OpenRaiser/Intern-Atlas huggingface dataset:
Evaluation of Joint Multi-Instance Multi-Label Learning For Breast Cancer Diagnosis
Venue: arXiv cs.CV 2015
Year: 2015

Multi-instance multi-label (MIML) learning is a challenging problem in many aspects. Such learning approaches might be useful for many medical diagnosis applications including breast cancer detection and classification. In this study subset of digiPATH dataset (whole slide digital breast cancer histopathology images) are used for training and evaluation of six state-of-the-art MIML methods. At the end, performance comparison of these approaches are given by means of effective evaluation metrics. It is shown that MIML-kNN achieve the best performance that is %65.3 average precision, where most of other methods attain acceptable results as well.
Mining Interesting Trivia for Entities from Wikipedia
Venue: arXiv cs.IR 2015
Year: 2015

Trivia is any fact about an entity, which is interesting due to any of the following characteristics - unusualness, uniqueness, unexpectedness or weirdness. Such interesting facts are provided in 'Did You Know?' section at many places. Although trivia are facts of little importance to be known, but we have presented their usage in user engagement purpose. Such fun facts generally spark intrigue and draws user to engage more with the entity, thereby promoting repeated engagement. The thesis has cited some case studies, which show the significant impact of using trivia for increasing user engagement or for wide publicity of the product/service. In this thesis, we propose a novel approach for mining entity trivia from their Wikipedia pages. Given an entity, our system extracts relevant sentences from its Wikipedia page and produces a list of sentences ranked based on their interestingness as trivia. At the heart of our system lies an interestingness ranker which learns the notion of interestingness, through a rich set of domain-independent linguistic and entity based features. Our ranking model is trained by leveraging existing user-generated trivia data available on the Web instead of creating new labeled data for movie domain. For other domains like sports, celebrities, countries etc. labeled data would have to be created as described in thesis. We evaluated our system on movies domain and celebrity domain, and observed that the system performs significantly better than the defined baselines. A thorough qualitative analysis of the results revealed that our engineered rich set of features indeed help in surfacing interesting trivia in the top ranks.
Group-blind detection with very large antenna arrays in the presence of pilot contamination
Venue: arXiv cs.IT 2015
Year: 2015

Massive MIMO is, in general, severely affected by pilot contamination. As opposed to traditional detectors, we propose a group-blind detector that takes into account the presence of pilot contamination. While sticking to the traditional structure of the training phase, where orthogonal pilot sequences are reused, we use the excess antennas at each base station to partially remove interference during the uplink data transmission phase. We analytically derive the asymptotic SINR achievable with group-blind detection, and confirm our findings by simulations. We show, in particular, that in an interference-limited scenario with one dominant interfering cell, the SINR can be doubled compared to non-group-blind detection.
Fast detection of multiple objects in traffic scenes with a common detection framework
Venue: arXiv cs.CV 2015
Year: 2015

Traffic scene perception (TSP) aims to real-time extract accurate on-road environment information, which in- volves three phases: detection of objects of interest, recognition of detected objects, and tracking of objects in motion. Since recognition and tracking often rely on the results from detection, the ability to detect objects of interest effectively plays a crucial role in TSP. In this paper, we focus on three important classes of objects: traffic signs, cars, and cyclists. We propose to detect all the three important objects in a single learning based detection framework. The proposed framework consists of a dense feature extractor and detectors of three important classes. Once the dense features have been extracted, these features are shared with all detectors. The advantage of using one common framework is that the detection speed is much faster, since all dense features need only to be evaluated once in the testing phase. In contrast, most previous works have designed specific detectors using different features for each of these objects. To enhance the feature robustness to noises and image deformations, we introduce spatially pooled features as a part of aggregated channel features. In order to further improve the generalization performance, we propose an object subcategorization method as a means of capturing intra-class variation of objects. We experimentally demonstrate the effectiveness and efficiency of the proposed framework in three detection applications: traffic sign detection, car detection, and cyclist detection. The proposed framework achieves the competitive performance with state-of- the-art approaches on several benchmark datasets.
Treetopes and their Graphs
Venue: arXiv cs.CG 2015
Year: 2015

