Reviewerly/exHarmony huggingface dataset:
SMS: A Framework for Service Discovery by Incorporating Social Media Information

With the explosive growth of services, including Web services, cloud services, APIs and mashups, discovering the appropriate services for consumers is becoming an imperative issue.The traditional service discovery approaches mainly face two challenges: 1) the single source of description documents limits the effectiveness of discovery due to the insufficiency of semantic information; 2) more factors should be considered with the generally increasing functional and nonfunctional requirements of consumers.In this paper, we propose a novel framework, called SMS, for effectively discovering the appropriate services by incorporating social media information.Specifically, we present different methods to measure four social factors (semantic similarity, popularity, activity, decay factor) collected from Twitter.Latent Semantic Indexing (LSI) model is applied to mine semantic information of services from meta-data of Twitter Lists that contains them.In addition, we assume the target query-service matching function as a linear combination of multiple social factors and design a weight learning algorithm to learn an optimal combination of the measured social factors.Comprehensive experiments based on a real-world dataset crawled from Twitter demonstrate the effectiveness of the proposed framework SMS, through some compared approaches.
Online Inter-Datacenter Service Migrations

Service migration between datacenters can reduce the network overhead within a cloud infrastructure; thereby, also improving the quality of service for the clients. Most of the algorithms in the literature assume that the client access pattern remains stable for a sufficiently long period so as to amortize such migrations. However, if such an assumption does not hold, these algorithms can take arbitrarily poor migration decisions that can substantially degrade system performance. In this paper, we approach the issue of performing service migrations for an unknown and dynamically changing client access pattern. We propose an online algorithm that minimizes the inter-datacenter network, taking into account the network load of migrating a service between two datacenters, as well as the fact that the client request pattern may change “quickly”, before such a migration is amortized. We provide a rigorous mathematical proof showing that the algorithm is 3.8-competitive for a cloud network structured as a tree of multiple datacenters. We briefly discuss how the algorithm can be modified to work on general graph networks with an O(log|V|) probabilistic approximation of the optimal algorithm. Finally, we present an experimental evaluation of the algorithm based on extensive simulations.
Dynamic Multi-Tenant Coordination for Sustainable Colocation Data Centers

Colocation data centers are an important type of data centers that have some unique challenges in managing their energy consumption. Tenants in a colocation data center usually manage their servers independently without coordination, leading to inefficiency. To address this issue, we propose a formulation of coordinated energy management for colocation data centers. Considering the randomness of workload arrival and electricity cost function, we formulate it as a stochastic optimization problem, and then develop an online algorithm to solve it efficiently. Our algorithm is based on Lyapunov optimization, which only needs to track the instantaneous values of the underlying random factors without requiring any knowledge of the statistics or future information. Moreover, alternating direction method of multipliers (ADMM) is utilized to implement our algorithm in a decentralized way, making it easy to be implemented in practice. We analyze the performance of our online algorithm, proving that it is asymptotically optimal and robust to the statistics of the involved random factors. Moreover, extensive trace-based simulations are conducted to illustrate the effectiveness of our approach.
Quantum Channel Capacities per Unit Cost

Communication over a noisy channel is often conducted in a setting in which different input symbols to the channel incur a certain cost. For example, for bosonic quantum channels, the cost associated with an input state is the number of photons, which is proportional to the energy consumed. In such a setting, it is often useful to know the maximum amount of information that can be reliably transmitted per cost incurred. This is known as the capacity per unit cost. In this paper, we generalize the capacity per unit cost to various communication tasks involving a quantum channel, such as classical communication, entanglement-assisted classical communication, private communication, and quantum communication. For each task, we define the corresponding capacity per unit cost and derive a formula for it analogous to that of the usual capacity. Furthermore, for the special and natural cases in which there is a zero-cost state, we obtain expressions in terms of an optimized relative entropy involving the zero-cost state. For each communication task, we construct an explicit pulse-position-modulation coding scheme that achieves the capacity per unit cost. Finally, we compute capacities per unit cost for various bosonic Gaussian channels and introduce the notion of a blocklength constraint as a proposed solution to the long-standing issue of infinite capacities per unit cost. This motivates the idea of a blocklength-cost duality on which we elaborate in depth.
Resonant and off-resonant microwave signal manipulation in coupled superconducting resonators

