Reviewerly/exHarmony huggingface dataset:
Path-Specific Effects

A cause may influence its effect via multiple paths. Paradigmatically (Hesslow [1976]), taking birth control pills both decreases one’s risk of thrombosis by preventing pregnancy and increases it by producing a blood chemical. Building on (Pearl [2001]), I explicate the notion of a path-specific effect. Roughly, a path-specific effect of C on E via path P is the degree to which a change in C would change E were it to be transmitted only via P. Facts about such effects may be gleaned from the structural equations commonly used to represent the causal relationships among variables. I contrast my analysis of the Hesslow case with those given by theorists of probabilistic causality, who mistakenly link it to issues of causal heterogeneity, token causation, and indeterminism. The reason probabilistic theories misdiagnose this case is that they pay inadequate attention to the structural relationships among variables. 1 Introduction2 Probabilistic Causality and Structural Causal Models3 Cartwright and Eells Debate Birth Control4 Defining Path-Specific Effects5 Path-Specific Effects and Probabilistic Causality6 Conclusion
A user experience environment model for human activity simulation

In this paper, we propose a user experience environment model for quantifying user experience information in a virtual space. This model consists of a spatial model, a user activity model, and an object model. The spatial and the object models represent a space containing the objects the user is using. The user activity model contains information on the experience information that a user accumulates while using the space. Experiments to quantify the user experience in the high complexity space require a lot of time and money. Thus, this approach can reduce the cost of obtaining user experience information by composing a virtual space similar to the real space and predicting the user activity in this space. In conclusion, we propose a simulator for collecting information on user experience environments and for predicting user activity through knowledge gained using the collected data.
Hyperspectral remote detection as an alternative to correlate data of soil constituents

Imaging spectroscopy is a proven technology for identifying and mapping the constituents of soils based on their spectral signatures, the variation of their reflectance or emissivity across different wavelengths, in the visible and infrared bands. Despite the great potential for use of hyperspectral images, there is a lack of previous work that examines the hyperspectral imaging sensors and spectroradiometers, especially in controlled laboratory configurations. Besides that, AisaFENIX is the only sensor of this model available for research in Brazil. In our country there is no experience related to this sensor, being a previous and necessary step of the use of the sensor in aero-surveys. We need to understand the operation and behavior of this sensor. In addition, Brazil is a hot and humid country, with no robust weather models that are in keeping with the local need. This work aims to correlate soil data obtained in the laboratory by two different hyperspectral sensors, AisaFENIX and spectroradiometer, in different bands of the electromagnetic spectrum. Soils were sampled in Mandaguaçu, Paraná, an area of approximately 200 ha, totaling 207 georeferenced samples. The soil reflectance laboratory data were obtained using the Fieldspec 3 Jr spectroradiometer sensor, which collects a spectral range of 350–2.500 nm (vis-SWIR), and the reflectance values of the hyperspectral images were acquired with the AisaFENIX sensor, which collects a spectral range of 350–2.500 nm. The correlation between the sensors was satisfactory (R2 0.97) in the corresponding iron range (500–640 nm), and for the corresponding bands of kaolinite (2.200 nm), gibbsite (2.265 nm), water (1.400 and 1.900 nm), the R2 values found were 0.61, 0.65, 0.63 and 0.64 respectively. The lowest correlation coefficients found were for the bands of organic carbon (600–1.100 nm), goethite (480 nm), hematite (530 nm) and specific goethite (450–480 nm), with R2 values less than 0.41. The t-test showed that the correlations were significant (p < 0.01), indicating that there is an excellent agreement between the two sensors. The SAM and ASDS indexes showed that the spectra obtained by the spectroradiometer and AisaFENIX sensors were similar, with a better index value of 0.03 (SAM) and 0.003 (ASDS). The methodology adopted in this work of studying the agreement between the data obtained by these sensors has significant potential and would be promising for future work to build upon.
Investigations with Reversible Feynman Gate and Irreversible Logic Schematics

Abstract In the contemporary world there is enormous hike in communication engineering applications, outcome with massive heat dissipation from the processing nodes. So energy efficient information network is one of paramount issue nowadays. For that optical reversible computing could be a landmark with base as optical logic gate. Reduction in power dissipation, consumption could be accomplished through a blend of reversible and irreversible optical processing and the nodes may recuperate the data. Accordingly, in this article two designs with semiconductor optical amplifier, used as Mach–Zehnder interferometer based all optical reversible Feynman gate, irreversible AND logic gate within a single photonic circuit has been proposed. The output waveforms for AND logic operation, Feynman logic the P (data output identical to input), Q (A ⊕ B) has been verified at 100 Gbps data rate. The designs have been evaluated on the basis of key parameter extinction ratio factor. Numerical simulations have inferred excellent ER performance with design-2(ER&gt;13 dB) in contrast to design-1(ER as 10.2 dB). Performance evaluations for significant deign parameters as pump current, length, width, carrier transport, confine and current injection factor yielded excellent performance. This evaluation could be an assist toward design of contemporary optical networks.
GMA: An adult account identification algorithm on Sina Weibo using behavioral footprints