We define treetopes, a generalization of the three-dimensional roofless polyhedra (Halin graphs) to arbitrary dimensions. Like roofless polyhedra, treetopes have a designated base facet such that every face of dimension greater than one intersects the base in more than one point. We prove an equivalent characterization of the 4-treetopes using the concept of clustered planarity from graph drawing, and we use this characterization to recognize the graphs of 4-treetopes in polynomial time. This result provides one of the first classes of 4-polytopes, other than pyramids and stacked polytopes, that can be recognized efficiently from their graphs.
Evaluating m-learning in Saudi Arabian higher education: a case study
Venue: arXiv cs.CY 2015
Year: 2015

Nowadays, mobile devices have become increasingly a part of education for those who study or teach at the university level and school levels. The support of electronic learning (elearning) is essential to making mobile learning (m-learning) successful. This paper presents a study that applies mlearning to a course at Qassim University, where 100 students attended during the academic year 2014, including summer courses. The study aims to demonstrate that m-learning provides students with the ability to engage in reflective thinking, to share information among peers and to facilitate the construction of social knowledge. 90 student questionnaires were filled correctly, remaining 10 had various anomalies and thus were not considered. The study also aims to demonstrate that learning provides methods for education strategies to be easily and rapidly applied. Such strategies include team work, time management, etc. In order to judge the feasibility of applying m-learning widely, a questionnaire was developed. The results indicate that m-learning helps make the process of education more convenient than in the past. A major disadvantage of applying m-learning is inadequate wireless network bandwidth.
Modeling and Analysis of SiNW BioFET as Molecular Antenna for Bio-Cyber Interfaces towards the Internet of Bio-NanoThings
Venue: arXiv cs.ET 2015
Year: 2015

Seamless connection of molecular nanonetworks to macroscale cyber networks is envisioned to enable the Internet of Bio-NanoThings, which promises for cutting-edge applications, especially in the medical domain. The connection requires the development of an interface between the biochemical domain of molecular nanonetworks and the electrical domain of conventional electromagnetic networks. To this aim, in this paper, we propose to exploit field effect transistor based biosensors (bioFETs) to devise a molecular antenna capable of transducing molecular messages into electrical signals. In particular, focusing on the use of SiNW FET-based biosensors as molecular antennas, we develop deterministic and noise models for the antenna operation to provide a theoretical framework for the optimization of the device from communication perspective. We numerically evaluate the performance of the antenna in terms of the Signal-to-Noise Ratio (SNR) at the electrical output.
Low-cost Sensor Glove with Force Feedback for Learning from Demonstrations using Probabilistic Trajectory Representations
Venue: arXiv cs.RO 2015
Year: 2015

Sensor gloves are popular input devices for a large variety of applications including health monitoring, control of music instruments, learning sign language, dexterous computer interfaces, and tele-operating robot hands. Many commercial products as well as low-cost open source projects have been developed. We discuss here how low-cost (approx. 250 EUROs) sensor gloves with force feedback can be build, provide an open source software interface for Matlab and present first results in learning object manipulation skills through imitation learning on the humanoid robot iCub.
Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark
Venue: arXiv cs.AI 2015
Year: 2015

