (2012. “If Trust is lost, Future Promises Naught but Troubles for Research.” Times Higher Education 2033:28–9. https://www.timeshighereducation.com), King (2014. “‘Say Nothing’: Silenced Records and the Boston College Subpoenas.” Archives & Records 35 (1):28–42. doi:10.1080/23257962.2013. 859573), Lowman and Palys (2012. “Defending Research Confidentiality ‘To the Extent the Law Allows:’ Lessons From the Boston College Subpoenas.” Journal of Academic Ethics 10 (4):271–97. doi:10.1007/s10805-012-9172–5), McMutrie (2014. “Secrets from Belfast.” Chronicle of Higher Education 60 (20):1–1. http://chronicle.com) and Neuenschwander (2014. Major Legal Challenges Facing Oral History in the Digital Age.” In Oral History in the Digital Age, edited by D. Boyd, S. Cohen, B. Rakerd, and D. Rehberger. Institute of Library and Museum Services. http://ohda.matrix.msu.edu/2012/06/major-le), discuss the continuing issues surrendering the interview tapes from the project. The issues are presented viewed through Mason’s (1986. “Four Ethical Issues of the Information Age.” MIS Quarterly 10 (1):5–12. doi:10.2307/248873) model of the four ethical issues of the information age: Privacy, Accuracy, Property and Accessibility (PARA). The issues are viewed within framework of PARA, with lessons learned, the impact to future oral history academic research and a discussion on possible future research.
Sentic blending: Scalable multimodal fusion for the continuous interpretation of semantics and sentics

The capability of interpreting the conceptual and affective information associated with natural language through different modalities is a key issue for the enhancement of human-agent interaction. The proposed methodology, termed sentic blending, enables the continuous interpretation of semantics and sentics (i.e., the conceptual and affective information associated with natural language) based on the integration of an affective common-sense knowledge base with any multimodal signal-processing module. In this work, in particular, sentic blending is interfaced with a facial emotional classifier and an opinion mining engine. One of the main distinguishing features of the proposed technique is that it does not simply perform cognitive and affective classification in terms of discrete labels, but it operates in a multidimensional space that enables the generation of a continuous stream characterising user's semantic and sentic progress over time, despite the outputs of the unimodal categorical modules have very different time-scales and output labels.
The quest for solar gravity modes: probing the solar interior

The solar gravity modes are the best probes to improve our knowledge on the solar interior, as they spend most of their time in the radia- tive zone, which represents 98% of the solar mass. Many attempts have been led to observe them using dierent techniques: either individually, then adding some statistical approach or more recently, globally leading to the detection of the signature of asymptotical properties of these modes. Then, several theoret- ical works have been done to quantify the eect of detecting g-mode on solar modeling and on the rotation prole. We will give here an update on the g- mode detection. Then, we will study an example of a theoretical work showing how their detection would improve our knowledge on the dynamics of the solar core as well as an application on the detection of the global properties to infer some physical inputs in solar models.
Development and application of molecular biomarkers for characterizing Caribbean Yellow Band Disease in Orbicella faveolata

Molecular stress responses associated with coral diseases represent an under-studied area of cnidarian transcriptome investigations. Caribbean Yellow Band Disease (CYBD) is considered a disease of Symbiodinium within the tissues of the coral host Orbicella faveolata. There is a paucity of diagnostic tools to assist in the early detection and characterization of coral diseases. The validity of a diagnostic test is determined by its ability to distinguish host organisms that have the disease from those that do not. The ability to detect and identify disease-affected tissue before visible signs of the disease are evident would then be a useful diagnostic tool for monitoring and managing disease outbreaks. Representational Difference Analysis (RDA) was utilized to isolate differentially expressed genes in O. faveolata exhibiting CYBD. Preliminary screening of RDA products identified a small number of genes of interest (GOI) which included an early growth response factor and ubiquitin ligase from the coral host as well as cytochrome oxidase from the algal symbiont. To further characterize the specificity of response, quantitative real-time PCR (qPCR) was utilized to compare the expression profiles of these GOIs within diseased tissues (visible lesions), tissues that precede visible lesions by 2–4 cm (transition area), and tissues from healthy-looking colonies with no signs of disease. Results show there are distinctive differences in the expression profiles of these three GOIs within each tissue examined. Collectively, this small suite of GOIs can provide a molecular “finger print” which is capable of differentiating between infected and uninfected colonies on reefs where CYBD is known to occur.
A new distance measure for probability distribution function of mixture type

