Miracle-dz/UniFAR-FTUs huggingface dataset:
A Vehicle Transmission Scheduling Scheme for Supporting Vehicle Trust Management

Recent developments in advanced sensors, wireless communications and intelligent vehicle control technologies have enabled vehicles to detect traffic anomalies on the road and then notify surrounding vehicles to improve traffic safety. However, due to the high-speed movement of vehicles and the frequent topological changes between vehicles, it is difficult for vehicles to evaluate the credibility of received messages. Quite a lot of research effort has been carried out to establish various trustworthiness platforms. These studies mostly focus on how to enhance the accuracy of credibility evaluation, overlooking that the transmission performance may affect the quality of vehicle messages. In this paper, we aim to support the improvement of credibility evaluation in vehicle networks by enhancing the transmission experience of vehicles. The proposed solution utilizes the vehicle's trajectory information, detection range and a roadside unit (RSU) coverage to form a controlled number of detection zones, which guarantees that events can be detected and reported while limiting the number of transmission vehicles. Furthermore, our scheme takes account of vehicle credibility and interference ranges when selecting reporting vehicles, supporting the timely and reliable delivery of vehicles' event reports when accidents occur. Our ns2 evaluation shows that our scheme can greatly reduce delay and loss rates of vehicle messages to help existing studies on accurate vehicle credibility evaluation.
A Novel Grip Force Measurement Concept for Tactile Stimulation Mechanisms – Design, Validation, and User Study

In this article, we developed a new grip force measurement concept that allows for embedding tactile stimulation mechanisms in a gripper. This concept is based on a single force sensor to measure the force applied on each side of the gripper, and substantially reduces tactor motion artifacts on force measurement. To test the feasibility of this new concept, we built a device that measures control of grip force in response to a tactile stimulation from a moving tactor. We calibrated and validated our device with a testing setup with a second force sensor over a range of 0 to 20 N without movement of the tactors. We tested the effect of tactor movement on the measured grip force, and measured artifacts of 1% of the measured force. We demonstrated that during the application of dynamically changing grip forces, the average errors were 2.9% and 3.7% for the left and right sides of the gripper, respectively. We characterized the bandwidth, backlash, and noise of our tactile stimulation mechanism. Finally, we conducted a user study and found that in response to tactor movement, participants increased their grip force, the increase was larger for a smaller target force, and depended on the amount of tactile stimulation.
Observed impacts of climate change on terrestrial birds in Europe: an overview

Abstract Climate change is predicted to be the major threat to biodiversity by 2100, when the global average temperature may increase by 5--6 degrees, according to current predictions and trends in greenhouse gas emissions. However, many impacts of climate change on biodiversity have already been observed. These observed effects provide tangible evidence to motivate action to mitigate against further climate change. Focusing on Europe, this overview summarises recently published observed impacts of modern climate change on birds. Due to the sensitivity of birds to weather fluctuations, the high numbers of ornithologists throughout Europe and the tradition in the long-term study of bird populations, there is no doubt that climate change impacts on birds have already occurred. These impacts include changes in (i) phenology (e.g., breeding times), (ii) migration patterns (e.g., time of spring arrival from the wintering grounds), (iii) species distribution (e.g., poleward shift of range margins) and (iv) abundances (e.g., population declines of habitat-specialist birds). Although the overall evidence available is comprehensive, there is a challenge in disentangling the effects of climate change from those of other concurrent factors such as habitat loss and degradation, e.g. due to large-scale intensification of agriculture. Birds are coping with climate change by means of their phenotypic plasticity, but little evidence is available to prove that evolutionary adaptation is already taking place. Further climate change is likely to affect birds at many levels, from changes in body size to shifts in assemblage composition, from mismatches with their resources to the introduction of exotic species and new diseases: the interactions among these levels need to be considered in future research and to devise effective conservation strategies. The rapid development of this key topic in conservation biology and applied ecology makes it important to ensure long-term funding for research, review and dissemination.
GroupIM: A Mutual Information Maximization Framework for Neural Group Recommendation

