Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Commercial Cloud Services : A Systematic Literature Review

Background: Cloud Computing is increasingly booming in industry with many competing providers and services. Accordingly, evaluation of commercial Cloud services is necessary. However, the existing evaluation studies are relatively chaotic. There exists tremendous confusion and gap between practices and theory about Cloud services evaluation. Aim: To facilitate relieving the aforementioned chaos, this work aims to synthesize the existing evaluation implementations to outline the state-of-the-practice and also identify research opportunities in Cloud services evaluation. Method: Based on a conceptual evaluation model comprising six steps, the Systematic Literature Review (SLR) method was employed to collect relevant evidence to investigate the Cloud services evaluation step by step. Results: This SLR identified 82 relevant evaluation studies. The overall data collected from these studies essentially represent the current practical landscape of implementing Cloud services evaluation, and in turn can be reused to facilitate future evaluation work. Conclusions: Evaluation of commercial Cloud services has become a world-wide research topic. Some of the findings of this SLR identify several research gaps in the area of Cloud services evaluation (e.g., the Elasticity and Security evaluation of commercial Cloud services could be a long-term challenge), while some other findings suggest the trend of applying commercial Cloud services (e.g., compared with PaaS, IaaS seems more suitable for customers and is particularly important in industry). This SLR study itself also confirms some previous experiences and reveals new Evidence-Based Software Engineering (EBSE) lessons.
Compressive spectral estimation for nonstationary random processes

We propose a “compressive” estimator of the Wigner-Ville spectrum (WVS) for time-frequency sparse, underspread, nonstationary random processes. A novel WVS estimator involving the signal's Gabor coefficients on an undersampled time-frequency grid is combined with a compressed sensing transformation in order to reduce the number of measurements required. The performance of the compressive WVS estimator is analyzed via a bound on the mean square error and through simulations. We also propose an efficient implementation using a special construction of the measurement matrix.
How to derive a protein folding potential? A new approach to the old problem

In this paper we introduce a novel method of deriving a pairwise potential for protein folding. The potential is obtained by optimization procedure, which simultaneously maximizes the energy gap for {\it all} proteins in the database. To test our method and compare it with other knowledge-based approaches to derive potentials, we use simple lattice model. In the framework of the lattice model we build a database of model proteins by a) picking randomly 200 lattice chain conformations; b) designing sequences which fold into these structures with some arbitrary ``true'' potential; c) use this database for extracting a potential; d) fold model proteins using the extracted potential. This test on the model system showed that our procedure is able to recover the potential with correlation $r \approx 91\%$ with the ``true'' one and we were able to fold all model structures using the recovered potential. Other statistical knowledge-based approaches were tested using lattice models and the results indicate that they also can recover the ``true'' potential with high degree of accuracy. When applied to real protein structures with energy function taken in contact pairwise approximation, our potential scored somewhat better than existing ones. However, the discrimination of the native structure from decoys is still not strong enough to make the potential useful for {\em ab initio} folding. We argue that more detail of protein structure and energetics should be taken into account to achieve better energy gaps. The suggested method is general to allow to systematically derive parameters for more sophisticated energy function. The internal control of validity of the potential derived by our method, is converegency to a unique solution upon addition of new proteins to the database.
Diversity Analysis of Bit-Interleaved Coded Multiple Beamforming

In this paper, diversity analysis of bit-interleaved coded multiple beamforming (BICMB) is extended to the case of general spatial interleavers, removing a condition on their previously known design criteria and quantifying the resulting diversity order. The diversity order is determined by a parameter Qmax which is inherited from the convolutional code and the spatial de-multiplexer used in BICMB. We introduce a method to find this parameter by employing a transfer function approach as in finding the weight spectrum of a convolutional code. By using this method, several Qmax values are shown and verified to be identical with the results from a computer search. The diversity analysis and the method to find the parameter are supported by simulation results. By using the Singleton bound, we also show that Qmax is lower bounded by the product of the number of streams and the code rate of an encoder. The design rule of the spatial de-multiplexer for a given convolutional code is proposed to meet the condition on the maximum achievable diversity order.
SPATIAL PREFERENTIAL ATTACHMENT NETWORKS: POWER LAWS AND CLUSTERING COEFFICIENTS1 BY EMMANUEL JACOB

