Miracle-dz/UniFAR-FTUs huggingface dataset:
Fractional cointegration in the presence of linear trends

Abstract. We consider bivariate regressions of nonstationary fractionally integrated variables dominated by linear time trends. The asymptotic behaviour of the ordinary least square (OLS) estimators in this case allows limiting normality to arise at a faster rate of convergence than if the individual series were detrended, increasing in this way the power of the tests for fractional cointegration. We also show that the limiting distribution of the t‐ratio of the slope coefficient depends upon the presence or not of a deterministic trend in the conditional regressor. We introduce the concept of local fractional trend to explain the apparently diverging asymptotic theories that apply when a trend is either present or absent in our set‐up.
Inference for Neural Network Predictive Models with Impulse Interventions

Neural Networks (NN) have demonstrated remarkable time series fitting and prediction abilities, outperforming in several applications other methods and particularly linear models, such as dynamic linear regression. However, due to their nature, NNs are not easy to interpret and are often considered as black box models. The importance of each independent variable is hard to estimate and therefore test whether they have significant explanatory power and hence be included in the model or not. This task is very important for several applications, where the effect of each variable has to be identified, such as marketing modelling and analysis, where the effectiveness of different marketing instruments has to be estimated, commonly modelled as impulse interventions. Statistical inference in these cases is sought, hindering the use of NNs. This paper proposes a framework to allow statistical inference of impulse interventions modelled with NNs. The effects of interventions are estimated and tested for statistical significance. Using a Monte Carlo simulation the power of the proposed test is compared with dynamic linear regression models. The power is found to be higher and the estimation of the simulated effects is more accurate. Based on this framework strategies to code multiple impulses with NNs are discussed.
Segmentation and Impression Estimation of Novels for Automatic Assignment of BGM

In this paper, we proposed a BGM assignment method to story segments using emotion dictionary. For segmentation of a story, we used information of characters appearing in a story. Changes of appearing characters and characters' sentiment information are used for segmentation. We prepared syntactic patterns for identification of characters and extraction method of sentiment information. For assignment of BGM to story segments, we extracted sentiment descriptions from story segments and recognized sentiment type of the segments using Emotion Description Dictionary. Detected sentiment information of a segment will be used to generate emotion vector. In the experiments, accuracy of segmentation was not good enough, but we could obtain helpful direction to improve our method, which is to use time and space information. For sentiment information detection, emotion vector model worded well although there are some problems in pattern patching of sentiment expression using Emotion Description Dictionary.
A Novel Group Recommendation Algorithm with Collaborative Filtering

Traditional recommender systems are designed to provide suggestions for individuals. However, there are scenarios in which groups of people are in need of decision support. For example, a group of friends want to choose a restaurant to have a dinner or to watch a movie together. In this paper, we propose a novel group recommendation algorithm for providing suggestions to groups. The proposed algorithm can be divided into two steps: the first step is to predict the preference of the unwatched items for each group members, which is a personalized prediction progress, then, it provides the recommendations for the group by aggregating group members' preferences, which mainly concerns the preferences of members who haven't seen the items. Without complex computation, the proposed algorithm can make accurate predictions of each item for group members. We demonstrate our algorithm on a famous dataset called Movie Lens and use the recall, the precision metrics and a combination of them to evaluate its performance. The experimental results show that the proposed algorithm can provide high quality group recommendations.
CD74 and intratumoral immune response in breast cancer

