Miracle-dz/UniFAR-FTUs huggingface dataset:
Convergence of a Robin boundary approximation for a Cahn–Hilliard system with dynamic boundary conditions

We prove the existence of unique weak solutions to an extension of a Cahn–Hilliard model proposed recently by C Liu and H Wu (2019 Arch. Ration. Mech. Anal. 233 167–247), in which the new dynamic boundary condition is further generalised with an affine linear relation between the surface and bulk phase field variables. As a first approach to tackle more general and nonlinear relations, we investigate the existence of unique weak solutions to a regularisation by a Robin boundary condition. Included in our analysis is the case where there is no diffusion for the surface phase field, which causes new difficulties for the analysis of the Robin system. Furthermore, for the case of affine linear relations, we show the weak convergence of solutions as the regularisation parameter tends to zero, and derive an error estimate between the two models. This is supported by numerical experiments which also demonstrate some non-trivial dynamics for the extended Liu–Wu model that is not present in the original model.
Recommender Systems Fairness Evaluation via Generalized Cross Entropy

Fairness in recommender systems has been considered with respect to sensitive attributes of users (e.g., gender, race) or items (e.g., revenue in a multistakeholder setting). Regardless, the concept has been commonly interpreted as some form of equality -- i.e., the degree to which the system is meeting the information needs of all its users in an equal sense. In this paper, we argue that fairness in recommender systems does not necessarily imply equality, but instead it should consider a distribution of resources based on merits and needs. We present a probabilistic framework based on generalized cross entropy to evaluate fairness of recommender systems under this perspective, where we show that the proposed framework is flexible and explanatory by allowing to incorporate domain knowledge (through an ideal fair distribution) that can help to understand which item or user aspects a recommendation algorithm is over- or under-representing. Results on two real-world datasets show the merits of the proposed evaluation framework both in terms of user and item fairness.
Effects of universal extra dimensions on top-quark electromagnetic interactions

Universal extra dimensions, presumably observable at some high-energy scale, would modify low-energy observables, being particularly relevant for physical processes forbidden at tree level by the Standard Model. We address the Kaluza-Klein contributions from the 5-dimensional Standard Model to the anomalous magnetic moment and to branching ratios of electromagnetic decays of the top quark. In accordance with present bounds on the compactification scale, contributions to both quantities are found to be at least 3 orders of magnitude below Standard-Model predictions.
Anisotropy control in photoelectron spectra: A coherent two-pulse interference strategy

Coherence among rotational ion channels during photoionization is exploited to control the anisotropy of the resulting photoelectron angular distributions at specific photoelectron energies. The strategy refers to a robust and single parameter control using two ultra-short light pulses delayed in time. The first pulse prepares a superposition of a few ion rotational states, whereas the second pulse serves as a probe that gives access to a control of the molecular asymmetry parameter $\beta$ for individual rotational channels. This is achieved by tuning the time delay between the pulses leading to channel interferences that can be turned from constructive to destructive. The illustrative example is the ionization of the $E(1\Sigma_{g}^{+})$ state of Li$_{2}$. Quantum wave packet evolutions are conducted including both electronic and nuclear degrees of freedom to reach angle-resolved photoelectron spectra. A simple interference model based on coherent phase accumulation during the field-free dynamics between the two pulses is precisely exploited to control the photoelectron angular distributions from almost isotropic, to marked anisotropic.
LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis

Point cloud based place recognition is still an open issue due to the difficulty in extracting local features from the raw 3D point cloud and generating the global descriptor, and it’s even harder in the large-scale dynamic environments. In this paper, we develop a novel deep neural network, named LPD-Net (Large-scale Place Description Network), which can extract discriminative and generalizable global descriptors from the raw 3D point cloud. Two modules, the adaptive local feature extraction module and the graph-based neighborhood aggregation module, are proposed, which contribute to extract the local structures and reveal the spatial distribution of local features in the large-scale point cloud, with an end-to-end manner. We implement the proposed global descriptor in solving point cloud based retrieval tasks to achieve the large-scale place recognition. Comparison results show that our LPD-Net is much better than PointNetVLAD and reaches the state-of-the-art. We also compare our LPD-Net with the vision-based solutions to show the robustness of our approach to different weather and light conditions.
Multi-Garment Net: Learning to Dress 3D People From Images

We present Multi-Garment Network (MGN), a method to predict body shape and clothing, layered on top of the SMPL model from a few frames (1-8) of a video. Several experiments demonstrate that this representation allows higher level of control when compared to single mesh or voxel representations of shape. Our model allows to predict garment geometry, relate it to the body shape, and transfer it to new body shapes and poses. To train MGN, we leverage a digital wardrobe containing 712 digital garments in correspondence, obtained with a novel method to register a set of clothing templates to a dataset of real 3D scans of people in different clothing and poses. Garments from the digital wardrobe, or predicted by MGN, can be used to dress any body shape in arbitrary poses. We will make publicly available the digital wardrobe, the MGN model, and code to dress SMPL with the garments at https://virtualhumans.mpi-inf.mpg. de/mgn
A unified representation network for segmentation with missing modalities