We define a class of growing networks in which new nodes are given a spatial position and are connected to existing nodes with a probability mechanism favoring short distances and high degrees. The competition of preferential attachment and spatial clustering gives this model a range of interesting properties. Empirical degree distributions converge to a limit law, which can be a power law with any exponent τ > 2. The average clustering coefficient of the networks converges to a positive limit. Finally, a phase transition occurs in the global clustering coefficients and empirical distribution of edge lengths when the power-law exponent crosses the critical value τ = 3. Our main tool in the proof of these results is a general weak law of large numbers in the spirit of Penrose and Yukich.
On Data Dependence in Distributed Stochastic Optimization

We study a distributed consensus-based stochastic gradient descent (SGD) algorithm and show that the rate of convergence involves the spectral properties of two matrices: the standard spectral gap of a weight matrix from the network topology and a new term depending on the spectral norm of the sample covariance matrix of the data. This data-dependent convergence rate shows that distributed SGD algorithms perform better on datasets with small spectral norm. Our analysis method also allows us to find data-dependent convergence rates as we limit the amount of communication. Spreading a fixed amount of data across more nodes slows convergence; for asymptotically growing data sets we show that adding more machines can help when minimizing twice-differentiable losses.
MINIMAX OPTIMAL RATES FOR MONDRIAN TREES AND FORESTS By

Introduced by Breiman [9], Random Forests are widely used classification and regression algorithms. While being initially designed as batch algorithms, several variants have been proposed to handle online learning. One particular instance of such forests is the Mondrian Forest [23, 24], whose trees are built using the so-called Mondrian process, therefore allowing to easily update their construction in a streaming fashion. In this paper, we provide a thorough theoretical study of Mondrian Forests in a batch learning setting, based on new results about Mondrian partitions. Our results include consistency and convergence rates for Mondrian Trees and Forests, that turn out to be minimax optimal on the set of s-Hölder function with s ∈ (0, 1] (for trees and forests) and s ∈ (1, 2] (for forests only), assuming a proper tuning of their complexity parameter in both cases. Furthermore, we prove that an adaptive procedure (to the unknown s ∈ (0, 2]) can be constructed by combining Mondrian Forests with a standard model aggregation algorithm. These results are the first demonstrating that some particular random forests achieve minimax rates in arbitrary dimension. Owing to their remarkably simple distributional properties, which lead to minimax rates, Mondrian trees are a promising basis for more sophisticated yet theoretically sound random forests variants.
No-Signalling-Assisted Zero-Error Capacity of Quantum Channels and an Information Theoretic Interpretation of the Lovász Number

We study the one-shot zero-error classical capacity of a quantum channel assisted by quantum no-signalling correlations, and the reverse problem of exact simulation of a prescribed channel by a noiseless classical one. Quantum no-signalling correlations are viewed as two-input and two-output completely positive and trace preserving maps with linear constraints enforcing that the device cannot signal. Both problems lead to simple semidefinite programmes (SDPs) that depend only on the Choi-Kraus (operator) space of the channel. In particular, we show that the zero-error classical simulation cost is precisely the conditional min-entropy of the Choi-Jamiołkowski matrix of the given channel. The zero-error classical capacity is given by a similar-looking but different SDP; the asymptotic zero-error classical capacity is the regularization of this SDP, and in general, we do not know of any simple form. Interestingly, however, for the class of classical-quantum channels, we show that the asymptotic capacity is given by a much simpler SDP, which coincides with a semidefinite generalization of the fractional packing number suggested earlier by Aram Harrow. This finally results in an operational interpretation of the celebrated Lovász ϑ function of a graph as the zero-error classical capacity of the graph assisted by quantum no-signalling correlations, the first information theoretic interpretation of the Lovász number.
Mutual information of IID complex Gaussian signals on block Rayleigh-faded channels

