Miracle-dz/UniFAR-FTUs huggingface dataset:
AUTOMATICALLY GENERATING TREE ADJOINING GRAMMARS FROM ABSTRACT SPECIFICATIONS

The paper describes a system that can automatically generate tree adjoining grammars from abstract specifications. Our system is based on the use of tree descriptions to specify a grammar by separately defining pieces of tree structure that encode independent syntactic principles. Various individual specifications are then combined to form the elementary trees of the grammar. The system enables efficient development and maintenance of a grammar, and also allows underlying linguistic constructions (such as wh‐movement) to be expressed explicitly. We have carefully designed our system to be as language independent as possible and tested its performance by constructing both English and Chinese grammars, with significant reductions in grammar development time. Provably consistent abstract specifications for different languages also offer unique opportunities for investigating how languages relate to themselves and to each other. For instance, the impact of a linguistic structure such as wh‐movement can be traced from its specification to the descriptions that it combines with, to its actual realization in trees. By focusing on syntactic properties at a higher level, our approach allowed a unique comparison of our English and Chinese grammars.
Analysis of Time and Frequency Domain Pace Algorithms for OFDM with Virtual Subcarriers

This paper studies common linear frequency direction pilot- symbol aided channel estimation algorithms for orthogonal frequency division multiplexing in a UTRA long term evolution context. Three deterministic algorithms are analyzed: the maximum likelihood (ML) approach, the noise reduction algorithm (NRA) and the robust Wiener (RW) filter. A closed form mean squared error is provided for these three algorithms. Analytical and simulation results show that, in the presence of virtual subcarriers, the ML can suffer large performance degradation due to ill-conditioned matrix issues. A solution to this problem is to use the Tikhonov regularization method giving the NRA. The equivalence between the NRA and the RW is proved analytically. A practical implementation of the NRA and RW is proposed based on partial-input partial-output FFT, leading to 6 to 8 times lower complexity than the reference implementation.
Engineering microenvironments to control stem cell fate and function

The two definitive characteristics of stem cells, the capacity for proliferation without loss of potency and the ability to differentiate into specialized cell type(s), endow these cells with great promise for the development of cell replacement therapies for degenerative disease and injury. Furthermore, stem cells are increasingly being recognized as versatile systems for developmental biologists to study lineage specification and tissue organization. Understanding the mechanisms that underlie stem cell self-renewal and differentiation is central to realizing this potential, and efforts have increasingly focused on elucidating the signals that regulate cell function in the tissues where they reside, i.e. the endogenous stem cell niche. The governing principle of this niche, first proposed 30 years ago, is that stem cell fate decisions are influenced by cells’ interactions with components of their microenvironment, and these cell extrinsic factors include soluble and immobilized factors, the extracellular matrix, and signals presented by neighboring cells. Thus, recreating or simulating this microenvironment may be critical for properly expanding and controlling the differentiation of stem cells outside the body, for both basic biological study and therapeutic translation. In this chapter we review the biology of stem cell niches, focusing on the mechanisms and “strategies” by which various niche components support stem cell functions, including immobilization of signals to control activity and dosage, progressive specification of cell behavior by a series of sequential signals, complex signaling
Data-based research at IIT Bombay

1. OVERVIEW The Indian Institute of Technology (IIT) Bombay has a history of research and development in the area of databases, dating back to the early 1980s. D. B. Phatak and N. L. Sarda were among the first faculty members at IIT Bombay to work in the area of database systems. This was a period when the financial sector of India, headquartered primarily in Bombay (now renamed Mumbai) saw a spurt in computerization, and IIT Bombay faculty played a leading role as consultants for database implementations in these companies. Research in the area of databases began in the early 1980s, but increased greatly from the early 1990s, with the hiring of several faculty including S. Seshadri, S. Sudarshan, and later Krithi Ramamritham, who moved to IIT Bombay from U. Mass. Amherst in the early to mid 1990s. With the hiring of Sunita Sarawagi and Soumen Chakrabarti in the late 1990s, there was a significant broadening, with the group no longer being just a database group, but rather a much broader data management group, with interests in information retrieval, and data mining. More recently Ganesh Ramakrishnan joined our group, further increasing its strengths in information retrieval and data mining. The number of PhD students increased from around 1 or 2 enrolled at a time in the early 1990s, to about 12 to 15 students at a time in recent years. While this number is much better than earlier, and is increasing rapidly, it is still small by most standards. However, our master’s and bachelor’s students have compensated for the shortage of PhD students, and have made very significant contributions to our research efforts, with well over three fourths of our papers having such students as coauthors. Today, the group covers a diverse range of interests, which you can see from the different research projects showcased in this article. In the following sections, we outline the major research projects of the group. We wrap up the article with a summary of other contributions to the community, by group members. For more information about the group, please visit: http://www.cse. iitb.ac. in/infolab
CloudAware: Towards context-adaptive mobile cloud computing

