Miracle-dz/UniFAR-FTUs huggingface dataset:
A Proximal-Point Algorithm with Variable Sample-Sizes (PPAWSS) for Monotone Stochastic Variational Inequality Problems

We consider a stochastic variational inequality (SVI) problem with a continuous and monotone mapping over a closed and convex set. In strongly monotone regimes, we present a variable sample-size averaging scheme (VS-Ave) that achieves a linear rate with an optimal oracle complexity. In addition, the iteration complexity is shown to display a muted dependence on the condition number compared with standard variance-reduced projection schemes. To contend with merely monotone maps, we develop amongst the first proximal-point algorithms with variable sample-sizes (PPAWSS), where increasingly accurate solutions of strongly monotone SVIs are obtained via (VS-Ave) at every step. This allows for achieving a sublinear convergence rate that matches that obtained for deterministic monotone VIs. Preliminary numerical evidence suggests that the schemes compares well with competing schemes.
Learning to Align Multi-Camera Domain for Unsupervised Video Person Re-Identification

Most video person re-identification (re-ID) methods are mainly based on supervised learning, which requires laborious cross-camera ID labeling. Due to this limit, it is difficult to increase the number of cameras for constructing a large camera network. In this paper, we address the person ID labeling issue by presenting novel deep representation learning without ID information across multiple cameras. Specifically, our method consists of both inter- and intra camera feature learning techniques. We maximize feature distances between people within a camera. At the same time, considering each camera as a different domain, we apply domain adversarial learning across multiple camera views for minimizing camera domain discrepancy. To further enhance our approach, we propose person part-level adaptation to effectively perform multi-camera domain invariant feature learning at different spatial regions. We carry out comprehensive experiments on four public re-ID datasets (i.e., PRID-2011, iLIDS-VID, MARS, and Market1501). Our method outperforms state-of-the-art methods by a large margin of about 20% in terms of rank-1 accuracy on the large-scale MARS dataset.
A Distributed-Parameter Control System Using Electromagnetic Images Stimulation for Human-Machine Perception Interface

This article develops a new human-machine perception interface method to convert visual patterns to accurate eddy-current stimulation using an electromagnet (EM) array. The eddy-current stimulation is formulated as a feedforward controller design. In this paper, a state-space model for the eddy-current stimulation is derived for design and analysis of the controller. Unlike traditional methods where the distributed parameter systems are often modeled using partial differential equations and solved numerically using numerical methods such as finite element analysis, the model presented here offers closed-form solutions in state-space representation. The novel approach enables the applications of the well-established control theory for analyzing the system controllability. The feasibility and accuracy of the feedforward control method are numerically illustrated and validated by generating the stimulation with two types of patterns, which provides an essential base for future research of human-machine perception interface.
Cross-Modal Subspace Learning with Scheduled Adaptive Margin Constraints

Cross-modal embeddings, between textual and visual modalities, aim to organise multimodal instances by their semantic correlations. State-of-the-art approaches use maximum-margin methods, based on the hinge-loss, to enforce a constant margin m, to separate projections of multimodal instances from different categories. In this paper, we propose a novel scheduled adaptive maximum-margin (SAM) formulation that infers triplet-specific constraints during training, therefore organising instances by adaptively enforcing inter-category and inter-modality correlations. This is supported by a scheduled adaptive margin function, that is smoothly activated, replacing a static margin by an adaptively inferred one reflecting triplet-specific semantic correlations while accounting for the incremental learning behaviour of neural networks to enforce category cluster formation and enforcement. Experiments on widely used datasets show that our model improved upon state-of-the-art approaches, by achieving a relative improvement of up to ~12.5% over the second best method, thus confirming the effectiveness of our scheduled adaptive margin formulation.
Dual Sequential Monte Carlo: Tunneling Filtering and Planning in Continuous POMDPs