We study the problem of making item recommendations to ephemeral groups, which comprise users with limited or no historical activities together. Existing studies target persistent groups with substantial activity history, while ephemeral groups lack historical interactions. To overcome group interaction sparsity, we propose data-driven regularization strategies to exploit both the preference covariance amongst users who are in the same group, as well as the contextual relevance of users' individual preferences to each group. We make two contributions. First, we present a recommender architecture-agnostic framework GroupIM that can integrate arbitrary neural preference encoders and aggregators for ephemeral group recommendation. Second, we regularize the user-group latent space to overcome group interaction sparsity by: maximizing mutual information between representations of groups and group members; and dynamically prioritizing the preferences of highly informative members through contextual preference weighting. Our experimental results on several real-world datasets indicate significant performance improvements (31-62% relative NDCG@20) over state-of-the-art group recommendation techniques.
Ghost Point Diffusion Maps for Solving Elliptic PDEs on Manifolds with Classical Boundary Conditions

In this paper, we extend the class of kernel methods, the so‐called diffusion maps (DM) and its local kernel variants to approximate second‐order differential operators defined on smooth manifolds with boundaries that naturally arise in elliptic PDE models. To achieve this goal, we introduce the ghost point diffusion maps (GPDM) estimator on an extended manifold, identified by the set of point clouds on the unknown original manifold together with a set of ghost points, specified along the estimated tangential direction at the sampled points on the boundary. The resulting GPDM estimator restricts the standard DM matrix to a set of extrapolation equations that estimates the function values at the ghost points. This adjustment is analogous to the classical ghost point method in a finite‐difference scheme for solving PDEs on flat domains. As opposed to the classical DM, which diverges near the boundary, the proposed GPDM estimator converges pointwise even near the boundary. Applying the consistent GPDM estimator to solve well‐posed elliptic PDEs with classical boundary conditions (Dirichlet, Neumann, and Robin), we establish the convergence of the approximate solution under appropriate smoothness assumptions. We numerically validate the proposed mesh‐free PDE solver on various problems defined on simple submanifolds embedded in Euclidean spaces as well as on an unknown manifold. Numerically, we also found that the GPDM is more accurate compared to DM in solving elliptic eigenvalue problems on bounded smooth manifolds. © 2021 Wiley Periodicals LLC.
Partial sequencing of Stearoyl-CoA desaturase gene in buffalo

Riassunto Sequenza parziale del gene della Stearoyl-CoA desaturasi nel bufalo. Il gene che codifica per la Stearoyl-CoA desaturasi, enzima coinvolto nella biosintesi degli acidi grassi mono-insaturi, è stato sequenziato nel bovino, dove risulta composto da 6 esoni, e in cui sono riferiti numerosi SNPs. Nel presente lavoro è stata definita la sequenza, nella specie bufalina, di una porzione del gene, che comprende il quarto esone, e che si differenzia da quella bovina per una delezione di tre nucleotidi e per 4 mutazioni puntiformi. Le sequenze di 127 bufali appartenenti a quattro popolazioni (Italiana, Greca, Egiziana e Turca), analizzate con il sistema DHPLC Transgenomic WAVE®, hanno evidenziato la presenza di quattro SNPs; il sequenziamento diretto ha dimostrato che uno di questi è causativo (Arginina-Lisina). Sono stati inoltre confrontati gli indici genetici per la percentuale di grasso del latte di 27 tori bufalini italiani sottoposti a prova di progenie che presentavano genotipi diversi allo SNP causativo. L’indice dei tori omozigoti per la Lisina è risultato superiore a quello dei tori eterozigoti e omozigoti per l’Arginina. Poiché la frequenza del genotipo Lisina nella popolazione bufalina italiana, selezionata per la produzione di mozzarella, è molto più elevata che nelle altre (P<0,01), che invece sono state selezionate per la produzione di latte, si ipotizza un effetto positivo del genotipo Lisina sul contenuto di grasso del latte.
Equality of Learning Opportunity in Personalized Recommendations