The uncontrolled spread of sexually explicit content (i.e., adult content) on online social networks (e.g., Sina Weibo) has become an emerging yet acute challenge. An effective method to identify accounts spreading adult content automatically is of significant values in user experience improvement and children protection. Traditional adult content detection and behavioral characteristics of individual account analysis techniques are ill-suited for identifying adult accounts on Sina Weibo due to the collaborative working of adult groups. In this paper, we propose an adult account identification algorithm, which is termed GMA, by analyzing behavioral features (i.e., behavioral footprints) of both groups and individual accounts. In GMA, a novel relation-based model, which considers the inter-relationships among groups, individual accounts and message sources, is applied to identify adult accounts. The experimental results show that compared with state-of-the-art methods, our proposed GMA algorithm can improve the performance of adult account identification on Sina Weibo.
Genetic Algorithms for Breast Cancer Diagnostics

Breast cancer is the most common cancer in women worldwide, representing about 12% of all new cancer cases and 25% of all cancers in women (2012). The widespread development of high-performance machine learning (ML) algorithms, with focus put on genetic algorithms (GAs) led to the search of efficient tools for breast cancer diagnostics. There is no sure way to prevent breast cancer, but its early detection using the GAs support might lower the risk of complications. The automatic breast cancer early detection can be achieved by building a reliable GA model, thus making the process of fighting the disease much easier. With such an approach, researchers and physicians can trustfully use the “digital second opinion” and make the final optimum decision. In this review, we provide an overview on some GA algorithms that can be applied in breast cancer diagnostics, thus emphasizing the benefits of using such effective algorithms as a physician’s “intelligent digital assistant.”
A randomized method for handling a difficult function in a convex optimization problem, motivated by probabilistic programming

We propose a randomized gradient method for handling a convex function whose gradient computation is demanding. The method bears a resemblance to the stochastic approximation family. But in contrast to stochastic approximation, the present method builds a model problem. The approach is adapted to probability maximization and probabilistic constrained problems. We discuss simulation procedures for gradient estimation.
Localization of eye Saccadic signatures in Electrooculograms using sparse representations with data driven dictionaries

In this paper, we propose two methods for localizing saccadic eye movement signatures from Electrooculograms (EOG). The first approach uses a sparse representation of data-driven dictionaries of saccadic movements. In this approach, we match the EOG subsequence with the dictionary element using distance metrics to identify the saccades. The second approach is to compare a saccadic signature template with the EOG subsequence using Dynamic Time Warping (DTW). We find that the proposed methods have advantages over one another in context specific applications. The first method is significantly faster with considerable accuracy, while the second approach is more accurate.
On the minimum of the mean-squared error in 2-means clustering

We study the minimum mean-squared error for 2-means clustering when the outcomes of the vector-valued random variable to be clustered are on two touching spheres of unit radius in $n$-dimensional Euclidean space and the underlying probability distribution is the normalized surface measure. For simplicity, we only consider the asymptotics of large sample sizes and replace empirical samples by the probability measure. The concrete question addressed here is whether a minimizer for the mean-squared error identifies the two individual spheres as clusters. Indeed, in dimensions $n \ge 3$, the minimum of the mean-squared error is achieved by a partition that separates the two spheres and has unit distance between the points in each cluster and the respective mean. In dimension $n=2$, however, the minimizer fails to identify the individual spheres; an optimal partition is obtained by a separating hyperplane that does not contain the point at which the spheres touch.
sPARE: Partial Replication for Multi-Tier Applications in the Cloud

Offering consistent low latency remains a key challenge for distributed applications, especially when deployed on the cloud where virtual machines (VMs) suffer from capacity variability caused by co-located tenants. Replicating redundant requests was shown to be an effective mechanism to defend application performance from high capacity variability. While the prior art centers on single-tier systems, it still remains an open question how to design replication strategies for distributed multi-tier systems. In this paper, we design a first of its kind PArtial REplication system, sPARE, that replicates and dispatches read-only workloads for distributed multi-tier web applications. The two key components of sPARE are (i) the variability-aware replicator that coordinates the replication levels on all tiers via an iterative searching algorithm, and (ii) the replication-aware arbiter that uses a novel token-based arbitration algorithm (TAD) to dispatch requests in each tier. We evaluate sPARE on web serving and searching applications, i.e., MediaWiki and Solr, the former deployed on our private cloud and the latter on Amazon EC2. Our results based on various interference patterns and traffic loads show that sPARE is able to improve the tail latency of MediaWiki and Solr by a factor of almost 2.7x and 2.9x, respectively.
Exponential separation between quantum and classical ordered binary decision diagrams, reordering method and hierarchies