We present the DualSMC network that solves continuous POMDPs by learning belief representations and then leveraging them for planning. It is based on the fact that filtering, i.e. state estimation, and planning can be viewed as two related sequential Monte Carlo processes, with one in the belief space and the other in the future planning trajectory space. In particular, we first introduce a novel particle filter network that makes better use of the adversarial relationship between the proposer model and the observation model. We then introduce a new planning algorithm over the belief representations, which learns uncertainty-dependent policies. We allow these two parts to be trained jointly with each other. We testify the effectiveness of our approach on three continuous control and planning tasks: the floor positioning, the 3D light-dark navigation, and a modified Reacher task.
Using Machine Learning to Construct Velocity Fields from OH-PLIF Images

ABSTRACT This work utilizes data-driven methods to morph a series of time-resolved experimental OH-PLIF images into corresponding three-component planar PIV fields in the closed domain of a premixed swirl combustor. The task is carried out with a fully convolutional network, which is a type of convolutional neural network (CNN) used in many applications in machine learning, alongside an existing experimental dataset which consists of simultaneous OH-PLIF and PIV measurements in both attached and detached flame regimes. Two types of models are compared: 1) a global CNN which is trained using images from the entire domain, and 2) a set of local CNNs, which are trained only on individual sections of the domain. The locally trained models show improvement in creating mappings in the detached regime over the global models. A comparison between model performance in attached and detached regimes shows that the CNNs are much more accurate across the board in creating velocity fields for attached flames. Inclusion of time history in the PLIF input resulted in small noticeable improvement on average, which could imply a greater physical role of instantaneous spatial correlations in the decoding process over temporal dependencies from the perspective of the CNN. Additionally, the performance of local models trained to produce mappings in one section of the domain is tested on other, unexplored sections of the domain. Interestingly, local CNN performance on unseen domain regions revealed the models’ ability to utilize symmetry and antisymmetry in the velocity field. Ultimately, this work shows the powerful ability of the CNN to decode the three-dimensional PIV fields from input OH-PLIF images, providing a potential groundwork for a very useful tool for experimental configurations in which accessibility of forms of simultaneous measurements are limited.
The impact of thermal winds on the outburst lightcurves of black hole X-ray binaries

Context. The observed signatures of winds from X-ray binaries are broadly consistent with thermal winds, which are driven by X-ray irradiation of the outer accretion disc. Thermal winds produce mass outflow rates that can exceed the accretion rate in the disc. Aims. We aim to study the impact of thermal wind mass loss on the stability and lightcurves of black hole X-ray binaries subject to the thermal-viscous instability driving their outbursts. Strong mass loss could stop outbursts early, as proposed for the 2015 outburst of V404 Cyg. Methods. We used an analytical model for thermal (Compton) wind mass loss as a function of radius, X-ray spectrum, and luminosity that was calibrated against numerical simulations. We also estimated the fraction of the X-rays, emitted close to the compact object, that are scattered back to the outer disc in the wind. Scattering in the thermal wind couples irradiation to the disc size and inner mass accretion rate. The disc evolution equations were modified to include this wind mass loss and the varying irradiation fraction. Results. Scattering in the strong wind expected of long Porb systems enhances the irradiation heating of the outer disc, keeping it stable against the thermal-viscous instability. This accounts very well for the existence of persistently bright systems with large discs, such as Cyg X-2, 1E 1740.7−2942, or GRS 1758−258. Mass loss from the thermal wind shortens the outburst, as expected, but it is insufficient in explaining the rapid decay timescale of black-hole X-ray binary outbursts. However, including the wind-related varying irradiation fraction produces lightcurves with plateaus in long Porb systems like GRO J1655−40. Plateau lightcurves may be a dynamical signature of enhanced irradiation due to scattering in thermal winds. Conclusions. Mass loss due to thermal winds is not a major driver for the outburst dynamics up to luminosities of 0.1 − 0.2 LEdd. Higher luminosities may produce stronger mass loss but studying them is complicated since the wind becomes opaque. Magnetic winds, which extract angular momentum with little mass loss, seem more promising to explain the fast decay timescales generically seen in black-hole X-ray binaries. Thermal winds can play an important role in the outburst dynamics through the varying irradiation heating. This may be evidenced by relating changes in wind properties, X-ray spectra, or luminosity with changes in the optical emission that traces the outer disc. Simulations should enable more accurate estimates of the dependence of the irradiation onto the disc as a function of irradiation spectrum, radius, and disc wind properties.
Combinatorial relations on skew Schur and skew stable Grothendieck polynomials