Over the last few years machine learning has demonstrated groundbreaking results in many areas of medical image analysis, including segmentation. A key assumption, however, is that the train- and test distributions match. We study a realistic scenario where this assumption is clearly violated, namely segmentation with missing input modalities. We describe two neural network approaches that can handle a variable number of input modalities. The first is modality dropout: a simple but surprisingly effective modification of the training. The second is the unified representation network: a network architecture that maps a variable number of input modalities into a unified representation that can be used for downstream tasks such as segmentation. We demonstrate that modality dropout makes a standard segmentation network reasonably robust to missing modalities, but that the same network works even better if trained on the unified representation.
Inverse Kinematics with Forward Dynamics Solvers for Sampled Motion Tracking

Tracking Cartesian motion with end effectors is a fundamental task in robot control. For motion that is not known in advance, the solvers must find fast solutions to the inverse kinematics (IK) problem for discretely sampled target poses. On joint control level, however, the robot's actuators operate in a continuous domain, requiring smooth transitions between individual states. In this work, we present a boost to the well-known Jacobian transpose method to address this goal, using the mass matrix of a virtually conditioned twin of the manipulator. Results on the UR10 show superior convergence and quality of our dynamics-based solver against the plain Jacobian method. Our algorithm is straightforward to implement as a controller, using common robotics libraries.
Frequency-Selective Beamforming Cancellation Design for Millimeter-Wave Full-Duplex

The wide bandwidths offered at millimeter-wave (mmWave) frequencies have made them an attractive choice for future wireless communication systems. Recent works have presented beamforming strategies for enabling in-band full-duplex (FD) capability at mmWave even under the constraints of hybrid beamforming, extending the exciting possibilities of next-generation wireless. Existing mmWave FD designs, however, do not consider frequency-selective mmWave channels. Wideband communication at mmWave suggests that frequency-selectivity will likely be of concern since communication channels will be on the order of hundreds of megahertz or more. This has motivated the work of this paper, in which we present a frequency-selective beamforming design to enable practical wideband mmWave FD applications. In our designs, we account for the challenges associated with hybrid analog/digital beamforming such as phase shifter resolution, a desirably low number of radio frequency (RF) chains, and the frequency-flat nature of analog beamformers. We use simulation to validate our work, which indicates that spectral efficiency gains can be achieved with our design by enabling simultaneous transmission and reception in-band.
Fine-Grained Segmentation Networks: Self-Supervised Segmentation for Improved Long-Term Visual Localization

Long-term visual localization is the problem of estimating the camera pose of a given query image in a scene whose appearance changes over time. It is an important problem in practice that is, for example, encountered in autonomous driving. In order to gain robustness to such changes, long-term localization approaches often use segmantic segmentations as an invariant scene representation, as the semantic meaning of each scene part should not be affected by seasonal and other changes. However, these representations are typically not very discriminative due to the very limited number of available classes. In this paper, we propose a novel neural network, the Fine-Grained Segmentation Network (FGSN), that can be used to provide image segmentations with a larger number of labels and can be trained in a self-supervised fashion. In addition, we show how FGSNs can be trained to output consistent labels across seasonal changes. We show through extensive experiments that integrating the fine-grained segmentations produced by our FGSNs into existing localization algorithms leads to substantial improvements in localization performance.
The Weighted Gaussian Curvature Derivative of a Space-Filling Diagram

Abstract The morphometric approach [11, 14] writes the solvation free energy as a linear combination of weighted versions of the volume, area, mean curvature, and Gaussian curvature of the space-filling diagram. We give a formula for the derivative of the weighted Gaussian curvature. Together with the derivatives of the weighted volume in [7], the weighted area in [4], and the weighted mean curvature in [1], this yields the derivative of the morphometric expression of solvation free energy.
Spectral Inference for Large Stochastic Blockmodels With Nodal Covariates

In many applications of network analysis, it is important to distinguish between observed and unobserved factors affecting network structure. To this end, we develop spectral estimators for both unobserved blocks and the effect of covariates in stochastic blockmodels. Our main strategy is to reformulate the stochastic blockmodel estimation problem as recovery of latent positions in a generalized random dot product graph. On the theoretical side, we establish asymptotic normality of our estimators for the subsequent purpose of performing inference. On the applied side, we show that computing our estimator is much faster than standard variational expectation--maximization algorithms and scales well for large networks. The results in this paper provide a foundation to estimate the effect of observed covariates as well as unobserved latent community structure on the probability of link formation in networks.
Reflection Time Difference as a Probe of S-Matrix Zeroes in Chaotic Resonance Scattering