CD74 (invariant chain) plays a role in MHC class II antigen presentation. We assessed CD74 and MHCII expression in tumor cells, as well as CD8, CD4, and CD68 tumor infiltrating leucocyte (TIL) density by immunohistochemistry in a cohort of 492 breast cancer patients. CD74 expression was associated with poor prognostic markers including patient age, tumor grade, ER status, non-Luminal A subtypes, and with MHCII expression and higher TIL densities, particularly in the Basal-like subgroup. Univariate analysis showed a favorable prognostic effect of CD74 (Hazard ratio = 0.46, 95% CI = 0.26–0.89, p = 0.022) and for combined CD74/MHCII (Hazard ratio = 0.26, 95% CI = 0.17–0.81, p = 0.014) positive status for overall survival that was only manifested in the Basal-like subgroup. CD74 and MHCII expression is associated with patient survival in Basal-like breast cancer, and the association with TIL may reflect an effective intratumoral immune response.
Feeding performance by sympatric blue and fin whales exploiting a common prey resource

ARI S. FRIEDLAENDER, Marine Mammal Institute, Hatfield Marine Science Center, Oregon State University, 2035 Marine Science Drive, Newport, Oregon 97365, U.S.A. and Southall Environmental Associates, Inc. 9099 Soquel Drive, Suite 8, Aptos, California 95003, U.S.A. ; JEREMY A. GOLDBOGEN, Department of Biology, Hopkins Marine Station, Stanford University, Pacific Grove, California 93950, U.S.A. ; ELLIOTT L. HAZEN, UC Santa Cruz–Long Marine Laboratory, Institute of Marine Sciences, 100 Shaffer Road, Santa Cruz, California 95060, U.S.A. and NOAA Southwest Fisheries Science Center, Environmental Research Division, 1352 Lighthouse Ave., Pacific Grove, California 93950, U.S.A. ; JOHN CALAMBOKIDIS, Cascadia Research Collective, 2181⁄2 W. 4th Avenue, Olympia, Washington 98501, U.S.A. ; BRAN-
Particle Swarm Optimization for Control of Nonlinear Dynamics

Chaos control refers to any form of manipulation of chaotic dynamical behavior exhibited by complex nonlinear systems. A chaotic system in general cannot be made to converge to a freely evolving desired trajectory, whether periodic or chaotic, because of the inherent unpredictability of the system. The control of chaos in this context consists of forcing the system to evolve along the desired trajectory. Control of these systems is difficult because in these systems the natural tendency of nearby trajectories is to diverge exponentially in phase space, due to the sensitive dependence on initial conditions. A new approach to particle motion in swarm optimization is developed for control of chaotic systems
Advancing Front Surface Mesh Generation in Parametric Space Using a Riemannian Surface Definition

A method is presented for meshing 3D CAD surfaces in parametric space using an advancing front approach and a metric map to govern the size and shape of the triangles in the parametric space. The creation of the metric map will be discussed. The advancing front mesher generates triangles based on the metric map, stretching them in order to capture the change in parameterization of the surface. The benefits of this algorithm include better quality elements without having to do costly real space calculations.
Differential expression of cyclin-dependent kinase inhibitors in human glomerular disease: role in podocyte proliferation and maturation.

BACKGROUND Normal human podocytes are terminally differentiated and quiescent cells. It is not known why podocytes fail to proliferate in response to most forms of injury. Proliferation is regulated by cell cycle proteins and their inhibitors. The Cip/Kip family of cyclin-dependent kinase (CDK) inhibitors (p21, p27, p57) in general prevent proliferation by inhibiting cyclin-CDK complexes. In the current study, we determined the expression and possible role of specific CDK inhibitors in podocyte proliferation in human disease characterized by podocyte injury. METHODS Immunostaining was performed for the CDK inhibitors p21, p27, and p57 and the proliferation marker Ki-67 on renal biopsies from patients with minimal change disease (MCD; N = 6), membranous glomerulopathy (MGN; N = 19), cellular variant of focal segmental glomerulosclerosis (FSGS; N = 12), collapsing glomerulopathy (CG; N = 9), and HIV-associated nephropathy (HIVAN; N = 16). Adult nephrectomy specimens without evidence of glomerular disease served as controls (N = 9). RESULTS Normal quiescent podocytes express p27 and p57, but not p21. In diseases without podocyte proliferation (MCD, MGN), p21, p27, and p57 expression did not change. In contrast, there was a uniform decrease in p27 and p57 immunostaining in diseases with podocyte proliferation (cellular FSGS, CG, and HIVAN). This was accompanied by the de novo expression of p21 in podocytes. CONCLUSIONS Our results show that podocyte quiescence may require the presence of the CDK inhibitors p27 and p57. In human glomerular diseases, a decrease in p27 and p57 may be permissive for the altered proliferative podocyte phenotype. p21 may have a multifactorial role in podocyte cell cycle regulation.
Classification of incomplete data using classifier ensembles

