Stability investigations of isotropic and anisotropic exponential inflation in the Starobinsky-Bel-Robinson gravity

In this paper, we would like to examine whether a novel Starobinsky-Bel-Robinson gravity model admits stable exponential inflationary solutions with or without spatial anisotropies. As a result, we are able to derive an exact de Sitter inflationary to this Starobinsky-Bel-Robinson model. Furthermore, we observe that an exact Bianchi type I inflationary solution does not exist in the Starobinsky-Bel-Robinson model. However, we find that a modified Starobinsky-Bel-Robinson model, in which the sign of coefficient of $R^2$ term is flipped from positive to negative, can admit the corresponding Bianchi type I inflationary solution. Unfortunately, stability analysis using the dynamical system approach indicates that both of these inflationary solutions turn out to be unstable. Interestingly, we show that a stable de Sitter inflationary solution can be obtained in the modified Starobinsky-Bel-Robinson gravity.Comment: 26 pages, 2 figures. V2 with the abstract revised to improve its clarity, some relevant references added, and some typos fixed. All main calculations and conclusions remain unchanged. Comments are welcom

LMGP: Lifted Multicut Meets Geometry Projections for Multi-Camera Multi-Object Tracking

Multi-Camera Multi-Object Tracking is currently drawing attention in the computer vision field due to its superior performance in real-world applications such as video surveillance in crowded scenes or in wide spaces. In this work, we propose a mathematically elegant multi-camera multiple object tracking approach based on a spatial-temporal lifted multicut formulation. Our model utilizes state-of-the-art tracklets produced by single-camera trackers as proposals. As these tracklets may contain ID-Switch errors, we refine them through a novel pre-clustering obtained from 3D geometry projections. As a result, we derive a better tracking graph without ID switches and more precise affinity costs for the data association phase. Tracklets are then matched to multi-camera trajectories by solving a global lifted multicut formulation that incorporates short and long-range temporal interactions on tracklets located in the same camera as well as inter-camera ones. Experimental results on the WildTrack dataset yield near-perfect performance, outperforming state-of-the-art trackers on Campus while being on par on the PETS-09 dataset.Comment: Official version for CVPR 202

Anisotropic power-law inflation for models of non-canonical scalar fields non-minimally coupled to a two-form field

In this paper, we investigate the validity of the so-called cosmic no-hair conjecture in the framework of anisotropic inflation models of non-canonical scalar fields non-minimally coupled to a two-form field. In particular, we focus on two typical {\it k}-inflation and Dirac-Born-Infeld inflation models, in which we find a set of exact anisotropic power-law inflationary solutions. Interestingly, these solutions are shown to be stable and attractive during an inflationary phase using the dynamical system analysis. The obtained results indicate that the non-minimal coupling between the scalar and two-form fields acts as a non-trivial source of generating stable spatial anisotropies during the inflationary phase and therefore violates the prediction of the cosmic no-hair conjecture, even when the scalar field is of non-canonical forms.Comment: 16 pages, 6 figures. Comments are welcom

Prototype edge-grown nanowire sensor array for the real-time monitoring and classification of multiple gases

The monitoring and classification of different gases using a single resistive semiconductor sensor are challenging because of the similar response characteristics. An array of separated sensors can be used as an electronic nose, but such arrays have a bulky structure and complex fabrication processes. Herein, we easily fabricated a gas-sensor array based on edge-grown SnO2 nanowires for the real-time monitoring and classification of multiple gases. The array comprised four sensors and was designed on a glass substrate. SnO2 nanowires were grown on-chip from the edge of electrodes, made contact together, and acted as sensing elements. This method was advantageous over the post-synthesis technique because the SnO2 nanowires were directly grown from the edge of the electrodes rather than on the surface. Accordingly, damage to the electrode was avoided by alloying Sn with Pt at a high growth temperature. The sensing characteristics of the sensor array were further examined for different gases, including methanol, isopropanol, ethanol, ammonia, hydrogen sulphide and hydrogen. Radar plots were used to improve the selective detection of different gases and enable effective classification

Miniaturized multisensor system with a thermal gradient: Performance beyond the calibration range

