Miracle-dz/UniFAR-FTUs huggingface dataset:
Connection-less communication of IoT devices over LTE mobile networks

The emergence of the Internet of Things (IoT) introduces a vast ecosystem of new network-enabled objects. Although most current cellular IoT services run over 2G and 3G cellular networks, the Long Term Evolution (LTE) is expected to be one of the main platforms for the emergence of new Machine to Machine (M2M) communication systems. Cellular communication protocols were designed and optimized to handle human-originated communications. However, with the forecasted deployment of billions of M2M devices, there is a growing concern in the industry that the cellular core may be overloaded by the sharp increase in control plane signaling load. The traffic characteristics of IoT devices are very different from those of smartphones and can enhance the risk for signaling storms. A new connection-less communication protocol for IoT systems over LTE mobile networks is proposed to spare signaling exchanges at the cellular core for M2M communications. It requires no standards modification and provides an overlaying channel between connected objects and base stations. Its effectiveness is demonstrated through simulations with realistic background network load parameters extracted from real LTE traffic sniffed from a busy downtown Manhattan intersection.
N-best parallel maximum likelihood beamformers for robust speech recognition

This work aims at improving speech recognition in noisy environments using a microphone array. The proposed approach is based on a preliminary generation of N-best hypotheses. The use of an adaptive maximum likelihood beamformer (the Limabeam algorithm), applied in parallel to each hypothesis, leads to an updated set of transcriptions, among which the maximally likely to clean speech models is selected. Results show that this method improves recognition accuracy over both Delay and Sum Beamforming and Unsupervised Limabeam especially at low SNRs. Results also show that it can recover the recognition errors made in the first recognition step.
Zooming interfaces for augmented reality browsers

Augmented Reality combines real world and virtual information in interactive visualizations. Since phones started integrating GPS, compass and accelerometer, several Augmented Reality browsers for phones have hit the market. These are applications that access large amounts of geo-referenced information from online sources and present it at corresponding physical locations, superimposed onto a live video stream. However, Augmented Reality is constrained by the camera's field of view and restricted to first- person views, limiting the amount of overview that users can gain. We present two zooming interfaces that compensate for these constraints by enabling users to smoothly zoom between the Augmented Reality view and (1) an egocentric panoramic view of 360°, and (2) an exocentric top-down view. We present the results from two studies that show how in most search tasks our zooming interfaces are faster and require less panning than an overlay- based tool, scaling better as the amount of information grows.
Essays in Corporate Prediction Markets

Personal subjective opinions are one of the most important assets in management. Prediction markets are mechanisms that can be deployed to elicit and aggregate a group of people’s opinions regarding the outcome of future events at any point in time. Prediction markets are exchange-traded markets where security values are tied to the outcome of future events. Prediction markets are systematically designed in a way that that their market prices capture the crowd’s consensus about the probability of a future event. Corporations harness internal prediction markets for managerial decision making and business forecasting. Prediction markets are traditionally designed for large and diverse populations, two properties that are not often displayed in corporate settings. Therefore special considerations must be given to prediction markets used in corporations. Our first contribution in this thesis is in addressing the issue of diversity, in the sense of risk preferences, in corporate prediction markets. We study prediction markets in the presence of risk averse or risk seeking agents that have unknown risk preferences. We show that such agents’ behavior is not desirable for the purpose of information aggregation. We then characterize the agents’ behavior with respect to prediction market parameters and offer a systematic method to market organizers that fine tunes market parameters so at to best mitigate the impact of a pool agents’ risk-preferences. Our Second contribution in this thesis is in recommending prediction market mechanisms in different settings. There are many prediction market mechanisms with various advantages and weaknesses. The choice of a market mechanism can heavily affect the market accuracy and in turn, the success of a managerial decision, or a forecast based on prediction markets’ prices. Using trade data from two real-world prediction markets, we study the two main types of prediction markets mechanism and provide the much-needed insight as to what market mechanism to choose in various situations.
Cancer stem cells and early stage basal-like breast cancer