The widespread use of mobile devices such as smartphones and tablets is flanked by an ever increasing supply of mobile applications. Along with this trend, expectations and requirements of users rise as well. For example, users do not want to compromise on comfortable daily routines as available on desktop computers. However, an intrinsic characteristic of mobile devices is their limited availability of resources (e.g., CPU, storage, bandwidth, energy) hindering in particular computation-intensive tasks. In this scenario, mobile cloud computing (MCC) promises to overcome these limitations by offering apparently infinite resources in the infrastructure that are transparently accessible also for mobile applications. In order to easily benefit from these offerings, dynamic code offloading has been proposed by several approaches recently. However, such solutions either do not consider the complexity arising from the dynamically changing context in mobile environments adequately or have a steep learning curve inhibiting easy adoption by developers. Therefore, this paper presents a novel approach towards context-adaptive mobile cloud computing. For that, first an extensive requirements analysis was conducted merging ISO standards with users', applications' and developers' needs. Based on this, an evaluation of related MCC-approaches allowed identifying promising concepts as well as current shortcomings. As a result, an MCC-framework, called CloudAware, is proposed that eases the development of MCC-applications by offering programming abstractions, multi-level distribution transparency, context adaptation features and is hands-free for end-users.
CEREBRAL BLOOD FLOW AND METABOLISM DURING EXERCISE

An approximately 20% increase in cerebral blood flow (CBF) and the cerebral metabolic rate for oxygen manifest during whole body exercise with determination of brain tissue flow and arterial inflow to the brain. Yet, during intense exercise CBF approaches the resting level in response to the decrease in arterial carbon dioxide tension with the exponential increase in ventilation. Here it is illustrated that the increase in CBF during exercise appears to depend to the ability to raise cardiac output and it is speculated whether there is a sympathetic restrain on CBF when the increase in cardiac output is small. Furthermore, it is considered whether the restrain in CBF during intense exercise affects cerebral oxygenation to an extent that it provokes so-called central fatigue. Finally it is highlighted that the cerebral activation associated with exercise provokes uptake of carbohydrate, notably of lactate, that cannot be accounted for by the concomitant increase in the cerebral metabolic rate for oxygen and it is pointed out that it remains unknown why that apparently surplus carbohydrate uptake by the brain is in need.
Gold Nanowire Based Electrical DNA Detection Using Rolling Circle Amplification

We present an electrical sensor that uses rolling circle amplification (RCA) of DNA to stretch across the gap between two electrodes, interact with metal nanoparticle seeds to generate an electrically conductive nanowire, and produce electrical signals upon detection of specific target DNA sequences. RCA is a highly specific molecular detection mechanism based on DNA probe circularization. With this technique, long single-stranded DNA with simple repetitive sequences are produced. Here we show that stretched RCA products can be metalized using silver or gold solutions to form metal wires. Upon metallization, the resistance drops from TΩ to kΩ for silver and to Ω for gold. Metallization is seeded by gold nanoparticles aligned along the single-stranded DNA product through hybridization of functionalized oligonucleotides. We show that combining RCA with electrical DNA detection produces results in readout with very high signal-to-noise ratio, an essential feature for sensitive and specific detection assays. Finally, we demonstrate detection of 10 ng of Escherichia coli genomic DNA using the sensor concept.
Integrated assessment models of global climate change

▪ Abstract We review recent work in the integrated assessment modeling of global climate change. This field has grown rapidly since 1990. Integrated assessment models seek to combine knowledge from multiple disciplines in formal integrated representations; inform policy-making, structure knowledge, and prioritize key uncertainties; and advance knowledge of broad system linkages and feedbacks, particularly between socioeconomic and biophysical processes. They may combine simplified representations of the socioeconomic determinants of greenhouse gas emissions, the atmosphere and oceans, impacts on human activities and ecosystems, and potential policies and responses. We summarize current projects, grouping them according to whether they emphasize the dynamics of emissions control and optimal policy-making, uncertainty, or spatial detail. We review the few significant insights that have been claimed from work to date and identify important challenges for integrated assessment modeling in its relationships to.
Gene Expression Analysis Reveals New Possible Mechanisms of Vancomycin-Induced Nephrotoxicity and Identifies Gene Markers Candidates