We give a combinatorial expansion of the stable Grothendieck polynomials of skew Young diagrams in terms of skew Schur functions, using a new row insertion algorithm for set-valued semistandard tableaux of skew shape. This expansion unifies some previous results: it generalizes a combinatorial formula obtained in earlier joint work with L\'opez Mart\'in and Teixidor i Bigas concerning Brill-Noether curves, and it generalizes a 2000 formula of Lenart and a recent result of Reiner-Tenner-Yong to skew shapes. We also give an expansion in the other direction: expressing skew Schur functions in terms of skew Grothendieck polynomials.
The thermodynamics and phase transition of a rainbow black hole

In this work, we construct a new kind of rainbow functions, which has generalized uncertainty principle parameter. Then, we investigate modified thermodynamic quantities and phase transition of rainbow Schwarzschild black hole by employing this new kind of rainbow functions. Our results demonstrate that the rainbow gravity and generalized uncertainty principle have a great effect on the picture of Hawking radiation. They prevent black holes from total evaporation and cause a remnant. In addition, after analyzing the modified local thermodynamic quantities, we find that the effect of rainbow gravity and the generalized uncertainty principle lead to one first-order phase transition, two second-order phase transitions and two Hawking–Page-type phase transitions in the thermodynamic system of rainbow Schwarzschild black hole.
First-principles studies of spin-phonon coupling in monolayer 
Cr2Ge2Te6

We perform systematic first-principles calculations to investigate the spin-phonon coupling (SPC) of Cr2Ge2Te6 (CGT) monolayer (ML). It is found that the Eg phonon mode at 211.8 cm^{-1} may have a SPC as large as 3.19 cm^{-1}, as it directly alters the superexchange interaction along the Cr-Te-Cr pathway. Furthermore, the strength of SPC of the CGT ML can be further enhanced by an in-plane compressive strain. These results provide useful insights for the understanding of SPC in novel two-dimensional magnetic semiconductors and may guide the design of spintronic and spin Seebeck materials and devices.
On Representing and Eliciting Resilience Requirements of Microservice Architecture Systems

Together with the spread of DevOps practices and container technologies, Microserivce Architecture has become a mainstream architecture style in recent years. Resilience is a key characteristic in Microservice Architecture Systems(MSA Systems), and it shows the ability to cope with various kinds of system disturbances which cause degradations of services. However, due to lack of consensus definition of resilience in the software field, although many works have been done on resilience for MSA Systems, developers still don't have a clear idea on how resilient an MSA System should be, and what resilience mechanisms are needed. In this paper, by referring to existing systematic studies on resilience in other scientific areas, the definition of microservice resilience is provided and a Microservice Resilience Measurement Model is proposed to measure service resilience. And we give a requirement model to represent resilience requirements of MSA Systems. A process framework is also proposed to elicit MSA System resilience requirements. As a proof of concept, a case study is conducted on an MSA System to illustrate how the resilience requirements are elicited and represented.
Hunting Young White Dwarfs at the Center of Planetary Nebulae

We present Gemini-South observations of nine faint and extended planetary nebulae (PNe). Using direct images taken with the spectrograph Gemini-South multi-object spectrograph (GMOS), we built the versus diagrams of the stars in the observed areas which allowed us to consider their geometrical positions and identify the probable central stars of the nebulae. Our stellar spectra of seven stars, also taken with GMOS, indicate that four (and probably two more) objects are white dwarfs of the DAO subtype. Moreover, the white dwarf status of the four stars is confirmed by the parameters Teff and log g derived with the help of theoretical stellar spectra. Given this evidence, we propose that these hot stars are the central ionizing sources of the nebulae. With this work we hope to help improve the current scarce statistics on central white dwarfs in PNe.
Visualization tools for parameter selection in cluster analysis.