Ductal carcinoma in situ (DCIS) is a category of early stage, non-invasive breast tumor defined by the intraductal proliferation of malignant breast epithelial cells. DCIS is a heterogeneous disease composed of multiple molecular subtypes including luminal, HER2 and basal-like types, which are characterized by immunohistochemical analyses and gene expression profiling. Following surgical and radiation therapies, patients with luminal-type, estrogen receptor-positive DCIS breast tumors can benefit from adjuvant endocrine-based treatment. However, there are no available targeted therapies for patients with basal-like DCIS (BL-DCIS) tumors due to their frequent lack of endocrine receptors and HER2 amplification, rendering them potentially susceptible to recurrence. Moreover, multiple lines of evidence suggest that DCIS is a non-obligate precursor of invasive breast carcinoma. This raises the possibility that targeting precursor BL-DCIS is a promising strategy to prevent BL-DCIS patients from the development of invasive basal-like breast cancer. An accumulating body of evidence demonstrates the existence of cancer stem-like cells (CSCs) in BL-DCIS, which potentially determine the features of BL-DCIS and their ability to progress into invasive cancer. This review encompasses the current knowledge in regard to the characteristics of BL-DCIS, identification of CSCs, and their biological properties in BL-DCIS. We summarize recently discovered relevant molecular signaling alterations that promote the generation of CSCs in BL-DCIS and the progression of BL-DCIS to invasive breast cancer, as well as the influence of the tissue microenvironment on CSCs and the invasive transition. Finally, we discuss the translational implications of these findings for the prognosis and prevention of BL-DCIS relapse and progression.
Reversed Visual Motion and Self-Sustaining Eye Oscillations

A random-dot field undergoing counterphase flicker paradoxically appears to move in the same direction as head and eye movements, ie opposite to the optic-flow field. The effect is robust and occurs over a wide range of flicker rates and pixel sizes. The phenomenon can be explained by reversed phi motion caused by apparent pixel movement between successive retinal images. The reversed motion provides a positive feedback control of the display, whereas under normal conditions retinal signals provide a negative feedback. This altered polarity invokes self-sustaining eye movements akin to involuntary optokinetic nystagmus.
A unified RLC model for high-speed on-chip interconnects

In this paper, we propose a compact on-chip interconnect model for full-chip simulation. The model consists of two components, a quasi-three-dimensional (3-D) capacitance model and an effective loop inductance model. In the capacitance model, we propose a novel concept of effective width (W/sub eff/) for a 3-D wire, which is derived from an analytical two-dimensional (2-D) model combined with a new analytical "wall-to-wall" model. The effective width provides a physics-based approach to decompose any 3-D structure into a series of 2-D segments, resulting in an efficient and accurate capacitance extraction. In the inductance model, we use an effective loop inductance approach for an analytic and hierarchical model construction. In particular, we show empirically that high-frequency signals (above multi-GHz) propagating through random signal lines can be approximated by a quasi-TEM mode relationship, leading to a simple way to extract the high-frequency inductance from the capacitance of the wire. Finally, the capacitance and inductance models are combined into a unified frequency-dependent RLC model, describing successfully the wide-band characteristics of on-chip interconnects up to 100 GHz. Non-orthogonal wire architecture is also investigated and included in the proposed model.
Context-aware mobile assistants for optimal interaction: a prototype for supporting the business traveler

Travel has many situations where context-aware computing can bring important benefits: pointing out notorious delays or bad weather during the planning phase, allowing the user to replan for handling unexpected situations, or suggesting flight alternatives to avoid strikes.This paper briefly describes an approach for integrating context-aware computing to a mobile travel assistant. We show how context-aware techniques can ease user interaction within mobile device applications. The presented ideas are illustrated with a scenario using a first version of a working prototype called Pocket reality.
Formation of Novel TRPC Channels by Complex Subunit Interactions in Embryonic Brain*

Mammalian short TRP channels (TRPCs) are putative receptor- and store-operated cation channels that play a fundamental role in the regulation of cellular Ca2+ homeostasis. Assembly of the seven TRPC homologs (TRPC1-7) into homo- and heteromers can create a large variety of different channels. However, the compositions as well as the functional properties of native TRPC complexes are largely undefined. We performed a systematic biochemical study of TRPC interactions in mammalian brain and identified previously unrecognized channel heteromers composed of TRPC1, TRPC4, or TRPC5 and the diacylglycerol-activated TRPC3 or TRPC6 subunits. The novel TRPC heteromers were found exclusively in embryonic brain. In heterologous systems, we demonstrated that assembly of these novel heteromers required the combination of TRPC1 plus TRPC4 or TRPC5 subunits along with diacylglycerol-sensitive subunits in the channel complexes. Functional interaction of the TRPC subunits was verified using a dominant negative TRPC5 mutant (TRPC5DN). Co-expression of TRPC5DN suppressed currents through TRPC5- and TRPC4-containing complexes; TRPC3-associated currents were unaffected by TRPC5DN unless TRPC1 was also co-expressed. This complex assembly mechanism increases the diversity of TRPC channels in mammalian brain and may generate novel heteromers that have specific roles in the developing brain.
Better bounds for online load balancing on unrelated machines