We present an experimental demonstration as well as a theoretical model of an integrated circuit designed for the manipulation of a microwave field down to the single-photon level. The device is made of a superconducting resonator coupled to a transmission line via a second frequency-tunable resonator. The tunable resonator can be used as a tunable coupler between the fixed resonator and the transmission line. Moreover, the manipulation of the microwave field between the two resonators is possible. In particular, we demonstrate the swapping of the field from one resonator to the other by pulsing the frequency detuning between the two resonators. The behavior of the system, which determines how the device can be operated, is analyzed as a function of one key parameter of the system, the damping ratio of the coupled resonators. We show a good agreement between experiments and simulations, realized by solving a set of coupled differential equations.
An Information-Theoretic Approach to the Cost-benefit Analysis of Visualization in Virtual Environments

a) an OpenSpace event [BMK * 15] (b) surgical simulation [VVA * 14] (c) gigaimage analytics [IV11] (d) sports training [MPW * 14]Figure 1: Four examples of typical virtual environments (VEs) used for visualization applications.
Increasing Imaging Resolution by Non-Regular Sampling and Joint Sparse Deconvolution and Extrapolation

Increasing the resolution of image sensors has been a never ending struggle since many years. In this paper, we propose a novel image sensor layout, which allows for the acquisition of images at a higher resolution and improved quality. For this, the image sensor makes use of non-regular sampling, which reduces the impact of aliasing. Therewith, it allows for capturing details, which would not be possible with state-of-the-art sensors of the same number of pixels. The non-regular sampling is achieved by rotating prototype pixel cells in a non-regular fashion. As not the whole area of the pixel cell is sensitive to light, a non-regular spatial integration of the incident light is obtained. Based on the sensor output data, a high-resolution image can be reconstructed by performing a deconvolution with respect to the integration area and an extrapolation of the information to the insensitive regions of the pixels. To solve this challenging task, we introduce a novel joint sparse deconvolution and extrapolation algorithm. The union of non-regular sampling and the proposed reconstruction allows for achieving a higher resolution and therewith an improved imaging quality.
Quality metrics for RDF graph summarization

RDF Graph Summarization pertains to the process of extracting concise but meaningful summaries from RDF Knowledge Bases (KBs) representing as close as possible the actual contents of the KB both in terms of structure and data. RDF Summarization allows for better exploration and visualization of the underlying RDF graphs, optimization of queries or query evaluation in multiple steps, better understanding of connections in Linked Datasets and many other applications. In the literature, there are efforts reported presenting algorithms for extracting summaries from RDF KBs. These efforts though provide different results while applied on the same KB, thus a way to compare the produced summaries and decide on their quality and best-fitness for specific tasks, in the form of a quality framework, is necessary. So in this work, we propose a comprehensive Quality Framework for RDF Graph Summarization that would allow a better, deeper and more complete understanding of the quality of the different summaries and facilitate their comparison. We work at two levels: the level of the ideal summary of the KB that could be provided by an expert user and the level of the instances contained by the KB. For the first level, we are computing how close the proposed summary is to the ideal solution (when this is available) by defining and computing its precision, recall and F-measure against the ideal solution. For the second level, we are computing if the existing instances are covered (i.e. can be retrieved) and at which degree by the proposed summary. Again we define and compute its precision, recall and F-measure against the data contained in the original KB. We also compute the connectivity of the proposed summary compared to the ideal one, since in many cases (like, e.g., when we want to query) this is an important factor and in general in RDF, linked datasets are usually used. We use our quality framework to test the results of three of the best RDF Graph Summarization algorithms, when summarizing different (in terms of content) and diverse (in terms of total size and number of instances, classes and predicates) KBs and we present comparative results for them. We conclude this work by discussing these results and the suitability of the proposed quality framework in order to get useful insights for the quality of the presented results.
Visual Analysis of Aneurysm Data using Statistical Graphics

This paper presents a framework to explore multi-field data of aneurysms occurring at intracranial and cardiac arteries by using statistical graphics. The rupture of an aneurysm is often a fatal scenario, whereas during treatment serious complications for the patient can occur. Whether an aneurysm ruptures or whether a treatment is successful depends on the interaction of different morphological such as wall deformation and thickness, and hemodynamic attributes like wall shear stress and pressure. Therefore, medical researchers are very interested in better understanding these relationships. However, the required analysis is a time-consuming process, where suspicious wall regions are difficult to detect due to the time-dependent behavior of the data. Our proposed visualization framework enables medical researchers to efficiently assess aneurysm risk and treatment options. This comprises a powerful set of views including 2D and 3D depictions of the aneurysm morphology as well as statistical plots of different scalar fields. Brushing and linking aids the user to identify interesting wall regions and to understand the influence of different attributes on the aneurysm's state. Moreover, a visual comparison of pre- and post-treatment as well as different treatment options is provided. Our analysis techniques are designed in collaboration with domain experts, e.g., physicians, and we provide details about the evaluation.
Xorshift1024*, xorshift1024+, xorshift128+ and xoroshiro128+ fail statistical tests for linearity

