OpenRaiser/Intern-Atlas huggingface dataset:
Modern Physiognomy: An Investigation on Predicting Personality Traits and Intelligence from the Human Face
Venue: arXiv cs.CV 2016
Year: 2016

The human behavior of evaluating other individuals with respect to their personality traits and intelligence by evaluating their faces plays a crucial role in human relations. These trait judgments might influence important social outcomes in our lives such as elections and court sentences. Previous studies have reported that human can make valid inferences for at least four personality traits. In addition, some studies have demonstrated that facial trait evaluation can be learned using machine learning methods accurately. In this work, we experimentally explore whether self-reported personality traits and intelligence can be predicted reliably from a facial image. More specifically, the prediction problem is separately cast in two parts: a classification task and a regression task. A facial structural feature is constructed from the relations among facial salient points, and an appearance feature is built by five texture descriptors. In addition, a minutia-based fingerprint feature from a fingerprint image is also explored. The classification results show that the personality traits "Rule-consciousness" and "Vigilance" can be predicted reliably, and that the traits of females can be predicted more accurately than those of male. However, the regression experiments show that it is difficult to predict scores for individual personality traits and intelligence. The residual plots and the correlation results indicate no evident linear correlation between the measured scores and the predicted scores. Both the classification and the regression results reveal that "Rule-consciousness" and "Tension" can be reliably predicted from the facial features, while "Social boldness" gets the worst prediction results. The experiments results show that it is difficult to predict intelligence from either the facial features or the fingerprint feature, a finding that is in agreement with previous studies.
Can Online Emotions Predict the Stock Market in China?
Venue: arXiv cs.SI 2016
Year: 2016

Whether the online social media, like Twitter or its variant Weibo, can be a convincing proxy to predict the stock market has been debated for years, especially for China. However, as the traditional theory in behavioral finance states, the individual emotions can influence decision-making of investors, so it is reasonable to further explore this controversial topic from the perspective of online emotions, which is richly carried by massive tweets in social media. Surprisingly, through thorough study on over 10 million stock-relevant tweets from Weibo, both correlation analysis and causality test show that five attributes of the stock market in China can be competently predicted by various online emotions, like disgust, joy, sadness and fear. Specifically, the presented model significantly outperforms the baseline solutions on predicting five attributes of the stock market under the $K$-means discretization. We also employ this model in the scenario of realistic online application and its performance is further testified.
Pattern Matching and Consensus Problems on Weighted Sequences and Profiles
Venue: arXiv cs.DS 2016
Year: 2016

We study pattern matching problems on two major representations of uncertain sequences used in molecular biology: weighted sequences (also known as position weight matrices, PWM) and profiles (i.e., scoring matrices). In the simple version, in which only the pattern or only the text is uncertain, we obtain efficient algorithms with theoretically-provable running times using a variation of the lookahead scoring technique. We also consider a general variant of the pattern matching problems in which both the pattern and the text are uncertain. Central to our solution is a special case where the sequences have equal length, called the consensus problem. We propose algorithms for the consensus problem parameterized by the number of strings that match one of the sequences. As our basic approach, a careful adaptation of the classic meet-in-the-middle algorithm for the knapsack problem is used. On the lower bound side, we prove that our dependence on the parameter is optimal up to lower-order terms conditioned on the optimality of the original algorithm for the knapsack problem.
The Big Match in Small Space
Venue: arXiv cs.GT 2016
Year: 2016

In this paper we study how to play (stochastic) games optimally using little space. We focus on repeated games with absorbing states, a type of two-player, zero-sum concurrent mean-payoff games. The prototypical example of these games is the well known Big Match of Gillete (1957). These games may not allow optimal strategies but they always have ε-optimal strategies. In this paper we design ε-optimal strategies for Player 1 in these games that use only O(log log T ) space. Furthermore, we construct strategies for Player 1 that use space s(T), for an arbitrary small unbounded non-decreasing function s, and which guarantee an ε-optimal value for Player 1 in the limit superior sense. The previously known strategies use space Ω(logT) and it was known that no strategy can use constant space if it is ε-optimal even in the limit superior sense. We also give a complementary lower bound. Furthermore, we also show that no Markov strategy, even extended with finite memory, can ensure value greater than 0 in the Big Match, answering a question posed by Abraham Neyman.
On the Construction of Jointly Superregular Lower Triangular Toeplitz Matrices
Venue: arXiv cs.IT 2016
Year: 2016