We present a method to compute, quickly and efficiently, the mutual information achieved by an IID (independent identically distributed) complex Gaussian input on a block Rayleigh-faded channel without side information at the receiver. The method accommodates both scalar and MIMO (multiple-input multiple-output) settings. Operationally, the mutual information thus computed represents the highest spectral efficiency that can be attained using standard Gaussian codebooks. Examples are provided that illustrate the loss in spectral efficiency caused by fast fading and how that loss is amplified by the use of multiple transmit antennas. These examples are further enriched by comparisons with the channel capacity under perfect channel-state information at the receiver, and with the spectral efficiency attained by pilot-based transmission.
Zero-Delay Joint Source-Channel Coding for a Multivariate Gaussian on a Gaussian MAC

In this paper, communication of a Multivariate Gaussian over a Gaussian Multiple Access Channel is studied. Distributed zero-delay joint source-channel coding (JSCC) solutions to the problem are given. Both nonlinear and linear approaches are discussed. The performance upper bound (signal-to-distortion ratio) for arbitrary code length is also derived and Zero-delay cooperative JSCC is briefly addressed in order to provide an approximate bound on the performance of zero-delay schemes. The main contribution is a nonlinear hybrid discrete-analog JSSC scheme based on distributed quantization and a linear continuous mapping named Distributed Quantizer Linear Coder (DQLC). The DQLC has promising performance which improves with increasing correlation, and is robust against variations in noise level. The DQLC exhibits a constant gap to the performance upper bound as the signal-to-noise ratio (SNR) becomes large for any number of sources and values of correlation. Therefore it outperforms a linear solution (uncoded transmission) in any case when the SNR gets sufficiently large.
Adapting Deep Network Features to Capture Psychological Representations: An Abridged Report

Deep neural networks have become increasingly successful at solving classic perception problems (e.g., recognizing objects), often reaching or surpassing human-level accuracy. In this abridged report of Peterson et al. [2016], we examine the relationship between the image representations learned by these networks and those of humans. We find that deep features learned in service of object classification account for a significant amount of the variance in human similarity judgments for a set of animal images. However, these features do not appear to capture some key qualitative aspects of human representations. To close this gap, we present a method for adapting deep features to align with human similarity judgments, resulting in image representations that can potentially be used to extend the scope of psychological experiments and inform human-centric AI.
Late Holocene freshening of the Baltic Sea derived from high-resolution strontium isotope analyses of mollusk shells

Strontium isotopic composition ( 87 Sr/ 86 Sr) data from subfossil 14 C-dated mollusk shells in raised beach sediments are used as a paleosalinity proxy in the brackish Baltic Sea, the precision (±5%) and accuracy (±0.7‰) of the method being judged from replicate analyses of modern shells. Paleosalinity data with an average time resolution of ∼200 yr for the period 7130–2775 calibrated 14 C yr B.P. indicate maximum surface salinities of 10‰–11‰, 11‰–12‰, and 12‰–13‰ for the Bothnian Bay, Bothnian Sea, and Baltic Proper (the three major Baltic subbasins). The relative salinity differences between the basins were small (≤30%) compared to the as much as eightfold present-day relative salinity differences (Bothnian Bay 1‰–3‰; Bothnian Sea 4‰–5‰; Baltic Proper 6‰–8‰). Late Holocene freshening (ca. 3000 calibrated 14 C yr B.P. to present) is most pronounced in the northernmost subbasin, the Bothnian Bay, consistent with the absence of a permanent halocline, sequestering of phosphorus in well-oxygenated bottom sediments, and phosphorus limitation of primary production in the present-day Bothnian Bay. This study suggests that paleosalinity data may be crucial to improving our understanding of the possible effects of any future, climate-induced freshening of the Baltic Sea.
DESIGN OF THE HIGH PLAINS EXPERIMENT WITH SPECIFIC FOCUS ON PHASE 2, SINGLE CLOUD EXPERIMENTATION

