Miracle-dz/UniFAR-FTUs huggingface dataset:
Unveiling the Limits of Learned Local Search Heuristics: Are You the Mightiest of the Meek?

In recent years, combining neural networks with local search heuristics has become popular in the field of combinatorial optimization. Despite its considerable computational demands, this approach has exhibited promising outcomes with minimal manual engineering. However, we have identified three critical limitations in the empirical evaluation of these integration attempts. Firstly, instances with moderate complexity and weak baselines pose a challenge in accurately evaluating the effectiveness of learning-based approaches. Secondly, the absence of an ablation study makes it difficult to quantify and attribute improvements accurately to the deep learning architecture. Lastly, the generalization of learned heuristics across diverse distributions remains underexplored. In this study, we conduct a comprehensive investigation into these identified limitations. Surprisingly, we demonstrate that a simple learned heuristic based on Tabu Search surpasses state-of-the-art (SOTA) learned heuristics in terms of performance and generalizability. Our findings challenge prevailing assumptions and open up exciting avenues for future research and innovation in combinatorial optimization.
Spatiotemporal Attention Enhances Lidar-Based Robot Navigation in Dynamic Environments

Foresighted robot navigation in dynamic indoor environments with cost-efficient hardware necessitates the use of a lightweight yet dependable controller. So inferring the scene dynamics from sensor readings without explicit object tracking is a pivotal aspect of foresighted navigation among pedestrians. In this letter, we introduce a spatiotemporal attention pipeline for enhanced navigation based on 2D lidar sensor readings. This pipeline is complemented by a novel lidar-state representation that emphasizes dynamic obstacles over static ones. Subsequently, the attention mechanism enables selective scene perception across both space and time, resulting in improved overall navigation performance within dynamic scenarios. We thoroughly evaluated the approach in different scenarios and simulators, finding excellent generalization to unseen environments. The results demonstrate outstanding performance compared to state-of-the-art methods, thereby enabling the seamless deployment of the learned controller on a real robot.
Revisiting Evaluation Metrics for Semantic Segmentation: Optimization and Evaluation of Fine-grained Intersection over Union

Semantic segmentation datasets often exhibit two types of imbalance: \textit{class imbalance}, where some classes appear more frequently than others and \textit{size imbalance}, where some objects occupy more pixels than others. This causes traditional evaluation metrics to be biased towards \textit{majority classes} (e.g. overall pixel-wise accuracy) and \textit{large objects} (e.g. mean pixel-wise accuracy and per-dataset mean intersection over union). To address these shortcomings, we propose the use of fine-grained mIoUs along with corresponding worst-case metrics, thereby offering a more holistic evaluation of segmentation techniques. These fine-grained metrics offer less bias towards large objects, richer statistical information, and valuable insights into model and dataset auditing. Furthermore, we undertake an extensive benchmark study, where we train and evaluate 15 modern neural networks with the proposed metrics on 12 diverse natural and aerial segmentation datasets. Our benchmark study highlights the necessity of not basing evaluations on a single metric and confirms that fine-grained mIoUs reduce the bias towards large objects. Moreover, we identify the crucial role played by architecture designs and loss functions, which lead to best practices in optimizing fine-grained metrics. The code is available at \href{https://github.com/zifuwanggg/JDTLosses}{https://github.com/zifuwanggg/JDTLosses}.
Comprehending Variability in Analysis Results of Software Product Lines

Analyses of a software product line (SPL) typically report variable results that are annotated with logical expressions indicating the set of product variants for which the results hold. These expressions can get complicated and difficult to reason about when the SPL has lots of features and product variants. Previous work introduced a visualizer that supports filters for highlighting the analysis results that apply to product variants of interest, but this work was weakly evaluated. In this paper, we report on a controlled user study that evaluates the effectiveness of this new visualizer in helping the user search variable results and compare the results of multiple variants. Our findings indicate that the use of the new visualizer significantly improves the correctness and efficiency of the user's work and reduces the user's cognitive load in working with variable results.
Magnetic activity variability of nearby bright Sun-like stars by 4 yr intensive Hα line monitoring