This paper proposes a method for classification of incomplete data using neural network ensembles. In the method, the incomplete data set is analyzed and projected into a group of complete data subsets that give a full description of the known values in the data set by joining together. Those complete data subsets are then used as the training sets for the neural networks. Base classifiers are selected and integrated according to their classification accuracies and the support degrees of their training data sets to give the final predication. Compared with other methods dealing with missing data in classification, the proposed method can utilize all the information provided by the incomplete data, maintain maximum consistency of the incomplete data set and avoid the dependency on distribution or model assumptions. Experiments on two UCI datasets showed the superiority of the algorithm to other two typical treatments of missing data in ensemble learning.
A robust blind image watermarking scheme based on feature points and rs-invariant domain

A robust blind image watermarking scheme based on feature points and RS-invariant domain is proposed. Our goal is to detect the watermark without any prior knowledge about the original image and the suffered distortions. We address the robustness against both geometric distortions and signal processing attacks. To resist the cropping and the translation, the idea of the localized watermarking is introduced and the disks centered at the feature points by using the Harris detector are used to embed the watermark. To resist the rotation and the scaling, the watermark is embedded into the RS-invariant domain obtained by taking the magnitude of the 1-dimension DFT along the angular axis of the log-polar mapping (LPM) image repeatedly for each disk. Simulation results show that the proposed scheme is robust against both geometric distortions and signal processing attacks
A Model Architecture for Mental Imagery

Imagination or mental imagery is the capability of generating sensory experiences without actual sensory inflow. These sensory experiences might be the results of actions that are not carried out physically but only simulated within the cognitive system of an agent. We propose a model architecture for mental imagery that enables an artificial agent to generate views of parts of himself based on a set of given pose parameters and according to a specific viewing direction. The architecture is evaluated in a robotic study with an agent consisting of a robotic manipulator and a camera head. The agent adaptively learns to associate views of its gripper with its current pose. Furthermore, a visual forward model predicts visual changes according to the agent’s gaze direction.
Experimental assessment of TCP performance over 802 . 11 b

This paper investigates performance behaviour of TCP sessions when supported over IEEE 802.11b Wireless LANs. We emphasize the interaction between uplink/downlink TCP fairness and link layer parameters or Access Point buffer provisioning. The novel aspect is that this investigation is fully made in an experimental environment. We prove that TCP suffers of some inequalities that derive to unfair bandwidth sharing between uplink and downlink. Our extensive experimental analysis shows the main effects of these inequalities on the TCP behavior and highlights some performance anomalies that are difficult to be measured via simulations. A simple, but effective, scheduling mechanism is then provided within the Access Point to alleviate the unfairness.
Dialogue context sensitive HMM-based speech synthesis

The focus of this work is speech synthesis tailored to the needs of spoken dialogue systems. More specifically, the framework of HMM-based speech synthesis is utilized to train an emphatic voice that also considers dialogue context for decision tree state clustering. To achieve this, we designed and recorded a speech corpus comprising system prompts from human-computer interaction, as well as additional prompts for slot-level emphasis. This corpus, combined with a general purpose text-to-speech one, was used to train voices using a) baseline context features, b) additional emphasis features, and c) additional dialogue context features. Both emphasis and dialogue context features are extracted from the dialogue act semantic representation. The voices were evaluated in pairs for dialogue appropriateness using a preference listening test. The results show that the emphatic voice is preferred to the baseline when emphasis markup is present, while the dialogue context-sensitive voice is preferred to the plain emphatic one when no emphasis markup is present and preferable to the baseline in both cases. This demonstrates that including dialogue context features for decision tree state clustering significantly improves the quality of the synthetic voice for dialogue.
Using Static Code Features for Intelligent Squash Prediction