Online educational platforms are promising to play a primary role in mediating the success of individuals' careers. Hence, while building overlying content recommendation services, it becomes essential to ensure that learners are provided with equal learning opportunities, according to the platform values, context, and pedagogy. Even though the importance of creating equality of learning opportunities has been well investigated in traditional institutions, how it can be operationalized scalably in online learning ecosystems through recommender systems is still under-explored. In this paper, we formalize principles, that aim to model a range of learning opportunity properties in recommendations, and a metric that combines them to quantify the equality of learning opportunities among learners. Then, we envision a scenario wherein platform owners seek to guarantee that the generated recommendations meet each principle, to a certain degree, for all learners, constrained to their individual preferences. Under this view, we provide observations on learning opportunities in a real-world online dataset, highlighting inequalities among learners. To mitigate this effect, we propose a post-processing approach that balances personalization and learning opportunity equality in recommendations. Experiments on a large-scale dataset demonstrate that our approach leads to higher equality of learning opportunity, with a small loss in personalization.
A Direct Mapped Method for Accurate Modeling and Real-Time Simulation of High Switching Frequency Resonant Converters

This article presents a direct mapped method (DMM) for real-time simulation of high switching frequency resonant converters. The DMM links state variables to diode statuses and provides an exact and noniterative solution to network equations. The proposed method is implemented on field programmable gate array (FPGA) to simulate an LLC converter with switching frequencies ranging up to 500 kHz. The best reported implementations of DMM achieve a 25-ns simulation time-step for a wide range of clock frequencies, ranging from 40 to 320 MHz.
Non Proportional Odds Models are Widely Dispensable -- Sparser Modeling based on Parametric and Additive Location-Shift Approaches

The potential of location-shift models to find adequate models between the proportional odds model and the non-proportional odds model is investigated. It is demonstrated that these models are very useful in ordinal modeling. While proportional odds models are often too simple, non proportional odds models are typically unnecessary complicated and seem widely dispensable. The class of location-shift models is also extended to allow for smooth effects. The additive location-shift model contains two functions for each explanatory variable, one for the location and one for dispersion. It is much sparser than hard-to-handle additive models with category-specific covariate functions but more flexible than common vector generalized additive models.
Carbon, nitrogen and microbial gradients induced by plant residues decomposing in soil

Understanding the role of the soil matrix in the decomposition of soil organic matter requires an assessment of how the soil matrix controls the availability of substrate to microorganisms. We aimed to identify the sites of microbial assimilation of a decomposing substrate the initial location of which in the soil matrix was known. We incubated wheat straw doubly labelled with 13C and 15N as a single layer in the middle of a core of soil and we separated, after different times of incubation, soil layers situated at different distances from the straw. We analysed them for their 13C and 15N contents and dehydrogenase activity. The presence of the straw induced steep gradients of dehydrogenase activity in the core; the activity increased at the contact with the straw and decreased to reach the reference value between 3 and 4 mm from the straw. This stimulated microbial activity was linked with an incorporation of 13C derived from the straw in the core to approximately 4 mm from it. Nearly half of this 13C was present in the microbial biomass. Low temperature scanning electron microscopy showed that the microbial biomass was largest near the straw. The straw induced strong spatial heterogeneity of microbial activity at the millimetric scale, presumably because of diffusion of soluble compounds from the straw. This heterogeneity allowed us to identify the sites of microbial assimilation as being the soil nearest to the straw.
ERP SYSTEMS IN CROATIAN ENTERPRISES AND INDUSTRY 4.0

Resource planning in manufacturing companies is crucial in the context of Industry 4.0. Enterprise Resource Planning (ERP) systems that enable vertical and horizontal integration between different operational and management levels are essential for monitoring process execution through real-time data access and for making decisions according to these data. The implementation of the ERP system is one of the key indicators highlighted by the European Commission (more than 100 indicators) in the Digital Agenda for Europe (A Europe 2020 Initiative), which sets the goals for the growth of the European Union by 2020. It is important to make an analysis of the state of implementation of the ERP systems in the Republic of Croatia. We researched within the metalworking industry in Croatia. Purpose of the research conducted is to determine the state of implementation of ERP systems in metalworking companies and on the compliance of ERP systems with Industry 4.0 and to investigate the opinions of ERP system users on the extent to which the ERP system they use meets the requirements of Industry 4.0. The main objective of our paper is twofold: to present the features of modern ERP systems in the Industry 4.0 framework and to present the results of our research.
Photohadronic Model for the Neutrino and Gamma-Ray Emission from TXS 0506+056

