OpenRaiser/Intern-Atlas huggingface dataset:
Optimal Multicasting in Hybrid RF/FSO DTNs
Venue: arXiv cs.NI 2016
Year: 2016

The multi-copy routing paradigm in Delay Tolerant Networks (DTNs) implies that increasing contact bandwidth leads to a decrease in data delivery delay and an improvement in throughput. With Hybrid Radio Frequency/Free Space Optical (RF/FSO) PHY layers, the high data rate FSO links can be used to increase the contact bandwidth. However, due to the highly directional nature of FSO links, broadcasting is difficult. A naive broadcast strategy where the beam divergence angle is increased to include many nodes in the broadcast set results in low data rate, and does not always result in the minimum achievable delay. In this work we develop an optimal multicast algorithm for hybrid RF/FSO networks. We show that the problem is an abstraction of the minimum weight set cover problem which is known to be NP-hard. A computationally cheap greedy local optimum heuristic is proposed. A comprehensive evaluation using delay, throughput and computation time as metrics is performed using various solutions. These extensive evaluations show that our solution outperforms both naive broadcast and multiple unicast, taking 95% less time as compared to the exact algorithm, while providing comparable performance.
Science Concierge: A fast content-based recommendation system for scientific publications
Venue: arXiv cs.IR 2016
Year: 2016

Finding relevant publications is important for scientists who have to cope with exponentially increasing numbers of scholarly material. Algorithms can help with this task as they help for music, movie, and product recommendations. However, we know little about the performance of these algorithms with scholarly material. Here, we develop an algorithm, and an accompanying Python library, that implements a recommendation system based on the content of articles. Design principles are to adapt to new content, provide near-real time suggestions, and be open source. We tested the library on 15K posters from the Society of Neuroscience Conference 2015. Human curated topics are used to cross validate parameters in the algorithm and produce a similarity metric that maximally correlates with human judgments. We show that our algorithm significantly outperformed suggestions based on keywords. The work presented here promises to make the exploration of scholarly material faster and more accurate.
Discovering items with potential popularity on social media
Venue: arXiv cs.IR 2016
Year: 2016

Predicting the future popularity of online content is highly important in many applications. Preferential attachment phenomena is encountered in scale free networks.Under it's influece popular items get more popular thereby resulting in long tailed distribution problem. Consequently, new items which can be popular (potential ones), are suppressed by the already popular items. This paper proposes a novel model which is able to identify potential items. It identifies the potentially popular items by considering the number of links or ratings it has recieved in recent past along with it's popularity decay. For obtaining an effecient model we consider only temporal features of the content, avoiding the cost of extracting other features. We have found that people follow recent behaviours of their peers. In presence of fit or quality items already popular items lose it's popularity. Prediction accuracy is measured on three industrial datasets namely Movielens, Netflix and Facebook wall post. Experimental results show that compare to state-of-the-art model our model have better prediction accuracy.
A Fast Incremental BSP Tree Archive for Non-dominated Points
Venue: arXiv cs.DS 2016
Year: 2016

Maintaining an archive of all non-dominated points is a standard task in multi-objective optimization. Sometimes it is sufficient to store all evaluated points and to obtain the non-dominated subset in a post-processing step. Alternatively the non-dominated set can be updated on the fly. While keeping track of many non-dominated points efficiently is easy for two objectives, we propose an efficient algorithm based on a binary space partitioning (BSP) tree for the general case of three or more objectives. Our analysis and our empirical results demonstrate the superiority of the method over the brute-force baseline method, as well as graceful scaling to large numbers of objectives.
Accurate and scalable social recommendation using mixed-membership stochastic block models
Venue: arXiv cs.SI 2016
Year: 2016