In this paper, we study quantum Ordered Binary Decision Diagrams( $$\mathrm {OBDD}$$ ) model; it is a restricted version of read-once quantum branching programs, with respect to “width” complexity. It is known that the maximal gap between deterministic and quantum complexities is exponential. But there are few examples of functions with such a gap. We present a new technique (“reordering”) for proving lower bounds and upper bounds for OBDD with an arbitrary order of input variables if we have similar bounds for the natural order. Using this transformation, we construct a total function $$\mathrm {REQ}$$ such that the deterministic $$\mathrm {OBDD}$$ complexity of it is at least $$2^{\varOmega (n / \log n)}$$ , and the quantum $$\mathrm {OBDD}$$ complexity of it is at most $$O(n^2/\log n)$$ . It is the biggest known gap for explicit functions not representable by $$\mathrm {OBDD}$$ s of a linear width. Another function(shifted equality function) allows us to obtain a gap $$2^{\varOmega (n)}$$ vs $$O(n^2)$$ . Moreover, we prove the bounded error quantum and probabilistic $$\mathrm {OBDD}$$ width hierarchies for complexity classes of Boolean functions. Additionally, using “reordering” method we extend a hierarchy for read-k-times Ordered Binary Decision Diagrams ( $${\textit{k}}\text {-}\mathrm {OBDD}$$ ) of polynomial width, for $$k = o(n / \log ^3 n)$$ . We prove a similar hierarchy for bounded error probabilistic $${\textit{k}}\text {-}\mathrm {OBDD}$$ s of polynomial, superpolynomial and subexponential width. The extended abstract of this work was presented on International Computer Science Symposium in Russia, CSR 2017, Kazan, Russia, June 8 – 12, 2017 Khadiev and Khadieva (2017)
Parallel cosine nearest neighbor graph construction

The nearest neighbor graph is an important structure in many data mining methods for clustering, advertising, recommender systems, and outlier detection. Constructing the graph requires computing up to n2 similarities for a set of n objects. This high complexity has led researchers to seek approximate methods, which find many but not all of the nearest neighbors. In contrast, we leverage shared memory parallelism and recent advances in similarity joins to solve the problem exactly. Our method considers all pairs of potential neighbors but quickly filters pairs that could not be a part of the nearest neighbor graph, based on similarity upper bound estimates. The filtering is data dependent and not easily predicted, which poses load balance challenges in parallel execution. We evaluated our methods on several real-world datasets and found they work up to two orders of magnitude faster than existing methods, display linear strong scaling characteristics, and incur less than 1% load imbalance during filtering.
Research Data Management Among Life Sciences Faculty: Implications for Library Service

Objective: This paper aims to inform on opportunities for librarians to assist faculty with research data management by examining practices and attitudes among life sciences faculty at a tier one research university.
Maximally entangled states in discrete and Gaussian regimes

We study a relation between discrete-variable quantum states and continuous-variable (especially, restricted on Gaussian) ones. In the previous work, we have investigated an information-theoretic correspondence between the Gaussian maximally mixed states and their purifications as Gaussian maximally entangled states in Jeong and Lim (Phys Lett A 380:3607, 2016). We here compare the purified continuous-variable maximally entangled state with a two-mode squeezed vacuum state, which is a conventional entangled state in Gaussian regime, by the explicit calculation of quantum fidelities between those states and an $$N\times N$$ -dimensional maximally entangled state in the finite Hilbert space. Consequently, we naturally conclude that the purified maximally entangled state is more suitable to the Gaussian maximally entangled state than the two-mode squeezed vacuum state, in a sense that it might be useful for continuous-variable quantum information tasks in which entangled states are needed.
Authorship Identification of Translational Algorithms

Authorship analysis is a process of identifying a true writer of a given document and has been studied for decades. However, only a handful of studies of authorship identification of translators are available even though online translations are widely available and popularly employed in automatic translations of posts in social networking services. The identification of translation algorithms has potential to contribute to the investigation of cybercrimes, involving translation of scam messages by algorithmic translations to reach speakers of foreign languages. This study tested bag of words (BOW) approach in authorship attribution and the existing approaches to translator attribution. We also proposed a simple but accurate feature that extracts the combinations of lexical and syntactic information from texts. Our experiments show that the proposed feature is text size invariant.
Recognizing Read-Once Functions from Depth-Three Formulas