B. Ackerman: I n t e g r a t i o n of Con t r i bu t i ons and Hypotheses G. L. Achtemeier : P red i c t o r Var iab les H. Appleman: Review of Seeding Experiments S. A. Changnon: User I n t e r a c t i o n s and Soclo-Economic Studies F. A. Hu f f : P r e c i p i t a t i o n Measurement G. M. Morgan: Seeding Techniques, Rada r -Ra in fa l l Measurement P. T. Schickedanz: S t a t i s t i c a l Design, P r e d i c t o r Var iab les R. G. Semonin: Operat ions
Interval type-2 Beta Fuzzy Near set based approach to content based image retrieval

In an automated search system, similarity is a key concept in solving a human task. Indeed, human process is usually a natural categorization that underlies many natural abilities such as image recovery, language comprehension, decision making, or pattern recognition. In the image search axis, there are several ways to measure the similarity between images in an image database, to a query image. Image search by content is based on the similarity of the visual characteristics of the images. The distance function used to evaluate the similarity between images depends on the criteria of the search but also on the representation of the characteristics of the image; this is the main idea of the near and fuzzy sets approaches. In this article, we introduce a new category of beta type-2 fuzzy sets for the description of image characteristics as well as the near sets approach for image recovery. Finally, we illustrate our work with examples of image recovery problems used in the real world.
Low-Density Lattice Codes

Low-density lattice codes (LDLC) are novel lattice codes that can be decoded efficiently and approach the capacity of the additive white Gaussian noise (AWGN) channel. In LDLC a codeword x is generated directly at the n-dimensional Euclidean space as a linear transformation of a corresponding integer message vector b, i.e., x = Gb-1, where H = G-1 is restricted to be sparse. The fact that H is sparse is utilized to develop a linear-time iterative decoding scheme which attains, as demonstrated by simulations, good error performance within ~0.5 dB from capacity at block length of n =100,000 symbols. The paper also discusses convergence results and implementation considerations.
Acceleration of a Full-scale Industrial CFD Application with OP 2 I .

Hydra is a full-scale industrial CFD application used for the design of turbomachinery at Rolls Royce plc. , capable of performing complex simulations over highly detailed unstructured mesh geometries. Hydra presents major challenges in data organization and movement that need to be overcome for continued high performance on emerging platforms. We present research in achieving this goal through the OP2 domain-specific high-level framework, demonstrating the viability of such a high-level programming approach. OP2 targets the domain of unstructured mesh problems and enables execution on a range of back-end hardware platforms. We chart the conversion of Hydra to OP2, and map out the key difficulties encountered in the process. Specifically we show how different parallel implementations can be achieved with an active library framework, even for a highly complicated industrial application and how different optimizations targeting contrasting parallel architectures can be applied to the whole application, seamlessly, reducing developer effort and increasing code longevity. Performance results demonstrate that not only the same runtime performance as that of the hand-tuned original code could be achieved, but it can be significantly improved on conventional processor systems, and many-core systems. Our results provide evidence of how high-level frameworks such as OP2 enable portability across a wide range of contrasting platforms and their significant utility in achieving high performance without the intervention of the application programmer.
Gray-box Inference for Structured Gaussian Process Models : Supplementary Material

1 Proof of Theorem 2 Here we proof the result that we can estimate the expected log likelihood and its gradients using expectations over low-dimensional Gaussians, that is that Theorem. For the structured gp model defined in our paper the expected log likelihood over the given variational distribution and its gradients can be estimated using expectations over Tn-dimensional Gaussians and |V|-dimensional Gaussians, where Tn is the length of each sequence and |V| is the vocabulary size. 1.1 Estimation of Lell in the full (non-sparse) model For the Lell we have that: Lell = 〈 Nseq ∑
Information Equals Amortized Communication