Much of the world's data is streaming, time-series data, where anomalies give significant information in critical situations; examples abound in domains such as finance, IT, security, medical, and energy. Yet detecting anomalies in streaming data is a difficult task, requiring detectors to process data in real-time, not batches, and learn while simultaneously making predictions. There are no benchmarks to adequately test and score the efficacy of real-time anomaly detectors. Here we propose the Numenta Anomaly Benchmark (NAB), which attempts to provide a controlled and repeatable environment of open-source tools to test and measure anomaly detection algorithms on streaming data. The perfect detector would detect all anomalies as soon as possible, trigger no false alarms, work with real-world time-series data across a variety of domains, and automatically adapt to changing statistics. Rewarding these characteristics is formalized in NAB, using a scoring algorithm designed for streaming data. NAB evaluates detectors on a benchmark dataset with labeled, real-world time-series data. We present these components, and give results and analyses for several open source, commercially-used algorithms. The goal for NAB is to provide a standard, open source framework with which the research community can compare and evaluate different algorithms for detecting anomalies in streaming data.
Layered Heaps Beating Standard and Fibonacci Heaps in Practice
Venue: arXiv cs.DS 2015
Year: 2015

We consider the classic problem of designing heaps. Standard binary heaps run faster in practice than Fibonacci heaps but have worse time guarantees. Here we present a new type of heap, a layered heap, that runs faster in practice than both standard binary and Fibonacci heaps, but has asymptotic insert times better than that of binary heaps. Our heap is defined recursively and maximum run time speed up occurs when a recursion depth of 1 is used, i.e. a heap of heaps.
Asymptotic Logical Uncertainty and The Benford Test
Venue: arXiv cs.LG 2015
Year: 2015

We give an algorithm A which assigns probabilities to logical sentences. For any simple infinite sequence of sentences whose truth-values appear indistinguishable from a biased coin that outputs "true" with probability p, we have that the sequence of probabilities that A assigns to these sentences converges to p.
Selling Two Goods Optimally
Venue: arXiv cs.GT 2015
Year: 2015

We provide sufficient conditions for revenue maximization in a two-good monopoly where the buyer's values for the items come from independent (but not necessarily identical) distributions over bounded intervals. Under certain distributional assumptions, we give exact, closed-form formulas for the prices and allocation rule of the optimal selling mechanism. As a side result we give the first example of an optimal mechanism in an i.i.d. setting over a support of the form $[0,b]$ which is not deterministic. Since our framework is based on duality techniques, we were also able to demonstrate how slightly relaxed versions of it can still be used to design mechanisms that have very good approximation ratios with respect to the optimal revenue, through a "convexification" process.
Repeat-Accumulate Codes for Reconciliation in Continuous Variable Quantum Key Distribution
Venue: arXiv cs.IT 2015
Year: 2015

This paper investigates the design of low-complexity error correction codes for the verification step in continuous variable quantum key distribution (CVQKD) systems. We design new coding schemes based on quasi-cyclic repeat-accumulate codes which demonstrate good performances for CVQKD reconciliation.
On the line-symmetry of self-motions of linear pentapods
Venue: arXiv cs.RO 2015
Year: 2015

We show that all self-motions of pentapods with linear platform of Type 1 and Type 2 can be generated by line-symmetric motions. Thus this paper closes a gap between the more than 100 year old works of Duporcq and Borel and the extensive study of line-symmetric motions done by Krames in the 1930's. As a consequence we also get a new solution set for the Borel Bricard problem. Moreover we discuss the reality of self-motions and give a sufficient condition for the design of linear pentapods of Type 1 and Type 2, which have a self-motion free workspace.
UAVs using Bayesian Optimization to Locate WiFi Devices
Venue: arXiv cs.AI 2015
Year: 2015

We address the problem of localizing non-collaborative WiFi devices in a large region. Our main motive is to localize humans by localizing their WiFi devices, e.g. during search-and-rescue operations after a natural disaster. We use an active sensing approach that relies on Unmanned Aerial Vehicles (UAVs) to collect signal-strength measurements at informative locations. The problem is challenging since the measurement is received at arbitrary times and they are received only when the UAV is in close proximity to the device. For these reasons, it is extremely important to make prudent decision with very few measurements. We use the Bayesian optimization approach based on Gaussian process (GP) regression. This approach works well for our application since GPs give reliable predictions with very few measurements while Bayesian optimization makes a judicious trade-off between exploration and exploitation. In field experiments conducted over a region of 1000 $\times$ 1000 $m^2$, we show that our approach reduces the search area to less than 100 meters around the WiFi device within 5 minutes only. Overall, our approach localizes the device in less than 15 minutes with an error of less than 20 meters.
Future Computing Platforms for Science in a Power Constrained Era
Venue: arXiv cs.DC 2015
Year: 2015