The detection of a high-energy muon neutrino on 2017 September 22 by the IceCube neutrino detector coincides with the multiwavelength flaring from the BL Lac object TXS 0506+056, most likely confirming an active galactic nucleus as a source of high energy cosmic rays and neutrinos. Using the photohadronic model, we have explained the very high energy γ-rays observed by MAGIC telescopes a few days after the neutrino event and extend the model to calculate the neutrino flux at different windows consistent with the flaring period of TXS 0506+056 and compared with the IceCube and MAGIC estimates. We also use this model to estimate the neutrino flux from the flaring of FSRQ PKS B1424-418, which is believed to be associated with the 2 PeV neutrino event observed by IceCube.
An MCMC Method for Uncertainty Set Generation via Operator-Theoretic Metrics

Model uncertainty sets are required in many robust optimization problems, such as robust control and prediction with uncertainty, but there is no definite methodology to generate uncertainty sets for nonlinear dynamical systems. In this paper, we propose a method for model uncertainty set generation via Markov chain Monte Carlo. The proposed method samples from distributions over dynamical systems via metrics over transfer operators and is applicable to general nonlinear systems. We adapt Hamiltonian Monte Carlo for sampling high-dimensional transfer operators in a computationally efficient manner. We present numerical examples to validate the proposed method for uncertainty set generation.
Attribute-Efficient Learning of Halfspaces with Malicious Noise: Near-Optimal Label Complexity and Noise Tolerance

This paper is concerned with computationally efficient learning of homogeneous sparse halfspaces in $\mathbb{R}^d$ under noise. Though recent works have established attribute-efficient learning algorithms under various types of label noise (e.g. bounded noise), it remains an open question when and how $s$-sparse halfspaces can be efficiently learned under the challenging malicious noise model, where an adversary may corrupt both the unlabeled examples and the labels. We answer this question in the affirmative by designing a computationally efficient active learning algorithm with near-optimal label complexity of $\tilde{O}\big({s \log^4 \frac d \epsilon} \big)$ and noise tolerance $\eta = \Omega(\epsilon)$, where $\epsilon \in (0, 1)$ is the target error rate, under the assumption that the distribution over (uncorrupted) unlabeled examples is isotropic log-concave. Our algorithm can be straightforwardly tailored to the passive learning setting, and we show that the sample complexity is $\tilde{O}\big({\frac 1 \epsilon s^2 \log^5 d} \big)$ which also enjoys the attribute efficiency. Our main techniques include attribute-efficient paradigms for instance reweighting and for empirical risk minimization, and a new analysis of uniform concentration for unbounded data -- all of them crucially take the structure of the underlying halfspace into account.
Improving the Recursion Computation of Clebsch-Gordan Coefficients for Light Scattering Simulations

Fast, accurate, and stable computation of the Clebsch-Gordan (C-G) coefficients is always desirable, for example, in light scattering simulations, the translation of the multipole fields, quantum physics and chemistry. Current recursion method for computing the C-G coefficients is often unstable for large quantum numbers due to numerical overflow or underflow. In this paper, we present an improved scheme for the computation of the C-G coefficients by separating the recursion process into sign-recursion and exponent-recursion explicitly. The result shows that the proposed method can greatly improve the stability of the computation without losing its efficiency, producing accurate values for the C-G coefficients even with extremely large quantum numbers.
Equivariant Maps for Hierarchical Structures

In many real-world settings, we are interested in learning invariant and equivariant functions over nested or multiresolution structures, such as a set of sequences, a graph of graphs, or a multiresolution image. While equivariant linear maps and by extension multilayer perceptrons (MLPs) for many of the individual basic structures are known, a formalism for dealing with a hierarchy of symmetry transformations is lacking. Observing that the transformation group for a nested structure corresponds to the ``wreath product'' of the symmetry groups of the building blocks, we show how to obtain the equivariant map for hierarchical data-structures using an intuitive combination of the equivariant maps for the individual blocks. To demonstrate the effectiveness of this type of model, we use a hierarchy of translation and permutation symmetries for learning on point cloud data, and report state-of-the-art on \kw{semantic3d} and \kw{s3dis}, two of the largest real-world benchmarks for 3D semantic segmentation.
Chromosome numbers and taxonomic notes on northern grasses. III. Twenty-five genera.