Superregular matrices have the property that all of their submatrices, which can be full rank are so. Lower triangular superregular matrices are useful for e.g., maximum distance separable convolutional codes as well as for (sequential) network codes. In this work, we provide an explicit design for all superregular lower triangular Toeplitz matrices in GF(2^p) for the case of matrices with dimensions less than or equal to 5 x 5. For higher dimensional matrices, we present a greedy algorithm that finds a solution provided the field size is sufficiently high. We also introduce the notions of jointly superregular and product preserving jointly superregular matrices, and extend our explicit constructions of superregular matrices to these cases. Jointly superregular matrices are necessary to achieve optimal decoding capabilities for the case of codes with a rate lower than 1/2 , and the product preserving property is necessary for optimal decoding capabilities in network recoding.
Tournament selection in zeroth-level classifier systems based on average reward reinforcement learning
Venue: arXiv cs.AI 2016
Year: 2016

As a genetics-based machine learning technique, zeroth-level classifier system (ZCS) is based on a discounted reward reinforcement learning algorithm, bucket-brigade algorithm, which optimizes the discounted total reward received by an agent but is not suitable for all multi-step problems, especially large-size ones. There are some undiscounted reinforcement learning methods available, such as R-learning, which optimize the average reward per time step. In this paper, R-learning is used as the reinforcement learning employed by ZCS, to replace its discounted reward reinforcement learning approach, and tournament selection is used to replace roulette wheel selection in ZCS. The modification results in classifier systems that can support long action chains, and thus is able to solve large multi-step problems.
Compressive phase-only filtering at extreme compression rates
Venue: arXiv cs.CV 2016
Year: 2016

We introduce an efficient method for the reconstruction of the correlation between a compressively measured image and a phase-only filter. The proposed method is based on two properties of phase-only filtering: such filtering is a unitary circulant transform, and the correlation plane it produces is usually sparse. Thanks to these properties, phase-only filters are perfectly compatible with the framework of compressive sensing. Moreover, the lasso-based recovery algorithm is very fast when phase-only filtering is used as the compression matrix. The proposed method can be seen as a generalisation of the correlation-based pattern recognition technique, which is hereby applied directly to non-adaptively acquired compressed data. At the time of measurement, any prior knowledge of the target object for which the data will be scanned is not required. We show that images measured at extremely high compression rates may still contain sufficient information for target classification and localization, even if the compression rate is high enough, that visual recognition of the target in the reconstructed image is no longer possible. The method has been applied by us to highly undersampled measurements obtained from a single-pixel camera, with sampling based on randomly chosen Walsh-Hadamard patterns.
F-measure Maximization in Multi-Label Classification with Conditionally Independent Label Subsets
Venue: arXiv cs.LG 2016
Year: 2016

We discuss a method to improve the exact F-measure maximization algorithm called GFM, proposed in (Dembczynski et al. 2011) for multi-label classification, assuming the label set can be can partitioned into conditionally independent subsets given the input features. If the labels were all independent, the estimation of only $m$ parameters ($m$ denoting the number of labels) would suffice to derive Bayes-optimal predictions in $O(m^2)$ operations. In the general case, $m^2+1$ parameters are required by GFM, to solve the problem in $O(m^3)$ operations. In this work, we show that the number of parameters can be reduced further to $m^2/n$, in the best case, assuming the label set can be partitioned into $n$ conditionally independent subsets. As this label partition needs to be estimated from the data beforehand, we use first the procedure proposed in (Gasse et al. 2015) that finds such partition and then infer the required parameters locally in each label subset. The latter are aggregated and serve as input to GFM to form the Bayes-optimal prediction. We show on a synthetic experiment that the reduction in the number of parameters brings about significant benefits in terms of performance.
The Systematic Normal Form of Lattices
Venue: arXiv cs.CC 2016
Year: 2016

We introduce a new canonical form of lattices called the systematic normal form (SNF). We show that for every lattice there is an efficiently computable "nearby" SNF lattice, such that for any lattice one can solve lattice problems on its "nearby" SNF lattice, and translate the solutions back efficiently to the original lattice. The SNF provides direct connections between arbitrary lattices, and various lattice related problems like the Shortest-Integer-Solution, Approximate Greatest Common Divisor. As our main application of SNF we derive a new set of worst-to-average case lattice reductions that deviate significantly from the template of Ajtai and improve upon previous reductions in terms of simplicity.
Programmable Crossbar Quantum-dot Cellular Automata Circuits
Venue: arXiv cs.ET 2016
Year: 2016