Consider the following decision problem: for a given monotone Boolean function f decide, whether f is read-once. For this problem, it is essential how the input function f is represented. Elbassioni et al. (J. Comb. Optim. 22(3), 293–304, 2011) proved that this problem is coNP-complete when f is given by a depth-4 read-2 monotone Boolean formula. Gurvich (2010) proved that this problem is coNP-complete even when the input is the following expression: C ∨ Dn, where Dn = x1y1 ∨ … ∨ xnyn and C is a monotone CNF over the variables x1, y1, … , xn, yn (note that this expression is a monotone Boolean formula of depth 3; in Gurvich (2010) nothing is said about the readability of C, but the proof is valid even if C is read-2 and thus the entire formula is read-3). We show that we can test in polynomial-time whether a given expression C ∨ D computes a read-once function, provided that C is a read-once monotone CNF and D is a read-once monotone DNF and all the variables of C occur also in D (recall that due to Gurvich, the problem is coNP-complete when C is read-2). We also observe that from the so-called Sausage Lemma of Boros et al. (2009) it follows that the problem of recognizing read-once functions is coNP-complete when the input formula is depth-3 read-2.
A method for discovering knowledge in texts

We discuss a more powerful probabilistic graphical model for discovering semantic patterns from sequential text data, such as sentences. It is developed based on the idea that each word (or each symbol) in a sentence itself might carry lexical, semantic, or syntactic information, which can be used to replace conditional dependences in existing methods. Hence, our method has fewer conditional independence assumptions in contrast to these existing probabilistic graphical methods, such as CRFs, HMMs, MEMMs and Naive Bayes. Moreover, our method does not need to employ dynamic programming and therefore the on-line time complexity and memory complexity are reduced. We test the method on discovering noun phrases, the meaning of an ambiguous word, and semantic arguments of a verb in a sentence. We find that the misclassification rate is smaller compared to previously published results on the same data sets. For example, the method achieves an average f-measure of 98.25% for recognizing noun phrases on WSJ data from Penn Treebank; an average accuracy of 81.12% for recognizing the six sense word line; an average f-measure of 93.61% for classifying semantic argument boundaries of a verb in a sentence on WSJ data from Penn Treebank and PropBank.
Randomized Voting-Based Rigid-Body Motion Segmentation

In this paper, we propose a novel rigid-body motion segmentation algorithm that uses randomized voting to assign high scores to correctly estimated models and low scores to wrongly estimated models. This algorithm is based on an epipolar geometrical representation of the camera motion, and computes scores using the distance between the feature point and the corresponding epipolar line. These scores are accumulated and utilized for motion segmentation. To evaluate the efficacy of our algorithm, we conduct a series of experiments using the Hopkins 155 data set and the UdG data set, which are representative test sets for rigid motion segmentation. Among several state-of-the-art data sets, our algorithm achieves the most accurate motion segmentation results and, in the presence of measurement noise, achieves comparable results to the other algorithms. Finally, we analyze why our motion segmentation algorithm works using probabilistic and theoretical analysis.
Fine-Grained Visual Comparison Based on Relative Attribute Quadratic Discriminant Analysis

In the vision research field, it is useful to make a comparison to see which one of two images exhibits a particular visual attribute more than the other. Feature representation and metric learning are two critical factors for a fine-grained visual comparison. In this paper, first an informative feature descriptor combining histograms of oriented gradient feature with gist feature is proposed, and then a method called relative attribute quadratic discriminant analysis is proposed for dimensionality reduction and metric learning simultaneously. In the method, those pairs most analogous to a test pair are identified by using the learned metric and then used to build a local ranking function. Finally, the test pair label is predicted by the ranking function for each attribute. Experimental results on the three databases of UT-Zap50K, Outdoor Scene Recognition dataset, and Public Figures faces dataset demonstrate the advantages of the proposed method.
Cost-Aware Traffic Management Under Demand Uncertainty from a Colocation Data Center User’s Perspective

Burstable billing is widely adopted by colocation data center providers to charge their users for data transferring. This paper proposes a cost-aware traffic management approach for a colocation data center user under burstable billing where it is charged based on the 95th percentile bandwidth usage. To do this, we first develop a tractable mathematical expression to calculate the 95th percentile usage of a user. Then, we develop an optimization problem to maximize the user's surplus based on both deterministic and stochastic predictions of the user's demand. We show that the resulted optimization problem, while non-convex by nature, can be efficiently solved or approximated using a convex program. We also show that the proposed approach can also be applied in a more general scenario where the user gets services from multiple service providers. Using real-world workload traces, we show that the proposed approach can reduce a colocation data center user's IP transit cost by 26 percent and increase its total surplus by 23 percent, compared to the current practice of allocating bandwidth on-demand.
Intelligent Agents and Environment

This article aims to provide a panorama of the fundamentals related to Agents. Specifically, the most popular definitions behind the concept of Intelligent Agents and the main properties that characterize an agent are discussed along with different agent-based models. Furthermore, the concept of Environment as well as its properties and the role that it plays in the context of the agent paradigm are presented. Actions and Interactions among agents and environment are also discussed before contextualizing the role of Intelligent Agents in the Computational Biology domain.