With ever-increasing amounts of online information available, modeling and predicting individual preferences-for books or articles, for example-is becoming more and more important. Good predictions enable us to improve advice to users, and obtain a better understanding of the socio-psychological processes that determine those preferences. We have developed a collaborative filtering model, with an associated scalable algorithm, that makes accurate predictions of individuals' preferences. Our approach is based on the explicit assumption that there are groups of individuals and of items, and that the preferences of an individual for an item are determined only by their group memberships. Importantly, we allow each individual and each item to belong simultaneously to mixtures of different groups and, unlike many popular approaches, such as matrix factorization, we do not assume implicitly or explicitly that individuals in each group prefer items in a single group of items. The resulting overlapping groups and the predicted preferences can be inferred with a expectation-maximization algorithm whose running time scales linearly (per iteration). Our approach enables us to predict individual preferences in large datasets, and is considerably more accurate than the current algorithms for such large datasets.
Eilenberg--Moore Monoids and Backtracking Monad Transformers
Venue: arXiv cs.PL 2016
Year: 2016

We develop an algebraic underpinning of backtracking monad transformers in the general setting of monoidal categories. As our main technical device, we introduce Eilenberg--Moore monoids, which combine monoids with algebras for strong monads. We show that Eilenberg--Moore monoids coincide with algebras for the list monad transformer ('done right') known from Haskell libraries. From this, we obtain a number of results, including the facts that the list monad transformer is indeed a monad, a transformer, and an instance of the MonadPlus class. Finally, we construct an Eilenberg--Moore monoid of endomorphisms, which, via the codensity monad construction, yields a continuation-based implementation a la Hinze.
Character-Level Neural Translation for Multilingual Media Monitoring in the SUMMA Project
Venue: arXiv cs.CL 2016
Year: 2016

The paper steps outside the comfort-zone of the traditional NLP tasks like automatic speech recognition (ASR) and machine translation (MT) to addresses two novel problems arising in the automated multilingual news monitoring: segmentation of the TV and radio program ASR transcripts into individual stories, and clustering of the individual stories coming from various sources and languages into storylines. Storyline clustering of stories covering the same events is an essential task for inquisitorial media monitoring. We address these two problems jointly by engaging the low-dimensional semantic representation capabilities of the sequence to sequence neural translation models. To enable joint multi-task learning for multilingual neural translation of morphologically rich languages we replace the attention mechanism with the sliding-window mechanism and operate the sequence to sequence neural translation model on the character-level rather than on the word-level. The story segmentation and storyline clustering problem is tackled by examining the low-dimensional vectors produced as a side-product of the neural translation process. The results of this paper describe a novel approach to the automatic story segmentation and storyline clustering problem.
Collaborative Distributed Hypothesis Testing
Venue: arXiv cs.IT 2016
Year: 2016

A collaborative distributed binary decision problem is considered. Two statisticians are required to declare the correct probability measure of two jointly distributed memoryless process, denoted by $X^n=(X_1,\dots,X_n)$ and $Y^n=(Y_1,\dots,Y_n)$, out of two possible probability measures on finite alphabets, namely $P_{XY}$ and $P_{\bar{X}\bar{Y}}$. The marginal samples given by $X^n$ and $Y^n$ are assumed to be available at different locations. The statisticians are allowed to exchange limited amount of data over multiple rounds of interactions, which differs from previous work that deals mainly with unidirectional communication. A single round of interaction is considered before the result is generalized to any finite number of communication rounds. A feasibility result is shown, guaranteeing the feasibility of an error exponent for general hypotheses, through information-theoretic methods. The special case of testing against independence is revisited as being an instance of this result for which also an unfeasibility result is proven. A second special case is studied where zero-rate communication is imposed (data exchanges grow sub-exponentially with $n$) for which it is shown that interaction does not improve asymptotic performance.
A smartphone-based vision simulator
Venue: arXiv cs.HC 2016
Year: 2016

Simulators, as tools that can clearly bring out the effect of impairment, are invaluable in the design and development process of an assistive device. Simulators are vital in meeting high standards of accessibility. Described is our work on a smartphone-based vision simulator for diabetic retinopathy that is economic, portable, flexible and easy-to-use.
A Software Methodology for Compiling Quantum Programs
Venue: arXiv cs.PL 2016
Year: 2016