We study the problem of scheduling permanent jobs on unrelated machines when the objective is to minimize the Lp norm of the machine loads. The problem is known as load balancing under the Lp norm. We present an improved upper bound for the greedy algorithm through simple analysis; this bound is also shown to be best possible within the class of deterministic online algorithms for the problem. We also address the question whether randomization helps online load balancing under Lp norms on unrelated machines; this is a challenging question which is open for more than a decade even for the L2 norm. We provide a positive answer to this question by presenting the first randomized online algorithms which outperform deterministic ones under any (integral) Lp norm for p = 2,…,137. Our algorithms essentially compute in an online manner a fractional solution to the problem and use the fractional values to make random choices. The local optimization criterion used at each step is novel and rather counterintuitive: the values of the fractional variables for each job correspond to flows at an approximate Wardrop equilibrium for an appropriately defined non-atomic congestion game. As corollaries of our analysis and by exploiting the relation between the Lp norm and the makespan of machine loads, we obtain new competitive algorithms for online makespan minimization, making progress in another longstanding open problem.
Dopamine D3 Receptor Antagonists as Potential Therapeutics for the Treatment of Neurological Diseases

D3 receptors represent a major focus of current drug design and development of therapeutics for dopamine-related pathological states. Their close homology with the D2 receptor subtype makes the development of D3 selective antagonists a challenging task. In this review, we explore the relevance and therapeutic utility of D3 antagonists or partial agonists endowed with multireceptor affinity profile in the field of central nervous system disorders such as schizophrenia and drug abuse. In fact, the peculiar distribution and low brain abundance of D3 receptors make them a valuable target for the development of drugs devoid of motor side effects classically elicited by D2 antagonists. Recent research efforts were devoted to the conception of chemical templates possibly endowed with a multi-target profile, especially with regards to other G-protein-coupled receptors (GPCRs). A comprehensive overview of the recent literature in the field is herein provided. In particular, the evolution of the chemical templates has been tracked, according to the growing advancements in both the structural information and the refinement of the key pharmacophoric elements. The receptor/multireceptor affinity and functional profiles for the examined compounds have been covered, together with their most significant pharmacological applications.
Push and pull escape travel motivations of Emirati nationals to Australia

Purpose The purpose of this study is to examine the escape motivations of the emerging market and provide suggestions for Australia’s promotion. This study adopts the push and pull framework to identify travel motivations of Emirati nationals to Australia. Design/methodology/approach This study uses a series of focus groups and in-depth interviews to understand the escape motivations that encourage Emiratis to leave their home country and travel to Australia for a holiday. Findings The results indicate that Emiratis are motivated to travel to Australia by three escape factors: physical, interpersonal and fun. The internal motivations that encourage Emiratis to escape their home country are inseparable from Australia’s external attributes that attract the Emiratis to the country. Originality/value The study contributes to the theory of tourist motivation by supporting it in the culturally different Muslim/Arab context, which has not been explored before. The authors argue that it is not so much what Australia offers and what escape needs the Emiratis can fulfil in Australia, but rather that Australia serves the Emiratis well and meets their escape needs.
OnCU system: ontology-based category utility approach for author name disambiguation

Author name disambiguation is essential for improving performance of document indexing, retrieval, and web search. Author name disambiguation resolves the conflict when multiple authors share the same name label. This paper introduces a novel approach which exploits ontologies and ontology-based category utility for author name disambiguation. Author name disambiguation determines the correct author from various candidate authors in the populated author ontology. Candidate authors are evaluated using proposed ontology-based category utility to resolve disambiguation. Ontology-based category utility has been proposed to exploit semantic information in ontology for semantic analysis for disambiguation. The ontology-based category utility increases the number of disambiguation by about 10% compared with that of category utility, and increases the overall amount of accuracy by around 98%.
Using Agile Practices to Solve Global Software Development Problems -- A Case Study

Although challenges in Global Software Development (GSD) are now well known, companies are still looking for workable solutions to issues relating to poor communication, lack of control, low staff morale, and ambiguous requirements, all of which are prevalent in distributed development settings. Solutions offered are often difficult to implement, are disparate, and can be theoretical rather than practical. We conducted a case study with a GSD company, currently using a plan driven approach to development, and uncovered many GSD problems. We mapped each problem to solutions known to work in Agile development settings. Although work has been undertaken in Agile for GSD, we look to the literature to identify practices that specifically address challenges for organisations already engaged in GSD, who are open to moving from a plan-driven/waterfall development process to a more agile approach. Many challenges are addressed by Agile practices, for example requirements ambiguity, need for autonomy, or unpredictable customer expectations. We hypothesise that many GSD problems can be solved by organisations taking an agile approach to their development.
Pseudo inputs for pairwise learning with Gaussian processes