Motivated by recent interest in the zeroes of S-matrix entries in the complex energy plane, I use the Heidelberg model of resonance scattering to introduce the notion of Reflection Time Difference which is shown to play the same role for the {\it zeroes} as the Wigner time delay plays for the S- matrix {\it poles}.
SIRUS: making random forests interpretable

State-of-the-art learning algorithms, such as random forests or neural networks, are often qualified as "black-boxes" because of the high number and complexity of operations involved in their prediction mechanism. This lack of interpretability is a strong limitation for applications involving critical decisions, typically the analysis of production processes in the manufacturing industry. In such critical contexts, models have to be interpretable, i.e., simple, stable, and predictive. To address this issue, we design SIRUS (Stable and Interpretable RUle Set), a new classification algorithm based on random forests, which takes the form of a short list of rules. While simple models are usually unstable with respect to data perturbation, SIRUS achieves a remarkable stability improvement over cutting-edge methods. Furthermore, SIRUS inherits a predictive accuracy close to random forests, combined with the simplicity of decision trees. These properties are assessed both from a theoretical and empirical point of view, through extensive numerical experiments based on our R/C++ software implementation sirus available from CRAN.
Sample Greedy Gossip for Distributed Network-Wide Average Computation.

This paper investigates the problem of distributed network-wide averaging and proposes a new greedy gossip algorithm. Instead of finding the optimal path of each node in a greedy manner, the proposed approach utilises a suboptimal communication path by performing greedy selection among randomly selected active local nodes. Theoretical analysis on convergence speed is also performed to investigate the characteristics of the proposed algorithm. The main feature of the new algorithm is that it provides great flexibility and well balance between communication cost and convergence performance introduced by the stochastic sampling strategy. Extensive numerical simulations are performed to validate the analytic findings.
Multielemental Determination of Cu , Zn , Mo , Mn , V in Soils by ICP-AES Method after Microwave Digestion

The development of ICP-AES came about due to the need for a fast multielement soil analysis. However, interferences do occur when there is a high concentration of any individual element in the soil. The trace elements Си, Zn, Mo, V, and Mn were determined by the ICP-AES method in a soil and interferences of macroelements AI, Ca, Fe, Mg, Na, К were studied. The limits of detection for trace elements were statistically calculated. The decomposition procedure for the soil samples was performed by popular microwave oven СЕМ. Modelled standard solution with matrix elements was used to obtain the calibration curve. Results were compared with the certified values of elements in certified reference material SO-2.
MEASURING COMPETITION ALONG THE SUPPLY CHAIN OF THE MALAYSIAN POULTRY INDUSTRY

Analysis of the competitive market describes the environment within which firms in a particular market operates. This can be done by considering several factors such as the analysis of the market structure i.e number and size distribution of buyers and sellers (market concentration), the degree of product differentiation, how easy it is for new firms to enter the market (barriers to entry), the shape of cost curves, the extent to which firms are vertically integrated or diversified and the price and information transmission process among vertically or spatially integrated markets. This paper considers the structure conduct performance model to evaluate competition indicators along the supply chain of the Malaysian poultry market. Malaysian Poultry industry Firm’s financial data for period 2005-2011 was obtained from Company Commission of Malaysia (SSM) for analysis. Results of the analysis could not prove any allegation of anticompetitive behavior and or exercise of market power by the integrators along the supply chain. However, the Farm level market is moderately concentrated over the study period indicated by the CR4 61.9% and HHI 2179. The overall performance of the industry based on results of the profitability ratio suggests the industry is doing quite well over the study period. Field of Research: competition, structure-conduct-performance, vertical coordination, poultry. ------------------------------------------------------------------------------------------------------------------------------------------
Evaluation of D 2 Attention Levels of Orienteering National Team Athletes Who Are in Education

The aim of this study was to determine the levels of attention in the rest state and post-performance status of the national orienteering team athletes who are in education age with the d2 attention test. For this purpose, this study was designed with relational screening model from quantitative research approaches. 40 volunteer athletes, 14 of which were female and 26 of them were male, were included in this study group. It was analyzed by Paired Samples t test whether the pre-test and post-test scores which the athletes participating in the study got from the d2 attention test were different. There was a significant difference among the applications in all scores of the d2 test (p <0.05). When the averages of the scores were examined, it was found by performing test repetitions that selective attention ability, sustainability of attention, high motivation and psychomotor speed (TN) most marked material) didn’t decrease the stability of psychomotor speed and quality of work performed CP (number of correctly marked d2)] d2 numbers [TN(E1) + (E2)] that were marked incorrectly or weren’t marked. As a result; it was observed with d2 test which is one of the tests used in the field of applied sports psychology to evaluate the visual focusing attention performance of the athletes that the reason of positive differentiation in the attention performances which was obtained after the intensive exercise was intense concentration and the attention levels were high due to the concentration.
Introducing a structure into a set of similar concepts