L'Ecuyer & Simard's Big Crush statistical test suite has revealed statistical flaws in many popular random number generators including Marsaglia's Xorshift generators. Vigna recently proposed some 64-bit variations on the Xorshift scheme that are further scrambled (i.e., xorshift1024*, xorshift1024+, xorshift128+, xoroshiro128+). Unlike their unscrambled counterparts, they pass Big Crush when interleaving blocks of 32 bits for each 64-bit word (most significant, least significant, most significant, least significant, etc.). We report that these scrambled generators systematically fail Big Crush – specifically the linear-complexity and matrix-rank tests that detect linearity – when taking the 32 lowest-order bits in reverse order from each 64-bit word.
Sensitivity of quantum speedup by quantum annealing to a noisy oracle

The glued-trees problem is the only example known to date for which quantum annealing provides an exponential speedup, albeit by partly using excited-state evolution, in an oracular setting. How robust is this speedup to noise on the oracle? To answer this, we construct phenomenological short-range and long-range noise models, and noise models that break or preserve the reflection symmetry of the spectrum. We show that under the long-range noise models an exponential quantum speedup is retained. However, we argue that a classical algorithm with an equivalent long-range noise model also exhibits an exponential speedup over the noiseless model. In the quantum setting, the long-range noise is able to lift the spectral gap of the problem so that the evolution changes from diabatic to adiabatic. In the classical setting, long-range noise creates a significant probability of the walker landing directly on the EXIT vertex. Under short-range noise the exponential speedup is lost, but a polynomial quantum speedup is retained for sufficiently weak noise. In contrast to noise range, we find that breaking of spectral symmetry by the noise has no significant impact on the performance of the noisy algorithms. Our results about the long-range models highlight that care must be taken in selecting phenomenological noise models so as not to change the nature of the computational problem. We conclude from the short-range noise model results that the exponential speedup in the glued-trees problem is not robust to noise, but a polynomial quantum speedup is still possible.
On Using Linear Diophantine Equations for in-Parallel Hiding of Decision Tree Rules

Data sharing among organizations has become an increasingly common procedure in several areas such as advertising, marketing, electronic commerce, banking, and insurance sectors. However, any organization will most likely try to keep some patterns as hidden as possible once it shares its datasets with others. This paper focuses on preserving the privacy of sensitive patterns when inducing decision trees. We adopt a record augmentation approach to hide critical classification rules in binary datasets. Such a hiding methodology is preferred over other heuristic solutions like output perturbation or cryptographic techniques, which limit the usability of the data, since the raw data itself is readily available for public use. We propose a look ahead technique using linear Diophantine equations to add the appropriate number of instances while maintaining the initial entropy of the nodes. This method can be used to hide one or more decision tree rules optimally.
Generating Maximal Entanglement between Spectrally Distinct Solid-State Emitters

We show how to create maximal entanglement between spectrally distinct solid-state emitters embedded in a waveguide interferometer. By revealing the rich underlying structure of multiphoton scattering in emitters, we show that a two-photon input state can generate deterministic maximal entanglement even for emitters with significantly different transition energies and linewidths. The optimal frequency of the input is determined by two competing processes: which-path erasure and interaction strength. We find that smaller spectral overlap can be overcome with higher photon numbers, and quasimonochromatic photons are optimal for entanglement generation. Our work provides a new methodology for solid-state entanglement generation, where the requirement for perfectly matched emitters can be relaxed in favor of optical state optimization.
Detecting mixed-unitary quantum channels is NP-hard

A quantum channel is said to be a<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">mixed-unitary</mml:mtext></mml:mrow></mml:math>channel if it can be expressed as a convex combination of unitary channels. We prove that, given the Choi representation of a quantum channel<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi mathvariant="normal">Φ</mml:mi></mml:math>, it is NP-hard with respect to polynomial-time Turing reductions to determine whether or not<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi mathvariant="normal">Φ</mml:mi></mml:math>is a mixed-unitary channel. This hardness result holds even under the assumption that<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi mathvariant="normal">Φ</mml:mi></mml:math>is not within an inverse-polynomial distance (in the dimension of the space upon which<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi mathvariant="normal">Φ</mml:mi></mml:math>acts) of the boundary of the mixed-unitary channels.
Quality assessment for view synthesis using low-level and mid-level structural representation