Power consumption will be a key constraint on the future growth of Distributed High Throughput Computing (DHTC) as used by High Energy Physics (HEP). This makes performance-per-watt a crucial metric for selecting cost-efficient computing solutions. For this paper, we have done a wide survey of current and emerging architectures becoming available on the market including x86-64 variants, ARMv7 32-bit, ARMv8 64-bit, Many-Core and GPU solutions, as well as newer System-on-Chip (SoC) solutions. We compare performance and energy efficiency using an evolving set of standardized HEP-related benchmarks and power measurement techniques we have been developing. We evaluate the potential for use of such computing solutions in the context of DHTC systems, such as the Worldwide LHC Computing Grid (WLCG).
Structure-Constrained Basis Pursuit for Compressed Sensing
Venue: arXiv cs.IT 2015
Year: 2015

In compressive sensing (CS) theory, as the number of samples is decreased below a minimum threshold, the average error of the recovery increases. Sufficient sampling is either required for quality reconstruction or the error is resignedly accepted. However, most CS work has not taken advantage of the inherent structure in a variety of signals relevant to engineering applications. Hence, this paper proposes a new method of recovery built on basis pursuit (BP), called Structure-Constrained Basis Pursuit (SCBP), that constrains signals based on known structure rather than through extra sampling. Preliminary assessments of this method on TIMIT recordings of the speech phoneme /aa/ show a substantial decrease in error: with a fixed 5:1 compression ratio the average recovery error is 23.8% lower versus vanilla BP. More significantly, this method can be applied to any CS application that samples structured data, such as FSK waveforms, speech, and tones. In these cases, higher compression ratios can be reached with comparable error.
Information Coverage Maximization in Social Networks
Venue: arXiv cs.SI 2015
Year: 2015

Social networks, due to their popularity, have been studied extensively these years. A rich body of these studies is related to influence maximization, which aims to select a set of seed nodes for maximizing the expected number of active nodes at the end of the process. However, the set of active nodes can not fully represent the true coverage of information propagation. A node may be informed of the information when any of its neighbours become active and try to activate it, though this node (namely informed node) is still inactive. Therefore, we need to consider both active nodes and informed nodes that are aware of the information when we study the coverage of information propagation in a network. Along this line, in this paper we propose a new problem called Information Coverage Maximization that aims to maximize the expected number of both active nodes and informed ones. After we prove that this problem is NP-hard and submodular in the independent cascade model and the linear threshold model, we design two algorithms to solve it. Extensive experiments on three real-world data sets demonstrate the performance of the proposed algorithms.
Quality-Aware Popularity Based Bandwidth Allocation for Scalable Video Broadcast over Wireless Access Networks
Venue: arXiv cs.NI 2015
Year: 2015

Video broadcast/multicast over wireless access networks is an attractive research issue in the field of wireless communication. With the rapid improvement of various wireless network technologies, it is now possible to provide high quality video transmission over wireless networks. The high quality video streams need higher bandwidth. Hence, during the video transmission through wireless networks, it is very important to make the best utilization of the limited bandwidth. Therefore, when many broadcasting video sessions are active, the bandwidth per video session can be allocated based on popularity of the video sessions (programs). Instead of allocating equal bandwidth to each of them, our proposed scheme allocates bandwidth per broadcasting video session based on popularity of the video program. When the system bandwidth is not sufficient to allocate the demanded bandwidth for all the active video sessions, our proposed scheme efficiently allocates the total system bandwidth among all the scalable active video sessions in such a way that higher bandwidth is allocated to higher popularity one. Using the mathematical and simulation analyses, we show that the proposed scheme maximizes the average user satisfaction level and achieves the best utilization of bandwidth. The simulation results indicate that a large number of subscribers can receive a significantly improved quality of video. To improve the video quality for large number of subscribers, the only tradeoff is that a very few subscribers receive slightly degraded video quality.
Canonical Paths for MCMC: from Art to Science
Venue: arXiv cs.DS 2015
Year: 2015