Chromosome numbers and voucher specimens are recorded for some collections of 25 genera of grasses, mainly collected in Canada. The somatic chromosome numbers of the following are recorded: (1) Agrostis borealis, 2n = 56; A. canina, 2n = 14; A. gigantea, 2n = 42; A. palustris, 2n = 30; A. perennans, 2n = 42; A. scabra, 2n = 42; and A. tenuis, 2n = 28; 32; and 34. (2) Alopecurus acqualis, 2n = 14; A. alpinus, 2n = 105; 112; and 119; A. geniculatus, 2n = 28; A. occidentalis, 2n = 112; and A. pratensis, 2n = 28. (3) Ammophila breviligulata, 2n = 28. (4) Anthoxanthum odoratum, 2n = 20. (5) Calamagrostis canadensis, 2n = 42; and 56; C. inexpansa, 2n = 70; C. lapponica, 2n = 28; 32; 42; ca. 91; 98; and 140; C. neglecta, 2n = 28; C. pickeringii, 2n = 28; C. purpurascens, 2n = 28; 42; and 84; and Calamagrostis sp. , putative hybrid, 2n = 49. (6) Cinna arundinacea, 2n = 28; and Cinna latifolia, 2n = 28. (7) Danlhonia compressa, 2n = 36; and D. spicata, 2n = 36. (8) Deschampsia brevifolia, 2n = 26; D. caespitosa, 2.
Application of Higher Order Sliding Mode Observer and Super Twisting Observer based Super Twisting Control in Hydroelectric Power Plants

Speed control of hydro Turbine is an important issue and a shorter settling time is desired. In case of operation of drives with speed sensors, the performance of the drive is not satisfactory. To get satisfactory performance, a sensorless speed control system for hydro Turbine driving a synchronous generator in the hydroelectric power plants is developed. This work, we apply the approach of two methods of regulation: Super Twisting Control (STC) based on super-twisting observer (STO) and Super Twisting Control (STC) based on Higher Order Sliding Mode Observer (HOSMO). Simulation model in the presence of stochastic disturbance has been established in Matlab -Simulink. Simulation results demonstrate and validate the productiveness and performances of the proposed control method.
Anticipated Effects of Climate Change on Freshwater Fishes and Their Habitat

Abstract We sketch an iterative assessment process for the effects of climate change on freshwater fisheries that uses water temperature, water quantity, and water quality variables to link the atmosphere to fishery resources. Iterative interaction among atmospheric, ecological, and fisheries scientists clarifies the information needs of each discipline and progressively improves the assessments of effects. The process incorporates information at different scales, i.e., organism/laboratory, species/habitat, and population/ecosystem. We illustrate the operation of the iterative assessment process with recent work done on the water temperature linkage, and sketch some linkages through water quantity and water quality variables. A Wild Salmonid Watch (WSW) could provide a framework for monitoring climate change and its effects on salmonid stocks on a hemispheric scale. We discuss the initial steps required to mobilize a WSW for climate change and its role as climate change develops in the decades ahead.
Common-Value Auctions with Ambiguity about Correlation

We consider common value second-price auctions when individuals have correlation capacities; i.e., when they have ambiguity over the joint information structures generating the signals for players, but put some bounds on the correlation between their information and others. We show that such ambiguity can a¤ect equilibrium bids in non-trivial ways; high and low types decrease their bids while intermediate types may increase their bids, compared to the canonical model. The results di¤er from the case of ambiguity about the prior, and from a cursed-equilibrium analysis.