We show how to efficiently simulate the sending of a single message M to a receiver who has partial information about the message, so that the expected number of bits communicated in the simulation is close to the amount of additional information that the message reveals to the receiver. This is a generalization and strengthening of the Slepian-Wolf theorem, which shows how to carry out such a simulation with low amortized communication in the case that M is a deterministic function of X. A caveat is that our simulation is interactive. As a consequence, we prove that the internal information cost (namely the information revealed to the parties) involved in computing any relation or function using a two party interactive protocol is exactly equal to the amortized communication complexity of computing independent copies of the same relation or function. We also show that the only way to prove a strong direct sum theorem for randomized communication complexity is by solving a particular variant of the pointer jumping problem that we define. This paper implies that a strong direct sum theorem for communication complexity holds if and only if efficient compression of communication protocols is possible. In particular, together with our result, a recent result of Ganor, Kol, and Raz implies that the strongest version of direct sum for randomized communication complexity is false.
Tectonic insight based on anisotropy of magnetic susceptibility and compaction studies in the Sierras Australes thrust and fold belt (southwest Gondwana boundary, Argentina)

The Sierras Australes fold and thrust belt (Buenos Aires Province, Argentina) was in the southwestern Gondwanaland margin during the Paleozoic. The Tunas Formation (Permian) is exposed along the eastern part of it and continues eastward beneath the Claromecó Basin. Anisotropy of magnetic susceptibility (AMS) and compaction studies are described and compared with previous paleomagnetic studies with the aim of determining direction and magnitude of the main stresses acting during the sedimentation of the Tunas Formation. The anisotropy ellipsoids are triaxial with oblate or prolate shapes, reflecting different stages of layer parallel shortening during the evolution of the basin. Kmax axes trend NW‐SE, parallel to the fold axes, while Kmin move from a horizontal (base) to a vertical orientation at the top of the succession, showing a change from a tectonic to almost a sedimentary fabric. The magnitude of anisotropy and compaction degree decreases toward the top of the succession. The AMS results are consistent with the outcrop structural observations and the compaction and paleomagnetic data. Regional pattern indicates a compression from the SW along this part of Gondwana, with a migration of the orogenic front and attenuation toward the NE in the foreland basin during the Upper Paleozoic. This deformation, locally assigned to the San Rafael noncollisional orogenic phase, is the result of the latitudinal movements toward the Equator of Gondwana (southern plates) and Laurentia (northern plates) during the Permian. This movement is the result of a rearrangement of the microplates that collided with Gondwana during the Late Devonian, to configure Pangea during the Triassic.
Asymptotically Near-Optimal RRT for Fast, High-Quality Motion Planning

We present lower bound tree-RRT (LBT-RRT), a single-query sampling-based motion-planning algorithm that is asymptotically near-optimal. Namely, the solution extracted from LBT-RRT converges to a solution that is within an approximation factor of 1 + ε of the optimal solution. Our algorithm allows for a continuous interpolation between the fast RRT algorithm and the asymptotically optimal RRT* and RRG algorithms when the cost function is the path length. When the approximation factor is 1 (i.e., no approximation is allowed), LBT-RRT behaves like RRG. When the approximation factor is unbounded, LBT-RRT behaves like RRT. In between, LBT-RRT is shown to produce paths that have higher quality than RRT would produce and run faster than RRT* would run. This is done by maintaining a tree that is a subgraph of the RRG roadmap and a second, auxiliary graph, which we call the lower-bound graph. The combination of the two roadmaps, which is faster to maintain than the roadmap maintained by RRT*, efficiently guarantees asymptotic near-optimality. We suggest to use LBT-RRT for high-quality anytime motion planning. We demonstrate the performance of the algorithm for scenarios ranging from 3 to 12 degrees of freedom and show that even for small approximation factors, the algorithm produces high-quality solutions (comparable with RRG and RRT*) with little running-time overhead when compared with RRT.