We propose an algorithm, HPREF (Hierarchical Partitioning by Repeated Features), that produces a hierarchical partition of a set of clusterings of a fixed dataset, such as sets of clusterings produced by running a clustering algorithm with a range of parameters. This gives geometric structure to such sets of clustering, and can be used to visualize the set of results one obtains by running a clustering algorithm with a range of parameters.
EPOSIT: An Absolute Pose Estimation Method for Pinhole and Fish-Eye Cameras

This paper presents a generic 6DOF camera pose estimation method, which can be used for both the pinhole camera and the fish-eye camera. Different from existing methods, relative positions of 3D points rather than absolute coordinates in the world coordinate system are employed in our method, and it has a unique solution. The application scope of POSIT (Pose from Orthography and Scaling with Iteration) algorithm is generalized to fish-eye cameras by combining with the radially symmetric projection model. The image point relationship between the pinhole camera and the fish-eye camera is derived based on their projection model. The general pose expression which fits for different cameras can be acquired by four noncoplanar object points and their corresponding image points. Accurate estimation results are calculated iteratively. Experimental results on synthetic and real data show that the pose estimation results of our method are more stable and accurate than state-of-the-art methods. The source code is available at this https URL.
Developing a Large-Scale Corpus for Natural Language Processing and Emotion Processing Research in Japanese

There is a lack of web-based corpora for Japanese language. Existing ones contain insufficient amount of data, consist of improper word segmentation, have no clear structure and are unsuitable for the needs of emotion processing research. In order to overcome the abovementioned problems we built a new corpus. It is based on blog entries from Ameba blogging service (http://ameblo.jp/). The corpus contains sentences from common language with a variety of expressions and writing styles, which is important for a number of tasks in NLP. The original structure (blog post and comments) is preserved, thanks to which semantic relations between posts and comments are maintained. The corpus is intended to serve as a base for compiling more detailed subcorpora. Its usability in this field was proven by using it to create emotion object corpus. The goal of this paper is to make reader aware of new linguistic resource that can be used in various NLP research projects.
Other Milestone 6 – Global and country specific prospective scenarios

innovation programme under grant agreement No 676754. Project Title Alternatives models and robust decision-making for future forest management Project Acronym ALTERFOR Project Coordinator Ljusk Ola Eriksson, Swedish University of Agricultural Sciences (SLU) Scientific Coordinator Vilis Brukas, Swedish University of Agricultural Sciences (SLU) Project Administrator Giulia Attocchi, Swedish University of Agricultural Sciences (SLU) Project Duration 1 April 2016 – 30 September 2020 Project Duration in months 54
Categorical Range Reporting with Frequencies

In this paper, we consider a variant of the color range reporting problem called color reporting with frequencies. Our goal is to pre-process a set of colored points into a data structure, so that given a query range Q, we can report all colors that appear in Q, along with their respective frequencies. In other words, for each reported color, we also output the number of times it occurs in Q. We describe an external-memory data structure that uses O(N(1 + log2 D/ logN)) words and answers one-dimensional queries in O(1 +K/B) I/Os, where N is the total number of points in the data structure, D is the total number of colors in the data structure, K is the number of reported colors, and B is the block size. Next we turn to an approximate version of this problem: report all colors σ that appear in the query range; for every reported color, we provide a constant-factor approximation on its frequency. We consider color reporting with approximate frequencies in two dimensions. Our data structure uses O(N) space and answers two-dimensional queries in O(logB N + log∗B +K/B) I/Os in the special case when the query range is bounded on two sides. As a corollary, we can also answer one-dimensional approximate queries within the same time and space bounds. 2012 ACM Subject Classification Theory of computation → Data structures design and analysis
Detection of mecA , fern A , and femB genes in clinical strains of staphylococci using polymerase chain reaction