Evaluating the similarity between two probability distribution functions (PDF) is very important in various research problems. This paper proposes a new metric that computes the distance between two PDFs of mixture type directly from their parameters. It is posed as a linear programming problem and its theoretical properties and performance are analyzed, experimented, and compared with existing measures. In addition, as a proof of concept, we applied the new metric to the problem of audio retrieval where involved PDFs are GMMs (Gaussian mixture model) with 4 mixtures. Experimental results on both synthetic and real data show that this new distance measure is quite promising.
Algebraic Optimization of Relational Preference Queries

The design and implementation of advanced personalized database applications requires a prefer-ence-driven approach. Representing preferences as strict partial orders is a good choice in most practical cases. Therefore the efficient integration of preference querying into standard data-base technology is an important issue. We pres-ent a novel approach to relational preference query optimization based on algebraic transformations. A variety of new laws for preference relational algebra is presented. This forms the foundation for a preference query optimizer applying heuristics like ‘push preference’. A pro-totypical implementation and a series of benchmarks show that significant performance gains can be achieved. In summary, our results give strong evidence that by extending relational databases by strict partial order preferences one can get both: good modeling capabilities for personalization and good query runtimes. Our approach extends to recursive databases as well.
Quantifying the Interference Gray Zone in Wireless Networks: A Measurement Study

In wireless networks where communications are made over a shared medium, interference and collisions are the primary causes of packet drops. In multi-hop networks such as wireless mesh networks, due to the hidden terminal problem, limiting the effects of collisions and interference is a key in achieving high performance. Typical wireless medium access control protocols perform carrier sensing to avoid collisions. Most research efforts so far has used the binary model when studying carrier sensing and interference; a node is either carrier sensed or not, and a link is either interfered or not. In reality however, there exists a gray zone. Carrier sensing and interference should be represented in continuous values. Using the measurement data from our 802.11a wireless mesh network test-bed, we propose metrics that represent the levels of carrier sensing and interference. Using our metrics, we also propose methods to estimate broadcast throughput and goodput. We evaluate the accuracy of our methods by comparing our models with the measured data. In addition, we investigate the impact of the capture effect on interference.
Mapping, Enriching and Interlinking Data from Heterogeneous Distributed Sources

As Linked Open Data is gaining traction, publishers incorporate more their data to the cloud. Since the whole Web of Data cannot be semantically represented though, data consumers should also be able to map any content to rdf on-demand to answer complicated queries by integrating information from multiple heterogeneous sources distributed over the Web or not. In both cases, the quality and integrity of the generated rdf output affects the performance of traversing and querying the Linked Open Data. Thus, well-considered and automated approaches to semantically represent and interlink, already during mapping, the domain level information of distributed heterogeneous sources is required. In this paper, we outline a plan to tackle this problem: We propose a uniform way of defining how to map and interlink data from heterogeneous sources, alternative approaches to perform the mappings and methods to assess the quality and integrity of the resulting Linked Data sets.
Quantization design for unconstrained distributed optimization

We consider an unconstrained distributed optimization problem and assume that the bit rate of the communication in the network is limited. We propose a distributed optimization algorithm with an iteratively refining quantization design, which bounds the quantization errors and ensures convergence to the global optimum. We present conditions on the bit rate and the initial quantization intervals for convergence, and show that as the bit rate increases, the corresponding minimum initial quantization intervals decrease. We prove that after imposing the quantization scheme, the algorithm still provides a linear convergence rate, and furthermore derive an upper bound on the number of iterations to achieve a given accuracy. Finally, we demonstrate the performance of the proposed algorithm and the theoretical findings for solving a randomly generated example of a distributed least squares problem.
A Relational Model of Data Large Shared Data Banks