Markov Chain Monte Carlo (MCMC) method is a widely used algorithm design scheme with many applications. To make efficient use of this method, the key step is to prove that the Markov chain is rapid mixing. Canonical paths is one of the two main tools to prove rapid mixing. However, there are much fewer success examples comparing to coupling, the other main tool. The main reason is that there is no systematic approach or general recipe to design canonical paths. Building up on a previous exploration by McQuillan, we develop a general theory to design canonical paths for MCMC: We reduce the task of designing canonical paths to solving a set of linear equations, which can be automatically done even by a machine. Making use of this general approach, we obtain fully polynomial-time randomized approximation schemes (FPRAS) for counting the number of $b$-matching with $b\leq 7$ and $b$-edge-cover with $b\leq 2$. They are natural generalizations of matchings and edge covers for graphs. No polynomial time approximation was previously known for these problems.
Eigenfunctions of Underspread Linear Communication Systems
Venue: arXiv cs.IT 2015
Year: 2015

In this paper we show that the eigenfunctions can be found exactly for systems whose delay-Doppler spread function is concentrated along a straight line and they can be found in approximate sense for systems having a spread function maximally concentrated in regions of the Doppler-delay plane whose area is smaller than one. The interesting results are that: i) the instantaneous frequency of the eigenfunctions is dictated by the contour level of the time-varying transfer function; ii) the eigenvalues are restricted between the minimum and maximum value of the system time-varying transfer function, but not all values are possible, as the system exhibits an inherent quantization.
Characterization of Vehicle Behavior with Information Theory
Venue: arXiv cs.IT 2015
Year: 2015

This work proposes the use of Information Theory for the characterization of vehicles behavior through their velocities. Three public data sets were used: i.Mobile Century data set collected on Highway I-880, near Union City, California; ii.Borlänge GPS data set collected in the Swedish city of Borlänge; and iii.Beijing taxicabs data set collected in Beijing, China, where each vehicle speed is stored as a time series. The Bandt-Pompe methodology combined with the Complexity-Entropy plane were used to identify different regimes and behaviors. The global velocity is compatible with a correlated noise with f^{-k} Power Spectrum with k >= 0. With this we identify traffic behaviors as, for instance, random velocities (k aprox. 0) when there is congestion, and more correlated velocities (k aprox. 3) in the presence of free traffic flow.
Debunking in a World of Tribes
Venue: arXiv cs.CY 2015
Year: 2015

Recently a simple military exercise on the Internet was perceived as the beginning of a new civil war in the US. Social media aggregate people around common interests eliciting a collective framing of narratives and worldviews. However, the wide availability of user-provided content and the direct path between producers and consumers of information often foster confusion about causations, encouraging mistrust, rumors, and even conspiracy thinking. In order to contrast such a trend attempts to \textit{debunk} are often undertaken. Here, we examine the effectiveness of debunking through a quantitative analysis of 54 million users over a time span of five years (Jan 2010, Dec 2014). In particular, we compare how users interact with proven (scientific) and unsubstantiated (conspiracy-like) information on Facebook in the US. Our findings confirm the existence of echo chambers where users interact primarily with either conspiracy-like or scientific pages. Both groups interact similarly with the information within their echo chamber. We examine 47,780 debunking posts and find that attempts at debunking are largely ineffective. For one, only a small fraction of usual consumers of unsubstantiated information interact with the posts. Furthermore, we show that those few are often the most committed conspiracy users and rather than internalizing debunking information, they often react to it negatively. Indeed, after interacting with debunking posts, users retain, or even increase, their engagement within the conspiracy echo chamber.