Thread-level speculation (TLS) is widely accepted to be a realistic model for execution of sequential programs on multi-core architectures. One problem with TLS occurs when large numbers of spawned threads are squashed due to data dependence violations. This can reduce or entirely obliterate the performance benefits of TLS. Until now, informal compiler heuristics or high-overhead runtime profiling have been used to identify likely squashes and suppress thread spawns at the appropriate program locations. In this paper, we adopt an alternative approach using machine learning to construct squash predictors based on static code features. On a set of standard Java benchmarks, we achieve a mean speedup that is 82% of the maximum speedup available from an oracle predictor.
Twisting cocycles in fundamental representation and triangular bi-crossproduct Hopf algebras

We find the general solution to the twisting equation in the tensor bialgebra T(R) of an associative unital ring R viewed as that of fundamental representation for a universal enveloping Lie algebra and its quantum deformations. We suggest a procedure of constructing twisting cocycles belonging to a given quasitriangular sub-bialgebra H⊂T(R). This algorithm generalizes Reshetikhin’s approach, which involves cocycles fulfilling the Yang–Baxter equation. Within this framework we study a class of quantized inhomogeneous Lie algebras related to associative rings in a certain way, for which we build twisting cocycles and universal R-matrices. Our approach is a generalization of the methods developed for the case of commutative rings in our recent work including such well-known examples as Jordanian quantization of the Borel subalgebra of sl(2) and the null-plane quantized Poincare algebra by Ballesteros et al. We reveal the role of special group 1-cocycles in this process and establish the bi-crossproduct stru.
A Spline Collocation Scheme for the Spherical Shallow Water Equations

We present a spline collocation scheme for the numerical solution of the shallow water equations in spherical geometry. Space is discretized by bilinear spherical splines, adapted to a reduced longitude-latitude grid. The poles are not exceptional points in this approach. Time is discretized by standard centered diierences. We obtain a fully explicit and very eecient scheme that reduces to simple matrix-vector multiplications in every time step. No linear systems have to be inverted. The scheme is applied to a standard test set 13] and its accuracy is compared to recent results 1] that document the implementation of a preliminary version of the new global model of German Weather Service (DWD).
Regulation of Stearoyl-CoA Desaturase 1 mRNA Stability by Polyunsaturated Fatty Acids in 3T3-L1 Adipocytes*

The effects of arachidonic acid (20:4, n-6) and other fatty acids on the expression of stearoyl-CoA desaturase gene 1 were investigated in fully differentiated 3T3-L1 adipocytes. Treatment of 3T3-L1 adipocytes with arachidonic acid resulted in a decrease in stearoyl-CoA desaturase (Scd) enzyme activity and scd1 mRNA. Arachidonic acid did not alter the transcription of the scd1 gene, whereas the half-life of the scd1 mRNA was reduced from 25.1 to 8.5 h. Blocking the conversion of arachidonic acid to eicosanoids by pretreatment of the cells with cyclooxygenase, lipoxygenase, or cytochrome P-450 epoxygenase inhibitors did not reverse the inhibition caused by arachidonic acid, indicating that eicosanoid synthesis is not necessary for the repression of scd1 mRNA expression. Treatment of adipocytes with linoleic (18:2, n-6) and linolenic (18:3, n-3) acids also resulted in inhibition of scd1 mRNA accumulation. By contrast, oleic acid (18:1, n-9) and stearic acid (18:0) had no effect on scd1 mRNA levels. Taken together, these results suggest that polyunsaturated fatty acids repress the expression of the scd1 gene in mature adipocytes by reducing the stability of scd1 mRNA.
Combining constraint processing and pattern matching to describe and locate structured motifs in genomic sequences.