In the paper, we examine the idea of supporting domain ontology creation by an automatic clustering of selected terms identified using a terminology extraction method. We discuss the problem of introducing a structure into a set of similar concepts. We extract terminology from economic articles in Polish Wikipedia, then we select several sets of similar concepts present in the top 5,500 extracted terms. We describe two methods for automatic clustering of such groups of phrases on the basis of their distributional properties, i.e. the quantitative characteristics of the contexts of their occurrences in texts and test them on two sets of data.
Reduced-Oil DDGS in Broiler and Layer Diets

CORN DDGS IS AN EXCELLENT FEED INGREDIENT for use in broiler and layer diets. Several scienti c reviews have been published that include recommended energy and nutrient values for DDGS when formulating broiler and layer diets (Waldroup et al. 2007; Choi et al., 2008; Swiatkiewicz and Korelski, 2008; Bregendahl, 2008; Salim et al., 2010; ElHack et al., 2015), but these recommendations generally apply for high-oil (greater than 10 percent crude fat) DDGS and not reduced-oil (less than 10 percent crude fat) DDGS. Fortunately, a considerable amount of research has been conducted during the past seven years to determine the apparent metabolizable energy (AMEn) and digestible amino acid content of DDGS with variable crude fat content. Results from these recent studies are summarized in this chapter. The energy and nutritional composition data, and prediction equations used to derive them, are the most current and representative for corn DDGS sources produced in the U.S., and should be used when formulating precision nutrition DDGS diets for broilers and layers.
A Data Mining Framework to Analyze Road Accident Data Using Map Reduce CCMF and TCAMP Algorithms

- Accident prediction is an important safety issue to raise alarms before accidents happen. In this paper we formulate relevant questions to anticipate the occurrence of accidents and process the available information using Hadoop. We examine the execution time on Hadoop when compared with other methods, and propose 2 algorithms to use Congestion Control Machine Framework (CCMF) and Traffic Congestion Analyzer using Map Reduce (TCAMP) to effectively analyze the available data and assess road accident reasons and advise authorities to take appropriate actions to increase road safety. We apply information mining to examine recorded road attributes to reduce road accidents specifically in India, and formulate a set of standards that can be utilized by the National Highway Authority of India to improve safety.
Music with Unconventional Computing : Towards a Platform for Physarum Polycephalum Sound Synthesis

Our research is concerned with the application of novel engineering paradigms implemented on biological substrates in the field of computer music. Computers have been programmed to produce sound since the beginning of the 1950’s. Nowadays, the computer is ubiquitous in many aspects of music, ranging from software for musical composition and production, to systems for distribution of music on the Internet. Therefore, it is likely that future developments in computational science will have an impact in music technology. This paper introduces a preliminary platform for digitizing data from a biological substance for collation and sonification. Contained, is a survey of existing platforms with a parallel study into the construction of our platform.
DFT Based OFDM Using Different Modulation Approaches TO Improve BER Performance

--A review work on the BER based OFDM system is presented in this paper for the DWT OFDM approach. The OFDM communication model overcomes the drawbacks of conventional communicational model and offers the high data rate, and high spectral efficiency. Compared to conventional approaches the 4th generation Long term evolution application has better spectral efficiency in terms of accuracy and high data rate, the 4th generation Long term evolution approach is formed by the collaboration of OFDM and MIMO. Although OFDM has many advantages over FDM but it suffers from inter carrier interference and inter symbol interference when multiple carriers are used and due to this interferences loss of Orthogonality happens, in order to overcome these interferences usage of cyclic prefix has became mandatory. But usage of cyclic prefix shows huge negative impact on bandwidth efficiency as the cyclic prefix approach consumes nearly 20% of bandwidth and BER performance too affected. In this paper a novel wavelet based OFDM model is presented which is mainly intended to provide good Orthogonality and better spectral efficiency using various modulation techniques, the unique thing in the usage of wavelet based OFDM is it does not need any spectral efficiency and absence of the cyclic prefix increases bandwidth efficiency when bandwidth increases simultaneously spectral efficiency increases. Finally the usage of the wavelet based OFDM shows improved BER over conventional FDM communication model. The simulation results indicates the usage of wavelet based OFDM in place of DWT based OFDM in LTE and finally the comparison between wavelet based OFDM and DFT based OFDM.