Future users of large data banks must be protected from having to know how the data is organized in the machine (the internal representation). A prompting service which supplies such information is not a satisfactory solution. Activities of users at terminals and most application programs should remain unaffected when the internal representation of data is changed and even when some aspects of the external representation are changed. Changes in data representation will often be needed as a result of changes in query, update, and report traffic and natural growth in the types of stored information. Existing noninferential, formatted data systems provide users with tree-structured files or slightly more general network models of the data. In Section 1, inadequacies of these models are discussed. A model based on n-ary relations, a normal form for data base relations, and the concept of a universal data sublanguage are introduced. In Section 2, certain operations on relations (other than logical inference) are discussed and applied to the problems of redundancy and consistency in the user's model.
Modulating Biofunctional starPEG Heparin Hydrogels by Varying Size and Ratio of the Constituents

Heparin and four-armed, end-functionalized polyethylene glycol (starPEG) were recently combined in sets of covalently linked biohybrid hydrogel networks capable of directing various therapeutically relevant cell types. To extend the variability and applicability of this novel biomaterials platform, the influence of size and molar ratio of the two building blocks on the hydrogel properties was investigated in the present study. Heparin and starPEG were converted in various molar ratios and in different molecular weights to tune swelling, stiffness and pore size of the obtained polymer networks. Hydrogels with a range of elastic moduli could be generated by controlling either the crosslinking density or the chain length of the starPEG, whereas altering the molecular mass of heparin did not significantly affect hydrogel strength. The concentration of heparin in the swollen gels was found to be nearly invariant at varying crosslinking degrees for any given set of building blocks but adjustable by the size of the building blocks. Since heparin is the base for all biofunctionalization schemes of the gels these findings lay the ground for an even more versatile customization of this powerful new class of biomaterials.
Energy Prediction in Smart Environments

In the past decade, smart home environment research has found application in many areas, such as activity recognition, visualization, and automation. However, less attention has been paid to monitoring, analyzing, and predicting energy usage in smart homes, despite the fact that electricity consumption in homes has grown dramatically. In this paper, we extract the useful features from sensor data collected in the smart home environment and select the most significant features based on mRMR feature selection criterion, then utilize three machine learning algorithms to predict energy use given these features. To validate these algorithms, we use real sensor data collected from volunteers living in our smart apartment testbed. We compare the performance between alternative learning algorithms and analyze the results of two experiments performed in the smart home.
Modeling Searcher Frustration

When search engine users have trouble finding what they are looking for, they become frustrated. In a pilot study, we found that 36% of queries submitted end with users being moderately to extremely frustrated. By modeling searcher frustration, search engines can predict the current state of user frustration, tailor the search experience to help the user find what they are looking for, and avert them from switching to another search engine. Among other observations, we found that across the fifteen users and six tasks in our study, frustration follows a law of conservation: a frustrated user tends to stay frustrated and a non-frustrated user tends to stay not frustrated. Future work includes extracting features from the query log data and readings from three physical sensors collected during the study to predict searcher
Role model detection using low rank similarity matrix

Computing meaningful clusters of nodes is crucial to analyse large networks. In this paper, we apply new clustering methods to improve the computational time. We use the properties of the adjacency matrix to obtain better role extraction. We also define a new non-recursive similarity measure and compare its results with the ones obtained with Browet's similarity measure. We will show the extraction of the different roles with a linear time complexity. Finally, we test our algorithm with real data structures and analyse the limit of our algorithm.
Listen reader: an electronically augmented paper-based book

While predictions abound that electronic books will supplant traditional paper-based books, many people bemoan the coming loss of the book as cultural artifact. In this project we deliberately keep the affordances of paper books while adding electronic augmentation. The Listen Reader combines the look and feel of a real book - a beautiful binding, paper pages and printed images and text - with the rich, evocative quality of a movie soundtrack. The book's multi-layered interactive soundtrack consists of music and sound effects. Electric field sensors located in the book binding sense the proximity of the reader's hands and control audio parameters, while RFID tags embedded in each page allow fast, robust page identification. Three different Listen Readers were built as part of a six-month museum exhibit, with more than 350,000 visitors. This paper discusses design, implementation, and lessons learned through the iterative design process, observation, and visitor interviews.