Quantum-dot fabrication and characterization is a well-established technology, which is used in photonics, quantum optics and nanoelectronics. Four quantum-dots placed at the corners of a square form a unit cell, which can hold a bit of information and serve as a basis for Quantum-dot Cellular Automata (QCA) nanoelectronic circuits. Although several basic QCA circuits have been designed, fabricated and tested, proving that quantum-dots can form functional, fast and low-power nanoelectornic circuits, QCA nanoelectronics still remain at its infancy. One of the reasons for this is the lack of design automation tools, which will facilitate the systematic design of large QCA circuits that contemporary applications demand. Here we present novel, programmable QCA circuits, which are based on crossbar architecture. These circuits can be programmed to implement any Boolean function in analogy to CMOS FPGAs and open the road that will lead to full design automation of QCA nanoelectronic circuits. Using this architecture we designed and simulated QCA circuits that proved to be area efficient, stable and reliable.
Procedural Generation of Angry Birds Levels using Building Constructive Grammar with Chinese-Style and/or Japanese-Style Models
Venue: arXiv cs.AI 2016
Year: 2016

This paper presents a procedural generation method that creates visually attractive levels for the Angry Birds game. Besides being an immensely popular mobile game, Angry Birds has recently become a test bed for various artificial intelligence technologies. We propose a new approach for procedurally generating Angry Birds levels using Chinese style and Japanese style building structures. A conducted experiment confirms the effectiveness of our approach with statistical significance.
Iterative $\ell_1$ minimization for non-convex compressed sensing
Venue: arXiv cs.IT 2016
Year: 2016

An algorithmic framework, based on the difference of convex functions algorithm (DCA), is proposed for minimizing a class of concave sparse metrics for compressed sensing problems. The resulting algorithm iterates a sequence of $\ell_1$ minimization problems. An exact sparse recovery theory is established to show that the proposed framework always improves on the basis pursuit ($\ell_1$ minimization) and inherits robustness from it. Numerical examples on success rates of sparse solution recovery illustrate further that, unlike most existing non-convex compressed sensing solvers in the literature, our method always out-performs basis pursuit, no matter how ill-conditioned the measurement matrix is. Moreover, the iterative $\ell_1$ (IL$_1$) algorithm lead by a wide margin the state-of-the-art algorithms on $\ell_{1/2}$ and logarithimic minimizations in the strongly coherent (highly ill-conditioned) regime, despite the same objective functions. Last but not least, in the application of magnetic resonance imaging (MRI), IL$_1$ algorithm easily recovers the phantom image with just 7 line projections.
Selecting the Selection
Venue: arXiv cs.AI 2016
Year: 2016

Modern saturation-based Automated Theorem Provers typically implement the superposition calculus for reasoning about first-order logic with or without equality. Practical implementations of this calculus use a variety of literal selections and term orderings to tame the growth of the search space and help steer proof search. This paper introduces the notion of lookahead selection that estimates (looks ahead) the effect on the search space of selecting a literal. There is also a case made for the use of incomplete selection functions that attempt to restrict the search space instead of satisfying some completeness criteria. Experimental evaluation in the \Vampire\ theorem prover shows that both lookahead selection and incomplete selection significantly contribute to solving hard problems unsolvable by other methods.
Tree-Deletion Pruning in Label-Correcting Algorithms for the Multiobjective Shortest Path Problem
Venue: arXiv cs.DS 2016
Year: 2016

In this paper, we re-evaluate the basic strategies for label correcting algorithms for the multiobjective shortest path (MOSP) problem, i.e., node and label selection. In contrast to common believe, we show that---when carefully implemented---the node-selection strategy usually beats the label-selection strategy. Moreover, we present a new pruning method which is easy to implement and performs very well on real-world road networks. In this study, we test our hypotheses on artificial MOSP instances from the literature with up to 15 objectives and real-world road networks with up to almost 160,000 nodes.
Centralized Coded Caching for Heterogeneous Lossy Requests
Venue: arXiv cs.IT 2016
Year: 2016

Centralized coded caching of popular contents is studied for users with heterogeneous distortion requirements, corresponding to diverse processing and display capabilities of mobile devices. Users' distortion requirements are assumed to be fixed and known, while their particular demands are revealed only after the placement phase. Modeling each file in the database as an independent and identically distributed Gaussian vector, the minimum delivery rate that can satisfy any demand combination within the corresponding distortion target is studied. The optimal delivery rate is characterized for the special case of two users and two files for any pair of distortion requirements. For the general setting with multiple users and files, a layered caching and delivery scheme, which exploits the successive refinability of Gaussian sources, is proposed. This scheme caches each content in multiple layers, and it is optimized by solving two subproblems: lossless caching of each layer with heterogeneous cache capacities, and allocation of available caches among layers. The delivery rate minimization problem for each layer is solved numerically, while two schemes, called the proportional cache allocation (PCA) and ordered cache allocation (OCA), are proposed for cache allocation. These schemes are compared with each other and the cut-set bound through numerical simulations.
Strategic Formation of Heterogeneous Networks
Venue: arXiv cs.GT 2016
Year: 2016