View synthesis is the most important technique in multi-view and free-viewpoint videos. The whole view synthesis includes the acquisition and processing of texture and depth images, and the virtual view rendering stage. Existing quality metrics for view synthesis have limited ability for the whole synthesis process for the following reasons. First, they are dedicated to a single stage of view synthesis, overlooking the commonality of all the possible distortions introduced in the whole process. Moreover, they only extract low-level features for quality assessment, ignoring the perceptual degradation caused by the mid-level contours that are destructed by heavy distortions in texture/depth images and the imperfect view rendering, which represent the spatial distribution/connection of adjacent contour pixels. Inspired by the above facts, this paper presents a quality metric for view synthesis using both Low-level and Mid-level Structural representation (LMS), aiming to accurately evaluate the distortions in the whole view synthesis process. Specifically, the scale space is first constructed to mimic the hierarchical property of the human visual system. Then, the statistics of gradient orientation is integrated with the statistics of gradient intensity for the low-level structural representation, which is motivated by the importance of the orientation selectivity mechanism to visual perception. Further, the mid-level structure is represented using bag of words for contour description based on the sparse coding of the primary visual cortex. Then the distances of both the low-level and mid-level features between the synthesized and reference images are calculated. Finally, two distances are integrated to generate the whole quality score. Extensive experiments on two public view synthesis databases demonstrate the superiority of the proposed method to the state-of-the-arts in evaluating the quality of the whole view synthesis.
Equation Planting: A Tool for Benchmarking Ising Machines

We introduce a methodology for generating benchmark problem sets for Ising machines---devices designed to solve discrete optimization problems cast as Ising models. In our approach, linear systems of equations are cast as Ising cost functions. While linear systems are easily solvable, the corresponding optimization problems are known to exhibit some of the salient features of NP-hardness, such as strong exponential scaling of heuristic solvers' runtimes and extensive distances between ground and low-lying excited states. We show how the proposed technique, which we refer to as `equation planting,' can serve as a useful tool for evaluating the utility of Ising solvers functioning either as optimizers or as ground-state samplers. We further argue that equation-planted problems can be used to probe the mechanisms underlying the operation of Ising machines.
On the Adaptivity of Stochastic Gradient-Based Optimization

Stochastic-gradient-based optimization has been a core enabling methodology in applications to large-scale problems in machine learning and related areas. Despite the progress, the gap between theory and practice remains significant, with theoreticians pursuing mathematical optimality at a cost of obtaining specialized procedures in different regimes (e.g., modulus of strong convexity, magnitude of target accuracy, signal-to-noise ratio), and with practitioners not readily able to know which regime is appropriate to their problem, and seeking broadly applicable algorithms that are reasonably close to optimality. To bridge these perspectives it is necessary to study algorithms that are adaptive to different regimes. We present the stochastically controlled stochastic gradient (SCSG) method for composite convex finite-sum optimization problems and show that SCSG is adaptive to both strong convexity and target accuracy. The adaptivity is achieved by batch variance reduction with adaptive batch sizes and a novel technique, which we referred to as geometrization, which sets the length of each epoch as a geometric random variable. The algorithm achieves strictly better theoretical complexity than other existing adaptive algorithms, while the tuning parameters of the algorithm only depend on the smoothness parameter of the objective.
Gaze Information Channel in Cognitive Comprehension of Poster Reading

Today, eye trackers are extensively used in studying human cognition. However, it is hard to analyze and interpret eye movement data from the cognitive comprehension perspective of poster reading. To find quantitative links between eye movements and cognitive comprehension, we tracked observers’ eye movement for reading scientific poster publications. We model in this paper eye tracking fixation sequences between content-dependent Areas of Interests (AOIs) as a Markov chain. Furthermore, we use the fact that a Markov chain is a special case of information or communication channel. Then, the gaze transition can be modeled as a discrete information channel, the gaze information channel. Next, some traditional eye tracking metrics, together with the gaze entropy and mutual information of the gaze information channel are calculated to quantify cognitive comprehension for every participant. The analysis of the results demonstrate that the gaze entropy and mutual information from individual gaze information channel are related to participants’ individual differences. This is the first study that eye tracking technology has been used to assess the cognitive comprehension of poster reading. The present work provides insights into human cognitive comprehension by using the novel gaze information channel methodology.
Temporally multimode four-photon Hong-Ou-Mandel interference

The two-photon Hong-Ou-Mandel (HOM) interference is a pure quantum effect which indicates the degree of indistinguishability of photons. The four-photon HOM interference exhibits richer dynamics in comparison to the two-photon interference and simultaneously is more sensitive to the input photon states. We demonstrate theoretically and experimentally an explicit dependency of the four-photon interference on the number of temporal modes, created in the process of parametric down-conversion. Moreover, we exploit the splitting ratio of the beam splitter to manipulate the interference between bunching and antibunching. Our results reveal that the temporal mode structure (multimodeness) of the quantum states shapes many-particle interference.