Two microchips, each with four identical microstructured sensors using SnO2 nanowires as sensing material (one chip decorated with Ag nanoparticles, the other with Pt nanoparticles), were used as a nano-electronic nose to distinguish five different gases and estimate their concentrations. This innovative approach uses identical sensors working at different operating temperatures thanks to the thermal gradient created by an integrated microheater. A system with in-house developed hardware and software was used to collect signals from the eight sensors and combine them into eight-dimensional data vectors. These vectors were processed with a support vector machine allowing for qualitative and quantitative discrimination of all gases after calibration. The system worked perfectly within the calibrated range (100% correct classification, 6.9% average error on concentration value). This work focuses on minimizing the number of points needed for calibration while maintaining good sensor performance, both for classification and error in estimating concentration. Therefore, the calibration range (in terms of gas concentration) was gradually reduced and further tests were performed with concentrations outside these new reduced limits. Although with only a few training points, down to just two per gas, the system performed well with 96% correct classifications and 31.7% average error for the gases at concentrations up to 25 times higher than its calibration range. At very low concentrations, down to 20 times lower than the calibration range, the system worked less well, with 93% correct classifications and 38.6% average error, probably due to proximity to the limit of detection of the sensors

Synthesis and anti-norovirus activity of pyranobenzopyrone compounds

During the last decade, noroviruses have gained media attention as the cause of large scale outbreaks of gastroenteritis on cruise ships, dormitories, nursing homes, etc. Although noroviruses do not multiply in food or water, they can cause large outbreaks because approximately 10–100 virions are sufficient to cause illness in a healthy adult. Recently, it was shown that the activity of acyl-coenzyme A:cholesterol acyltransferase-1 (ACAT1) enzyme may be important in norovirus infection. In search of anti-noroviral agents based on the inhibition of ACAT1, we synthesized and evaluated the inhibitory activities of a class of pyranobenzopyrone molecules containing amino, pyridine, substituted quinolines, or 7,8-benzoquinoline nucleus. Three of the sixteen evaluated compounds possess ED[subscript]5[subscript]0 values in the low micrometer range. 2-Quinolylmethyl derivative 3A and 4-quinolylmethyl derivative 4A showed ED[subscript]5[subscript]0 values of 3.4 and 2.4 [mu]M and TD[subscript]5[subscript]0 values of >200 and 96.4 [mu]M, respectively. The identified active compounds are suitable for further modification for the development of anti-norovirus agents

LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching

Obtaining large pre-trained models that can be fine-tuned to new tasks with limited annotated samples has remained an open challenge for medical imaging data. While pre-trained deep networks on ImageNet and vision-language foundation models trained on web-scale data are prevailing approaches, their effectiveness on medical tasks is limited due to the significant domain shift between natural and medical images. To bridge this gap, we introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets, covering a large number of organs and modalities such as CT, MRI, X-ray, and Ultrasound. We benchmark several state-of-the-art self-supervised algorithms on this dataset and propose a novel self-supervised contrastive learning algorithm using a graph-matching formulation. The proposed approach makes three contributions: (i) it integrates prior pair-wise image similarity metrics based on local and global information; (ii) it captures the structural constraints of feature embeddings through a loss function constructed via a combinatorial graph-matching objective; and (iii) it can be trained efficiently end-to-end using modern gradient-estimation techniques for black-box solvers. We thoroughly evaluate the proposed LVM-Med on 15 downstream medical tasks ranging from segmentation and classification to object detection, and both for the in and out-of-distribution settings. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models. For challenging tasks such as Brain Tumor Classification or Diabetic Retinopathy Grading, LVM-Med improves previous vision-language models trained on 1 billion masks by 6-7% while using only a ResNet-50.Comment: Update Appendi

A Schrödinger Equation for Evolutionary Dynamics

We establish an analogy between the Fokker–Planck equation describing evolutionary landscape dynamics and the Schrödinger equation which characterizes quantum mechanical particles, showing that a population with multiple genetic traits evolves analogously to a wavefunction under a multi-dimensional energy potential in imaginary time. Furthermore, we discover within this analogy that the stationary population distribution on the landscape corresponds exactly to the ground-state wavefunction. This mathematical equivalence grants entry to a wide range of analytical tools developed by the quantum mechanics community, such as the Rayleigh–Ritz variational method and the Rayleigh–Schrödinger perturbation theory, allowing us not only the conduct of reasonable quantitative assessments but also exploration of fundamental biological inquiries. We demonstrate the effectiveness of these tools by estimating the population success on landscapes where precise answers are elusive, and unveiling the ecological consequences of stress-induced mutagenesis—a prevalent evolutionary mechanism in pathogenic and neoplastic systems. We show that, even in an unchanging environment, a sharp mutational burst resulting from stress can always be advantageous, while a gradual increase only enhances population size when the number of relevant evolving traits is limited. Our interdisciplinary approach offers novel insights, opening up new avenues for deeper understanding and predictive capability regarding the complex dynamics of evolving populations