Quantum computers promise to transform our notions of computation by offering a completely new paradigm. To achieve scalable quantum computation, optimizing compilers and a corresponding software design flow will be essential. We present a software architecture for compiling quantum programs from a high-level language program to hardware-specific instructions. We describe the necessary layers of abstraction and their differences and similarities to classical layers of a computer-aided design flow. For each layer of the stack, we discuss the underlying methods for compilation and optimization. Our software methodology facilitates more rapid innovation among quantum algorithm designers, quantum hardware engineers, and experimentalists. It enables scalable compilation of complex quantum algorithms and can be targeted to any specific quantum hardware implementation.
Partial Sublinear Time Approximation and Inapproximation for Maximum Coverage
Venue: arXiv cs.DS 2016
Year: 2016

We develop a randomized approximation algorithm for the classical maximum coverage problem, which given a list of sets $A_1,A_2,\cdots, A_m$ and integer parameter $k$, select $k$ sets $A_{i_1}, A_{i_2},\cdots, A_{i_k}$ for maximum union $A_{i_1}\cup A_{i_2}\cup\cdots\cup A_{i_k}$. In our algorithm, each input set $A_i$ is a black box that can provide its size $|A_i|$, generate a random element of $A_i$, and answer the membership query $(x\in A_i?)$ in $O(1)$ time. Our algorithm gives $(1-{1\over e})$-approximation for maximum coverage problem in $O(p(m))$ time, which is independent of the sizes of the input sets. No existing $O(p(m)n^{1-ε})$ time $(1-{1\over e})$-approximation algorithm for the maximum coverage has been found for any function $p(m)$ that only depends on the number of sets, where $n=\max(|A_1|,\cdots,| A_m|)$ (the largest size of input sets). The notion of partial sublinear time algorithm is introduced. For a computational problem with input size controlled by two parameters $n$ and $m$, a partial sublinear time algorithm for it runs in a $O(p(m)n^{1-ε})$ time or $O(q(n)m^{1-ε})$ time. The maximum coverage has a partial sublinear time $O(p(m))$ constant factor approximation. On the other hand, we show that the maximum coverage problem has no partial sublinear $O(q(n)m^{1-ε})$ time constant factor approximation algorithm. It separates the partial sublinear time computation from the conventional sublinear time computation by disproving the existence of sublinear time approximation algorithm for the maximum coverage problem.
Learning A Deep $\ell_\infty$ Encoder for Hashing
Venue: arXiv cs.LG 2016
Year: 2016

We investigate the $\ell_\infty$-constrained representation which demonstrates robustness to quantization errors, utilizing the tool of deep learning. Based on the Alternating Direction Method of Multipliers (ADMM), we formulate the original convex minimization problem as a feed-forward neural network, named \textit{Deep $\ell_\infty$ Encoder}, by introducing the novel Bounded Linear Unit (BLU) neuron and modeling the Lagrange multipliers as network biases. Such a structural prior acts as an effective network regularization, and facilitates the model initialization. We then investigate the effective use of the proposed model in the application of hashing, by coupling the proposed encoders under a supervised pairwise loss, to develop a \textit{Deep Siamese $\ell_\infty$ Network}, which can be optimized from end to end. Extensive experiments demonstrate the impressive performances of the proposed model. We also provide an in-depth analysis of its behaviors against the competitors.
Energy Harvesting-Aided Spectrum Sensing and Data Transmission in Heterogeneous Cognitive Radio Sensor Network
Venue: arXiv cs.NI 2016
Year: 2016

The incorporation of Cognitive Radio (CR) and Energy Harvesting (EH) capabilities in wireless sensor networks enables spectrum and energy efficient heterogeneous cognitive radio sensor networks (HCRSNs). The new networking paradigm of HCRSNs consists of EH-enabled spectrum sensors and battery powered data sensors. Spectrum sensors can cooperatively scan the licensed spectrum for available channels, while data sensors monitor an area of interest and transmit sensed data to the sink over those channels. In this work, we propose a resource allocation solution for the HCRSN to achieve the sustainability of spectrum sensors and conserve energy of data sensors. The proposed solution is achieved by two algorithms that operate in tandem, a spectrum sensor scheduling algorithm and a data sensor resource allocation algorithm. The spectrum sensor scheduling algorithm allocates channels to spectrum sensors such that the average detected available time for the channels is maximized, while the EH dynamics are considered and PU transmissions are protected. The data sensor resource allocation algorithm allocates the transmission time, power and channels such that the energy consumption of the data sensors is minimized. Extensive simulation results demonstrate that the energy consumption of the data sensors can be significantly reduced while maintaining the sustainability of the spectrum sensors.
Modular Lattices from a Variation of Construction A over Number Fields
Venue: arXiv cs.IT 2016
Year: 2016