We consider learning and prediction of pairwise comparisons between instances. The problem is motivated from a perceptual view point, where pairwise comparisons serve as an effective and extensively used paradigm. A state-of-the-art method for modeling pairwise data in high dimensional domains is based on a classical pairwise probit likelihood imposed with a Gaussian process prior. While extremely flexible, this non-parametric method struggles with an inconvenient O(n3) scaling in terms of the n input instances which limits the method only to smaller problems. To overcome this, we derive a specific sparse extension of the classical pairwise likelihood using the pseudo-input formulation. The behavior of the proposed extension is demonstrated on a toy example and on two real-world data sets which outlines the potential gain and pitfalls of the approach. Finally, we discuss the relation to other similar approximations that have been applied in standard Gaussian process regression and classification problems such as FI(T)C and PI(T)C.
HbVar: A relational database of human hemoglobin variants and thalassemia mutations at the globin gene server

We have constructed a relational database of hemoglobin variants and thalassemia mutations, called HbVar, which can be accessed on the web at http://globin.cse. psu.edu. Extensive information is recorded for each variant and mutation, including a description of the variant and associated pathology, hematology, electrophoretic mobility, methods of isolation, stability information, ethnic occurrence, structure studies, functional studies, and references. The initial information was derived from books by Dr. Titus Huisman and colleagues [Huisman et al., 1996, 1997, 1998]. The current database is updated regularly with the addition of new data and corrections to previous data. Queries can be formulated based on fields in the database. Tables of common categories of variants, such as all those involving the alpha1‐globin gene (HBA1) or all those that result in high oxygen affinity, are maintained by automated queries on the database. Users can formulate more precise queries, such as identifying “all beta‐globin variants associated with instability and found in Scottish populations. ” This new database should be useful for clinical diagnosis as well as in fundamental studies of hemoglobin biochemistry, globin gene regulation, and human sequence variation at these loci. Hum Mutat 19:225–233, 2002. © 2002 Wiley‐Liss, Inc.
On Progression and Query Evaluation in First-Order Knowledge Bases with Function Symbols

In a seminal paper, Lin and Reiter introduced the notion of progression of basic action theories. Unfortunately, progression is second-order in general. Recently, Liu and Lakemeyer improve on earlier results and show that for the local-effect and normal actions case, progression is computable but may lead to an exponential blow-up. Nevertheless, they show that for certain kinds of expressive first-order knowledge bases with disjunctive information, called proper, it is efficient. However, answering queries about the resulting state is still undecidable. In this paper, we continue this line of research and extend proper KBs to include functions. We prove that their progression wrt local-effect, normal actions, and range-restricted theories, is first-order definable and efficiently computable. We then provide a new logically sound and complete decision procedure for certain kinds of queries.
Active segmentation for robotics

The semantic robots of the immediate future are robots that will be able to find and recognize objects in any environment. They need the capability of segmenting objects in their visual field. In this paper, we propose a novel approach to segmentation based on the operation of fixation by an active observer. Our approach is different from current approaches: while existing works attempt to segment the whole scene at once into many areas, we segment only one image region, specifically the one containing the fixation point. Furthermore, our solution integrates monocular cues (color, texture) with binocular cues (stereo disparities and optical flow). Experiments with real imagery collected by our active robot and from the known databases [1] demonstrate the promise of the approach.
Who Blogs What: Understanding Behavior, Impact and Types of Bloggers

We investigated bloggers’ publishing patterns by focusing on the topics that their posts cover. Applying clustering algorithms on the dataset from a blog website of 370,000 posts from 2,275 blogs, we identified two types of bloggers: specialists and generalists. Then we compared their respective contributions to the blog osphere in terms of productivity and buzz-factor. Our analysis suggests that specialists generally have a higher impact than generalists. It also reveals that among specialists, only a small fraction create a large “buzz” or produce a voluminous output.
Effect of age and sex on retinal layer thickness and volume in normal eyes

AbstractThe aim of the study was to evaluate the effect of sex and age on the thickness of the retinal layer in normal eyes using spectral-domain optical coherence tomography (SD-OCT). Fifty healthy subjects between the ages of 20 and 80 had their retinal layers measured using SD-OCT at Seoul St. Mary's Hospital. Mean thickness and volume were measured for 9 retinal layers in the fovea, the pericentral ring, and the peripheral ring. The differences of sex- and age-related thickness and volume in each retinal layer were analyzed.The retinal nerve fiber layer (RNFL), ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer (INL), and outer plexiform layer (OPL) were thinnest in the fovea area, whereas the outer nuclear layer (ONL), photoreceptor layer (PHL), and retinal pigment epithelium (RPE) were thickest at similar locations. Mean thickness of the RNFL, GCL, IPL, and OPL was significantly greater in men than women. However, mean thickness of the ONL was greater in women than in men. When compared between patients < 30 years and > 60 years of age, the thickness and volume of peripheral RNFL, GCL, and pericentral and peripheral IPL were significantly larger in the younger group than the older group. Conversely, the thickness and volume of foveal INL and IR were larger in the older group than in the younger group.The thickness and volume of the retinal layer in normal eyes significantly vary depending on age and sex. These results should be considered when evaluating layer analysis in retinal disease.