In molecular biology and bioinformatics, searching RNA gene occurrences in genomic sequences is a task whose importance has been renewed by the recent discovery of numerous functional RNA, often interacting with other ligands. Even if several programs exist for RNA motif search, no program exists that can represent and solve the problem of searching for occurrences of RNA motifs in interaction with other molecules. In this paper, we present a CSP formulation of this problem. We represent such RNA as structured motifs that occur on more than one sequence and which are related together by possible hybridization. Together with pattern matching algorithms, constraint satisfaction techniques have been implemented in a prototype MilPat and applied to search for tRNA and snoRNA genes on genomic sequences. Results show that these combined techniques allow to efficiently search for interacting motifs in large genomic sequences and offer a simple and extensible framework to solve such problems.
Sensitivity-driven co-synthesis of distributed embedded systems

Describes a new, sensitivity-driven algorithm for the co-synthesis of real-time distributed embedded systems. Many embedded computing systems are distributed systems: communicating periodic processes executing on several CPUs/ASICs connected by communication links. We use performance estimates to compute a local sensitivity of the design to process allocation. We propose a priority prediction method to schedule processes. Based on these techniques, we develop a gradient-search algorithm which co-synthesizes heterogeneous distributed systems of arbitrary topology and the associated application software architecture. Experimental results show that our algorithm can find good implementation architectures in small amounts of CPU time.
Computational Bundling for Auctions (CMU-CS-13-111)

Revenue maximization in combinatorial auctions (and other multidimensional selling settings) is one of the most important and most elusive problems in mechanism design. The design problem is NP-complete, and the optimal designs include features that are not acceptable in many applications, such as favoring some bidders over others and randomization. In this paper, we instead study a common revenue-enhancement approach bundling in the context of the most commonly studied combinatorial auction mechanism, the Vickrey-Clarke-Groves (VCG) mechanism. A second challenge in mechanism design for combinatorial auctions is that the prior distribution on each bidder’s valuation can be doubly exponential. Such priors do not exist in most applications. Rather, in many applications (such as premium display advertising markets), there is essentially a point prior, which may not be accurate. We adopt the point prior model, and prove robustness to inaccuracy in the prior. Then, we present a branch-and-bound framework for finding the optimal bundling. We introduce several techniques for branching, upper bounding, lower bounding, and lazy bounding. Experiments on CATS distributions validate the approach and show that our techniques dramatically improve scalability over a leading general-purpose MIP solver.
Scheduling Dataflow Execution Across Multiple Accelerators

Dataflow execution engines such as MapReduce, DryadLINQ and PTask have enjoyed success because they simplify development for a class of important parallel applications. Expressing the computation as a dataflow graph allows the runtime, and not the programmer, to own problems such as synchronization, data movement and scheduling leveraging dynamic information to inform strategy and policy in a way that is impossible for a programmer who must work only with a static view. While this vision enjoys considerable intuitive appeal, the degree to which dataflow engines can implement performance profitable policies in the general case remains under-evaluated. We consider the problem of scheduling in a dataflow engine on a platform with multiple GPUs. In this setting, the cost of moving data from one accelerator to another must be weighed against the benefit of parallelism exposed by performing computations on multiple accelerators. An ideal runtime would automatically discover an optimal or near-optimal partitioning of computation across the available resources with little or no user input. The wealth of dynamic and static information available to a scheduler in this context makes the policy space for scheduling dauntingly large. We present and evaluate a number of approaches to scheduling and partitioning dataflow graphs across available accelerators. We show that simple throughputor localitypreserving policies do not always do well. For the workloads we consider, an optimal static partitioner operating on a global view of the dataflow graph is an attractive solution either matching or out-performing a hand-optimized schedule. Application-level knowledge of the graph can be leveraged to achieve best-case performance.