Vancomycin, one of few effective treatments against methicillin-resistant Staphylococcus aureus, is nephrotoxic. The goals of this study were to (1) gain insights into molecular mechanisms of nephrotoxicity at the genomic level, (2) evaluate gene markers of vancomycin-induced kidney injury, and (3) compare gene expression responses after iv and ip administration. Groups of six female BALB/c mice were treated with seven daily iv or ip doses of vancomycin (50, 200, and 400 mg/kg) or saline, and sacrificed on day 8. Clinical chemistry and histopathology demonstrated kidney injury at 400 mg/kg only. Hierarchical clustering analysis revealed that kidney gene expression profiles of all mice treated at 400 mg/kg clustered with those of mice administered 200 mg/kg iv. Transcriptional profiling might thus be more sensitive than current clinical markers for detecting kidney damage, though the profiles can differ with the route of administration. Analysis of transcripts whose expression was changed by at least twofold compared with vehicle saline after high iv and ip doses of vancomycin suggested the possibility of oxidative stress and mitochondrial damage in vancomycin-induced toxicity. In addition, our data showed changes in expression of several transcripts from the complement and inflammatory pathways. Such expression changes were confirmed by relative real-time reverse transcription–polymerase chain reaction. Finally, our results further substantiate the use of gene markers of kidney toxicity such as KIM-1/Havcr1, as indicators of renal injury.
Nearest group queries

k nearest neighbor (kNN) search is an important problem in a vast number of applications, including clustering, pattern recognition, image retrieval and recommendation systems. It finds k elements from a data source D that are closest to a given query point q in a metric space. In this paper, we extend kNN query to retrieve closest elements from multiple data sources. This new type of query is named k nearest group (kNG) query, which finds k groups of elements that are closest to q with each group containing one object from each data source. kNG query is useful in many location based services. To efficiently process kNG queries, we propose a baseline algorithm using R-tree as well as an improved version using Hilbert R-tree. We also study a variant of kNG query, named kNG Join, which is analagous to kNN Join. Given a set of query points Q, kNG Join returns k nearest groups for each point in Q. Such a query is useful in publish/subscribe systems to find matching items for a collection of subscribers. A comprehensive performance study was conducted on both synthetic and real datasets and the experimental results show that Hilbert R-tree achieves significantly better performance than R-tree in answering both kNG query and kNG Join.
Q methodology in health economics

The recognition that health economists need to understand the meaning of data if they are to adequately understand research findings which challenge conventional economic theory has led to the growth of qualitative modes of enquiry in health economics. The use of qualitative methods of exploration and description alongside quantitative techniques gives rise to a number of epistemological, ontological and methodological challenges: difficulties in accounting for subjectivity in choices, the need for rigour and transparency in method, and problems of disciplinary acceptability to health economists. Q methodology is introduced as a means of overcoming some of these challenges. We argue that Q offers a means of exploring subjectivity, beliefs and values while retaining the transparency, rigour and mathematical underpinnings of quantitative techniques. The various stages of Q methodological enquiry are outlined alongside potential areas of application in health economics, before discussing the strengths and limitations of the approach. We conclude that Q methodology is a useful addition to economists' methodological armoury and one that merits further consideration and evaluation in the study of health services.
BLIND SEPARATION OF MIXED-KURTOSIS SIGNALS USING AN ADAPTIVE THRESHOLD NONLINEARITY

A parameterized threshold nonlinearity, which separates a mixture of signals with any distribution (except for Gaussian), is introduced. This nonlinearity is particularly simple to implement, since it neither uses hyperbolic nor polynomial functions, unlike most nonlinearities used for blind separation. For some specific distributions, the stable region of the threshold parameter is derived, and optimal values for best separation performance are given. If the threshold parameter is made adaptive during the separation process, the successful separation of signals whose distribution is unknown is demonstrated and compared against other known methods.
Restructuring Public Transit Systems : Evidence on Cost Properties and Optimal Network Configuration from Medium and Large-Sized Companies

This paper analyses the cost structure of a sample of Italian local public transport (LPT) companies operating in medium and large urban centres. The main focus is to identify the proper network configuration for the LPT service, by verifying the presence and the extent of scale and density economies. Technological characteristics of public transit systems are analysed by estimating both variable and total cost function models, which consider three alternative supply-oriented output measures, include firm-specific fixed effects, and allow for X-inefficiency to play a role through the estimation of a stochastic cost frontier. The evidence is remarkably robust across the different specifications which have been tested and shows the presence of short-run and long-run economies of scale, as well as of economies of network density, for both the average sample firm and for operators belonging to the highest percentile (large-sized companies). This suggests that, from a technological point of view, a proper LPT network design should at least include a large urban centre and should be extended so as to embrace the intercity service too, while a regulatory policy aimed at fragmenting the served area in various sub-networks would imply an efficiency loss.
Multi-Antenna Broadcast Channels with Limited Feedback and User Selection