We consider a variation of Construction A of lattices from linear codes based on two classes of number fields, totally real and CM Galois number fields. We propose a generic construction with explicit generator and Gram matrices, then focus on modular and unimodular lattices, obtained in the particular cases of totally real, respectively, imaginary, quadratic fields. Our motivation comes from coding theory, thus some relevant properties of modular lattices, such as minimal norm, theta series, kissing number and secrecy gain are analyzed. Interesting lattices are exhibited.
Optimal DoF of the K-User Broadcast Channel with Delayed and Imperfect Current CSIT
Venue: arXiv cs.IT 2016
Year: 2016

This work studies the optimal Degrees-of-Freedom (DoF) of the $K$-User MISO Broadcast Channel (BC) with delayed Channel-State Information at the Transmitter (CSIT) and with additional current noisy CSIT where the current channel estimation error scales in~$P^{-α}$ for $α\in[0,1]$. This papers establishes for the first time the optimal DoF in this setting thanks to a new transmission scheme which achieves the elusive DoF-optimal combining of the Maddah-Ali and Tse scheme (MAT) introduced in their seminal work in $2010$ with Zero-Forcing (ZF) for an arbitrary number of users. The derived sum DoF takes the surprisingly simple form $(1-α) K/H_K+αK$ where $H_K\triangleq \sum_{k=1}^K \frac{1}{k}$ is the sum-DoF achieved using solely MAT.
Market Share Analysis with Brand Effect
Venue: arXiv cs.GT 2016
Year: 2016

In this paper, we investigate the effect of brand in market competition. Specifically, we propose a variant Hotelling model where companies and customers are represented by points in an Euclidean space, with axes being product features. $N$ companies compete to maximize their own profits by optimally choosing their prices, while each customer in the market, when choosing sellers, considers the sum of product price, discrepancy between product feature and his preference, and a company's brand name, which is modeled by a function of its market area of the form $-β\cdot\text{(Market Area)}^q$, where $β$ captures the brand influence and $q$ captures how market share affects the brand. By varying the parameters $β$ and $q$, we derive existence results of Nash equilibrium and equilibrium market prices and shares. In particular, we prove that pure Nash equilibrium always exists when $q=0$ for markets with either one and two dominating features, and it always exists in a single dominating feature market when market affects brand name linearly, i.e., $q=1$. Moreover, we show that at equilibrium, a company's price is proportional to its market area over the competition intensity with its neighbors, a result that quantitatively reconciles the common belief of a company's pricing power. We also study an interesting "wipe out" phenomenon that only appears when $q>0$, which is similar to the "undercut" phenomenon in the Hotelling model, where companies may suddenly lose the entire market area with a small price increment. Our results offer novel insight into market pricing and positioning under competition with brand effect.
Reading Between the Pixels: Photographic Steganography for Camera Display Messaging
Venue: arXiv cs.CV 2016
Year: 2016

We exploit human color metamers to send light-modulated messages less visible to the human eye, but recoverable by cameras. These messages are a key component to camera-display messaging, such as handheld smartphones capturing information from electronic signage. Each color pixel in the display image is modified by a particular color gradient vector. The challenge is to find the color gradient that maximizes camera response, while minimizing human response. The mismatch in human spectral and camera sensitivity curves creates an opportunity for hidden messaging. Our approach does not require knowledge of these sensitivity curves, instead we employ a data-driven method. We learn an ellipsoidal partitioning of the six-dimensional space of colors and color gradients. This partitioning creates metamer sets defined by the base color at the display pixel and the color gradient direction for message encoding. We sample from the resulting metamer sets to find color steps for each base color to embed a binary message into an arbitrary image with reduced visible artifacts. Unlike previous methods that rely on visually obtrusive intensity modulation, we embed with color so that the message is more hidden. Ordinary displays and cameras are used without the need for expensive LEDs or high speed devices. The primary contribution of this work is a framework to map the pixels in an arbitrary image to a metamer pair for steganographic photo messaging.
Generalising the Discriminative Restricted Boltzmann Machine
Venue: arXiv cs.LG 2016
Year: 2016