MecA, a structural gene located on the chromosome of Staphylococcus aureus, characterizes methicillin-resistant S. aureus (MRSA), and femA and femB(fem) genes encode proteins which influence the level of methicillin resistance of S. aureus. In order to examine effectiveness of detecting mecA and fern genes in identification of MRSA, the presence of these genes in 237 clinically isolated strains of staphylococci was investigated by polymerase chain reaction (PCR). An amplified mecA DNA fragment of 533 base pairs (bp) was detected in 100% of oxacillin-resistant S. aureus, in 16-7 % of oxacillin-sensitive S. aureus, in 815 % of S. epidermidis, and in 58'3% of other coagulase-negative staphylococci (CNS). While the PCR product of femA (509 bp) or femB (651 bp) was obtained from almost all the S. aureus strains except for five oxacillin-resistant strains (2-5%), neither of these genes were detected in CNS. Therefore, the detection of femA and femB together with mecA by PCR was considered to be a more reliable indicator to identify MRSA by differentiating it from mecA -positive CNS than single detection of mecA.
An Efficient LES-Acoustic Coupling Method for Sound Generation and High Order Propagation from Jets

The use of a hybrid CFD-CAA method is becoming a standard approach for farfield jet noise prediction, due to its low computational cost compared with a direct compressible prediction. Hybrid CFD-CAA methods require the exchange of information between different codes. For 3D far-field propagation that relies on noise sources information, the amount of data that has to be sent can be enormous, since the size of the meshes is typically of the order of million of elements. Hence, the use of traditional file-based approaches to exchange the data between the codes is limited, as hundreds of terabytes for storing the noise sources can be reached and the scalability of the overall method is limited by the available I/O bandwidth. In this work, a coupling strategy is used in which all the necessary data is exchanged directly in memory by using an open-source library called CWIPI [1]. In addition, it includes an interpolation package that allows the use of different mesh topologies for different codes. The code used for the noise source calculation is a Large Eddy Simulations (LES) code that is use for industry design and academic research [2]. For the acoustic propagation, the solver used is APESolver, part of the high-order open-source code Nektar++ [3]. Encouraging results for combustion noise has been presented by Lackhove et al. [4] with a similar methodology.
Temporal Inference In Forward Search Temporal Planning

Forward search planners are typically good at using search mechanisms to find solutions to a problem. They do search by performing state evaluations and selecting a promising successor state to navigate to. These planners often use a relaxation to solve an easier form of the problem in each state evaluation, in order to attain heuristic guidance. A popular relaxation is the delete relaxation used by planners such as FF (Hoffmann and Nebel 2001). However, forward search planners can often find it difficult to make use of inference, and as a result lose the power that inference brings. Without inference, a planner needs to search for every action, even ones that could have been inferred without any search. This paper identifies patterns of required concurrency and provides an analysis of these patterns and the inferences that can be deduced from them. We discuss ideas of how these inferences can be implemented in a current forward search planner, POPF (Coles et al. 2010).
Compact Interface quasi-Newton algorithm for biomechanical applications in massively parallel computers

In this work we present aHPC version of the interface quasi Newton algorithm. This algorithm is used to couple staggered fluid-structure interaction (FSI) solvers. This algorithm allows to tackle two of the main problems in computational biomechanics: added mass instability and n-fieldcoupling. The components of the IQN algorithm are dismembered and fused to obtain a reduced version, minimizing memory consumption and operations. This algorithm is firstly tested with a complex 2D case, and after used to compute a whole heart. The fluid-electro-mechanical model computed in the whole heart, provided deeper insights in the heartbeat behavior. Although the intracavitary velocities are smaller, the Q-criterion analysis revealed a more complex fluid pattern, if compared against the outflow tracts. Although the limitations of this model, this work is a step forward a simulation tool to study physiopathology and test biomedical devices.