We report intensive monitoring of the activity variability in the Hα line for 10 Sun-like stars using the 1.88 m reflector at Okayama Branch Office, Subaru Telescope, during the last four years (2019–2022). Our aim was to investigate features of the stellar magnetic activity behavior. We correlated the Hα line variability of each star with the stellar activity levels derived from the Ca ii H&K line, suggesting its efficiency as a magnetic activity indicator. In analyzing the Hα line variation, we observed that some stars exhibited linear or quadratic trends during the observation period. Among several G- and K-type stars expected to have co-existing activity cycles, we confirmed the 2.9 yr short cycle of ϵ Eri (K2V) from the Hα observations. Additionally, we established upper limits on the Hα variability of β Com (G0V) and κ1 Cet (G5V) concerning their expected shorter cycles. We also detected the possibility of short-term activity cycles in two F-type stars, β Vir (F9V; ∼530 d) and α CMi (F5IV-V; ∼130 d). The cycle in α CMi was observed in only one season of our 4 yr observations, suggesting the temporal absence of the cycle period. However, for stars with planets, we did not observe significant magnetic activity variability likely associated with the planetary orbital period. It is speculated that the impact of Hα variability on radial velocity (RV) measurements may vary with spectral type.
NeRF Revisited: Fixing Quadrature Instability in Volume Rendering

Neural radiance fields (NeRF) rely on volume rendering to synthesize novel views. Volume rendering requires evaluating an integral along each ray, which is numerically approximated with a finite sum that corresponds to the exact integral along the ray under piecewise constant volume density. As a consequence, the rendered result is unstable w.r. t. the choice of samples along the ray, a phenomenon that we dub quadrature instability. We propose a mathematically principled solution by reformulating the sample-based rendering equation so that it corresponds to the exact integral under piecewise linear volume density. This simultaneously resolves multiple issues: conflicts between samples along different rays, imprecise hierarchical sampling, and non-differentiability of quantiles of ray termination distances w.r. t. model parameters. We demonstrate several benefits over the classical sample-based rendering equation, such as sharper textures, better geometric reconstruction, and stronger depth supervision. Our proposed formulation can be also be used as a drop-in replacement to the volume rendering equation of existing NeRF-based methods. Our project page can be found at pl-nerf.github. io.
Alternating Proximity Mapping Method for Strongly Convex-Strongly Concave Saddle-Point Problems

This is a continuation of our previous work entitled \enquote{Alternating Proximity Mapping Method for Convex-Concave Saddle-Point Problems}, in which we proposed the alternating proximal mapping method and showed convergence results on the sequence of our iterates, the sequence of averages of our iterates, and the sequence of function values evaluated at the averages of the iterates for solving convex-concave saddle-point problems. In this work, we extend the application of the alternating proximal mapping method to solve strongly convex-strongly concave saddle-point problems. We demonstrate two sets of sufficient conditions and also their simplified versions, which guarantee the linear convergence of the sequence of iterates towards a desired saddle-point. Additionally, we provide two sets of sufficient conditions, along with their simplified versions, that ensure the linear convergence of the sequence of function values evaluated at the convex combinations of iteration points to the desired function value of a saddle-point.
Worldwide estimates and bibliography of net primary productivity derived from pre-1982 publications

An extensive compilation of more than 700 field estimates of net primary productivity of natural and agricultural ecosystems worldwide was synthesized in Germany in the 1970s and early 1980s. Although the Osnabrueck data set has not been updated since the 1980s, it represents a wealth of information for use in model development and validation. This report documents the development of this data set, its contents, and its recent availability on the Internet from the Oak Ridge National Laboratory Distributed Active Archive Center for Biogeochemical Dynamics. Caution is advised in using these data, which necessarily include assumptions and conversions that may not be universally applicable to all sites.
Gravitational and Dynamic Components of Muscle Torque Gravitational and Dynamic Components of Muscle Torque Underlie Tonic and Phasic Muscle Activity during Goal-Directed Underlie Tonic and Phasic Muscle Activity during Goal-Directed Reaching Reaching

Human reaching movements require complex muscle activations to produce the forces necessary to move the limb in a controlled manner. How gravity and the complex kinetic properties of the limb contribute to the generation of the muscle activation pattern by the central nervous system (CNS) is a long-standing and controversial question in neuroscience. To tackle this issue, muscle activity is often subdivided into static and phasic components. The former corresponds to posture maintenance and transitions between postures. The latter corresponds to active movement production and the compensation for the kinetic properties of the limb. In the present study, we improved the methodology for this subdivision of muscle activity into static and phasic components by relating them to joint torques. Ten healthy subjects pointed in virtual reality to visual targets arranged to create a standard center-out reaching task in three dimensions. Muscle activity and motion capture data were synchronously collected during the movements. The motion capture data were used to calculate postural and dynamic components of active muscle torques using a dynamic model of the arm with 5 degrees of freedom. Principal Component Analysis (PCA) was then applied to muscle activity and the torque components, separately, to reduce the dimensionality of the data. Muscle activity was also reconstructed from gravitational and dynamic torque components. Results show that the postural and dynamic components of muscle torque represent a signiﬁcant amount of variance in muscle activity. This method could be used to deﬁne static and phasic components of muscle activity using muscle torques.
Enabling Social Digital Twins in the 6G Era with Information Centric Networking