We present a novel theoretical result that generalises the Discriminative Restricted Boltzmann Machine (DRBM). While originally the DRBM was defined assuming the {0, 1}-Bernoulli distribution in each of its hidden units, this result makes it possible to derive cost functions for variants of the DRBM that utilise other distributions, including some that are often encountered in the literature. This is illustrated with the Binomial and {-1, +1}-Bernoulli distributions here. We evaluate these two DRBM variants and compare them with the original one on three benchmark datasets, namely the MNIST and USPS digit classification datasets, and the 20 Newsgroups document classification dataset. Results show that each of the three compared models outperforms the remaining two in one of the three datasets, thus indicating that the proposed theoretical generalisation of the DRBM may be valuable in practice.
System for Filtering Messages on Social Media Content
Venue: arXiv cs.SI 2016
Year: 2016

The social networking era has left us with little privacy. The details of the social network users are published on Social Networking sites. Vulnerability has reached new heights due to the overpowering effects of social networking. The sites like Facebook, Twitter are having a huge set of users who publish their files, comments, messages in other users walls. These messages and comments could be of any nature. Even friends could post a comment that would harm a persons integrity. Thus there has to be a system which will monitor the messages and comments that are posted on the walls. If the messages are found to be neutral (does not have any harmful content), then it can be published. If the messages are found to have non-neutral content in them, then these messages would be blocked by the social network manager. The messages that are non-neutral would be of sexual, offensive, hatred, pun intended nature. Thus the social network manager can classify content as neutral and non-neutral and notify the user if there seems to be messages of non-neutral behavior.
Reinterpreting the Transformation Posterior in Probabilistic Image Registration
Venue: arXiv cs.CV 2016
Year: 2016

Probabilistic image registration methods estimate the posterior distribution of transformation. The conventional way of interpreting the transformation posterior is to use the mode as the most likely transformation and assign its corresponding intensity to the registered voxel. Meanwhile, summary statistics of the posterior are employed to evaluate the registration uncertainty, that is the trustworthiness of the registered image. Despite the wide acceptance, this convention has never been justified. In this paper, based on illustrative examples, we question the correctness and usefulness of conventional methods. In order to faithfully translate the transformation posterior, we propose to encode the variability of values into a novel data type called ensemble fields. Ensemble fields can serve as a complement to the registered image and a foundation for developing advanced methods to characterize the uncertainty in registration-based tasks. We demonstrate the potential of ensemble fields by pilot examples
Performance analysis of the Kahan-enhanced scalar product on current multi- and manycore processors
Venue: arXiv cs.PF 2016
Year: 2016

We investigate the performance characteristics of a numerically enhanced scalar product (dot) kernel loop that uses the Kahan algorithm to compensate for numerical errors, and describe efficient SIMD-vectorized implementations on recent multi- and manycore processors. Using low-level instruction analysis and the execution-cache-memory (ECM) performance model we pinpoint the relevant performance bottlenecks for single-core and thread-parallel execution, and predict performance and saturation behavior. We show that the Kahan-enhanced scalar product comes at almost no additional cost compared to the naive (non-Kahan) scalar product if appropriate low-level optimizations, notably SIMD vectorization and unrolling, are applied. The ECM model is extended appropriately to accommodate not only modern Intel multicore chips but also the Intel Xeon Phi "Knights Corner" coprocessor and an IBM POWER8 CPU. This allows us to discuss the impact of processor features on the performance across four modern architectures that are relevant for high performance computing.