We establish a network formation game for the Internet's Autonomous System (AS) interconnection topology. The game includes different types of players, accounting for the heterogeneity of ASs in the Internet. In this network formation game, the utility of a player depends on the network structure, e.g., the distances between nodes and the cost of links. We also consider the case where utility (or monetary) transfers are allowed between the players. We incorporate reliability considerations in the player's utility function, and analyze static properties of the game as well as its dynamic evolution. We provide dynamic analysis of topological quantities, and explain the prevalence of some "network motifs" in the Internet graph. We assess our predictions with real-world data.
Interpretable Deep Neural Networks for Single-Trial EEG Classification
Venue: arXiv cs.NE 2016
Year: 2016

Background: In cognitive neuroscience the potential of Deep Neural Networks (DNNs) for solving complex classification tasks is yet to be fully exploited. The most limiting factor is that DNNs as notorious 'black boxes' do not provide insight into neurophysiological phenomena underlying a decision. Layer-wise Relevance Propagation (LRP) has been introduced as a novel method to explain individual network decisions. New Method: We propose the application of DNNs with LRP for the first time for EEG data analysis. Through LRP the single-trial DNN decisions are transformed into heatmaps indicating each data point's relevance for the outcome of the decision. Results: DNN achieves classification accuracies comparable to those of CSP-LDA. In subjects with low performance subject-to-subject transfer of trained DNNs can improve the results. The single-trial LRP heatmaps reveal neurophysiologically plausible patterns, resembling CSP-derived scalp maps. Critically, while CSP patterns represent class-wise aggregated information, LRP heatmaps pinpoint neural patterns to single time points in single trials. Comparison with Existing Method(s): We compare the classification performance of DNNs to that of linear CSP-LDA on two data sets related to motor-imaginery BCI. Conclusion: We have demonstrated that DNN is a powerful non-linear tool for EEG analysis. With LRP a new quality of high-resolution assessment of neural activity can be reached. LRP is a potential remedy for the lack of interpretability of DNNs that has limited their utility in neuroscientific applications. The extreme specificity of the LRP-derived heatmaps opens up new avenues for investigating neural activity underlying complex perception or decision-related processes.
Text writing in the air
Venue: arXiv cs.MM 2016
Year: 2016

This paper presents a real time video based pointing method which allows sketching and writing of English text over air in front of mobile camera. Proposed method have two main tasks: first it track the colored finger tip in the video frames and then apply English OCR over plotted images in order to recognize the written characters. Moreover, proposed method provides a natural human-system interaction in such way that it do not require keypad, stylus, pen or glove etc for character input. For the experiments, we have developed an application using OpenCv with JAVA language. We tested the proposed method on Samsung Galaxy3 android mobile. Results show that proposed algorithm gains the average accuracy of 92.083% when tested for different shaped alphabets. Here, more than 3000 different Magnetic 3D shaped characters were used [Ref: http://learnrnd.com/news.php?id=Magnetic_3D_Bio_Printing]. Our proposed system is the software based approach and relevantly very simple, fast and easy. It does not require sensors or any hardware rather than camera and red tape. Moreover, proposed methodology can be applicable for all disconnected languages but having one issue that it is color sensitive in such a way that existence of any red color in the background before starting the character writing can lead to false results.
Multiview Differential Geometry of Curves
Venue: arXiv cs.CV 2016
Year: 2016

The field of multiple view geometry has seen tremendous progress in reconstruction and calibration due to methods for extracting reliable point features and key developments in projective geometry. Point features, however, are not available in certain applications and result in unstructured point cloud reconstructions. General image curves provide a complementary feature when keypoints are scarce, and result in 3D curve geometry, but face challenges not addressed by the usual projective geometry of points and algebraic curves. We address these challenges by laying the theoretical foundations of a framework based on the differential geometry of general curves, including stationary curves, occluding contours, and non-rigid curves, aiming at stereo correspondence, camera estimation (including calibration, pose, and multiview epipolar geometry), and 3D reconstruction given measured image curves. By gathering previous results into a cohesive theory, novel results were made possible, yielding three contributions. First we derive the differential geometry of an image curve (tangent, curvature, curvature derivative) from that of the underlying space curve (tangent, curvature, curvature derivative, torsion). Second, we derive the differential geometry of a space curve from that of two corresponding image curves. Third, the differential motion of an image curve is derived from camera motion and the differential geometry and motion of the space curve. The availability of such a theory enables novel curve-based multiview reconstruction and camera estimation systems to augment existing point-based approaches. This theory has been used to reconstruct a "3D curve sketch", to determine camera pose from local curve geometry, and tracking; other developments are underway.