By creating the virtual representation of the elements and services of physical systems, Digital Twins (DTs) are expected to provide crucial support to the ambitious Sixth-generation (6G) applications. Being data-driven entities that process a variety of data in real-time, DTs can be hosted at the network edge, thus providing low latency services. However, the deployment of a networking architecture supporting the dynamic discovery of DTs and their instantiation at the edge is still an open challenge. To fill the gap, in this article we conceive a solution that leverages two revolutionary paradigms, namely the Social Internet of Things (SIoT) and Information Centric Networking (ICN). On the one hand, SIoT can provide trustworthy interactions between DTs to support effective service discovery and composition. On the other hand, ICN is a network model that includes data-centric primitives and in-network caching to facilitate service provisioning. By combining the two paradigms, we create a new framework where DT services are requested by name and discovered in a timely fashion by navigating a name-based SIoT network. Whenever possible, services already instantiated at the edge are reused to satisfy different clients. After demonstrating the benefits of the envisioned framework with a preliminary evaluation, we highlight research opportunities for this timely topic.
Ultrapotassic Mafic Rocks as Geochemical Proxies for Post-collisional Dynamics of Orogenic Lithospheric Mantle: the Case of Southwestern Anatolia, Turkey

High-Mg ultrapotassic volcanic rock occurrences of lamproitic affinity are exposed in southwestern Anatolia, mostly within the Menderes Massif. From north to south the lamproitic volcanism shows increasingly younger ages ranging from 20 to 4 Ma. Volcanism is contemporaneous with more voluminous shoshonitic, high-K calc-alkaline, and ultrapotassic magmatic activity in the Simav–Selendi, Usak, Kirka, Koroglu, Afyon and Isparta–Golcuk areas. The southward decrease in the age of the volcanism correlates with changes in geochemical composition, particularly a decrease in 87Sr/86Sr, 207Pb/204Pb, Zr/Nb and Th/Nb, and an increase in 143Nd/144Nd, 176Hf/177Hf, 206Pb/204Pb, 208Pb/204Pb and Ce/Pb, thus delineating a systematic change from orogenic (crust-like) to anorogenic (convecting mantle-like) signatures. Rare earth element compositions of clinopyroxene phenocrysts demonstrate an increasing role for residual garnet for locations in the central parts of the Menderes Massif, indicating a lithosphere thickness greater than 80 km. In contrast, K2O abundances remain nearly constant at around 7%, indicating buffering by phlogopite in the mantle source. Magma genesis in southwestern Anatolia is controlled by post-collisional extensional events initiated after major lithospheric thickening. Geochemical constraints suggest that the mantle source experienced two main geodynamic stages. The first stage caused ultradepletion of the mantle and subsequent metasomatic enrichment, which allowed coupling of the geochemical signatures of ultradepleted harzburgite with those of crust-derived sediments. This happened during the final closure stages of the southern Neotethys Ocean and the accretion of forearc oceanic lithosphere (island-arc type), as shallowly subducted material to the Anatolian lithosphere. The second stage is post-collisional, and is related to the collapse of the orogenic belt and the development of extension-related horst and graben structures. This stage is concurrent with the initiation of a thermal anomaly originating from a gap, identified by seismic tomography, in the subducted slab under western Anatolia. We propose that the lithospheric mantle underwent intense ‘asthenospherization’ owing to lithosphere–asthenosphere interaction, caused by the southward expansion of this gap during slab roll-back. The geochemical resemblance of the lamproites to more voluminous, contemporaneous shoshonitic magmas implies their derivation from a heterogeneous mantle source that had been affected by similar processes. These mantle processes may be closely associated with the major episode of uplift in the Menderes Massif.
Reconstructing a sediment pulse: Modeling the effect of placer mining on Fraser River, Canada

Gold mining along 525 km of the Fraser River between 1858 and 1909 added an estimated 1.1 × 108 t of tailings, half gravel and the rest finer, to the river's natural sediment load. We simulate the response using a 1‐D multigrain size morphodynamic model. Since premining conditions are unknown and modern data are insufficient for tuning the process representation, we devised a novel modeling strategy which may be useful in other data‐poor applications. We start the model from a smoothed version of the modern longitudinal profile with bed grain size distributions optimized to match alternative assumptions about natural sediment supply and compare runs that include mining with control runs that can be used to quantify the effects of deficiencies in process representation and initialization. Simulations with an appropriate choice of natural supply rate closely match the best available test data, which consist of a detailed 1952–1999 gravel budget for the distal part of the model domain. The simulations suggest that the main response to mining was rapid bed fining, which allowed a major increase in bed load transport rate with only slight (~0.1 m) mean aggradation within the mining region and most of the excess sediment exported well beyond the mountain front within the mining period or soon afterward. We compare this pattern of response by a large, powerful river with previous case studies of river adjustment to sediment supply change.
Improved Thermoelectric Performance of Sb2Te3 Nanosheets by Coating Pt Particles in Wide Medium-Temperature Zone