We analyze the sum-rate performance of a multi-antenna downlink system carrying more users than transmit antennas, with partial channel knowledge at the transmitter due to finite rate feedback. In order to exploit multiuser diversity, we show that the trans mitter must have, in addition to directional information, information regarding the quality of each channel. Such information should reflect both the channel magnitude and the quantization error. Expressions for the SINR distribution and the sum-rate are derived, and tradeoffs between the number of feedback bits, the number of users, and the SNR are observed. In particular, for a target performance, having more users reduces feedback load.
Oiko-nomic Threads

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author. Copyright is held by the authors. ISWC’14 Adjunct, Sep 13 September 17 2014, Seattle, USA ACM 978-1-4503-3048-0/14/09. http://dx.doi. org/10.1145/2641248.2641281 Abstract Oiko-nomic Threads is a collective art project for an algorithmically controlled knitting machine and open data. The installation represents a system commenting on the notion of work through the production of a textile in real-time. By means of rethinking, modifying and redefining the functionality of an obsolete knitting machine and employing financial data from the databases of the Greek National Manpower Employment Offices as well as selected patterns inspired by Greek folk art, a textile is generated algorithmically. This way, the woven textile is to be understood as both a document of its own making as well as a dynamic base of archival resources which presents a computer-generated interpretation of the original financial data.
MULTIMODAL BIOMETRICS BASED AUTHENTICATION AGAINST DICTIONARY ATTACKS

The Multimodal Biometric based user authentication systems are highly secured and efficient to use and place total trust on the authentication server where biometric verification data are stored in a central database. Such systems are, prone to dictionary attacks initiated at the server side. In this paper, we propose an efficient approach based on multimodal biometrics (Iris and fingerprint) based user authentication and key exchange system. In this system, minutiae points and texture properties are extracted from the fingerprint and iris images are stored in the encrypted form in the server’s database, to overcome the dictionary attacks mounted by the server. The image processing techniques are used to extract a biometric measurement from the fingerprint and iris. During login procedure the mutual authentication is done between the server and user and a symmetric key is generated on both sides, which could be used for further secure communication between them. Thus meet-in-the middle attack that happens between the user and the server can also be overcome. This system can be directly applied to strengthen existing password or biometric based systems without requiring additional computation. KeywordsAuthentication, Dictionary Attack, Fingerprint, Fusion, Iris, Key Exchange, Minutiae points.
NDE Techniques for Reliable Inspection of Carbon Steel Tubes

Ferromagnetic tubes in Heat Exchangers, Boilers, Air Fin Coolers are most common in the Petrochemical Industry. Carbon Steel, an inexpensive material has good mechanical properties and heat transfer capabilities. However, it is often employed in conditions where corrosion if it sets in, acts very fast. Over the years with shrinking maintenance budgets and longer intervals between plant shutdowns the need for reliable inspection of these units has increased. The advent of digital technology, several enhancements to the inspection equipment based on RFEC, Magnetic Flux Leakage, Ultrasonic IRIS have taken place. Each of these techniques has its own merits and demerits. This paper presents an overview of the techniques available for carbon steel inspection discussing their merits and demerits. It also presents a methodology of selecting the right combination of techniques to achieve reliable inspection results using practical case studies of field work carried out.
Design and Simulation of Digital PLL Synchronizer for BPSK and QPSK Based on Software Defined Radio

This paper presents a design and simulation of digital PLL synchronizer, using Costas loop based on SDR for high frequency communication systems. Design parameters are selected for each unit of the proposed systems in order to accommodate SDR requirements. Different techniques for carrier recovery based on SDR are discussed. PLL techniques is chosen for synchronization, since it is one of the most active synchronization techniques. BPSK and QPSK synchronizers for coherent receivers have been designed and simulated based on SDR using both Costas loop and modified Costas loop. The simulation result shows that these two systems are reliable in recovering the carrier phase and frequency when significant frequency and phase are present. Simulation result shows that the BPSK system has e P = 3 10 - at o b N E / equal to 8.5 dB in the presence of AWGN and has the ability to track frequency offset up to 1200Hz with 2*10 4 - probability of bit error at o b N