The p-type Sb2Te3 alloy, a binary compound belonging to the V2VI3-based materials, has been widely used as a commercial material in the room-temperature zone. However, its low thermoelectric performance hinders its application in the low-medium temperature range. In this study, we prepared Sb2Te3 nanosheets coated with nanometer-sized Pt particles using a combination of solvothermal and photo-reduction methods. Our findings demonstrate that despite the adverse effects on certain properties, the addition of Pt particles to Sb2Te3 significantly improves the thermoelectric properties, primarily due to the enhanced electronic conductivity. The optimal ZT value reached 1.67 at 573 K for Sb2Te3 coated with 0.2 wt% Pt particles, and it remained above 1.0 within the temperature range of 333–573 K. These values represent a 47% and 49% increase, respectively, compared to the pure Sb2Te3 matrix. This enhancement in thermoelectric performance can be attributed to the presence of Pt metal particles, which effectively enhance carrier and phonon transport properties. Additionally, we conducted a Density Functional Theory (DFT) study to gain further insights into the underlying mechanisms. The results revealed that Sb2Te3 doped with Pt exhibited a doping level in the band structure, and a sharp rise in the Density of States (DOS) was observed. This sharp rise can be attributed to the presence of Pt atoms, which lead to enhanced electronic conductivity. In conclusion, our findings demonstrate that the incorporation of nanometer-sized Pt particles effectively improves the carrier and phonon transport properties of the Sb2Te3 alloy. This makes it a promising candidate for medium-temperature thermoelectric applications, as evidenced by the significant enhancement in thermoelectric performance achieved in this study.
Pre-proofs Inorganic mesoporous particles for controlled α-linolenic acid delivery to stimulate GLP-1 secretion in vitro

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The Concentration-Density Relation of Galaxies in the Las Campanas Redshift Survey

We report the results of the evaluation of the "concentration-density" relation of galaxies in the local universe, taking advantage of the very large and homogeneous data set available from the Las Campanas Redshift Survey. This data set consists of galaxies inhabiting the entire range of galactic environments from the sparsest field to the densest clusters, thus allowing us to study environmental variations without combining multiple data sets with inhomogeneous characteristics. Concentration is quantified by the automatically measured concentration index C, which is a good measure of a galaxy's bulge-to-disk ratio. The environment of the sample galaxies is characterized both by the three-space local galaxy density and by membership in groups and clusters. We find that the distribution of C in galaxy populations varies both with local density and with cluster/group membership: the fraction of centrally concentrated galaxies increases with local galaxy density and is higher in clusters than in the field. A comparison of the concentration-local density relation in clusters and the field shows that the two connect rather smoothly at the intermediate density regime, implying that the apparent cluster/field difference is only a manifestation of the variation with the local density. We conclude that the structure of galaxies is predominantly influenced by the local density and not by the broader environments characterized by cluster/field memberships.
A potential chemopreventive activity of tamoxifen and amygdalin on oxidative stress in mammary carcinoma-induced in female mice.

Keywords This study was undertaken to investigate the potential effect of tamoxifen and amygdalin in mammary carcinoma induced in mice. Fifty female albino mice, 4-6 weeks old age, were divided into five equal groups: group I (control normal) administrated no drugs; group II (carcinogenic group) administrated 7,12-dimethylbenz[a]anthracene (DMBA) dissolved in sesame oil (50 mg/kg b.wt) once a weak orally for 4 weeks; group III (tamoxifen treated group) given tamoxifen (50 mg/kg b.wt/day) orally for 4 weeks after induction of mammary cancer; group IV (amygdalin treated group) treated with amygdalin (0.6 mg/kg b.wt/day) for 4 weeks after induction of mammary carcinoma; group V (tamoxifen + amygdalin treated group) administrated tamoxifen as in group (III) with amygdalin as in group (IV). The obtained results exhibited that DMBA-induced mammary carcinomas caused a significant increase in serum progesterone and mammary tissue L-malondialdehyde (L-MDA) concentrations, with significant decrease in total antioxidant capacity (TAC) as compared to the control group. However, the administration of tamoxifen and amygdalin alleviates DMBA-induced mammary carcinomas through decreasing serum progesterone and L-MDA concentrations, along with increasing TAC concentration in mammary tissue. Additionally, hematological parameters of tamoxifen and amygdalin-treated mice displayed a significant increase compared to mammary cancer treated mice. Thus, it can be concluded that tamoxifen with amygdalin may be successful in the treatment of mammary carcinoma as well as improvement of oxidative stress of mammary carcinoma tissue.