Fast simulation of the CEPC detector with Delphes

Fast simulation tools are highly appreciated in particle physics phenomenology studies, especially in the exploration of the physics potential of future experimental facilities. The Circular Electron Positron Collider is a proposed Higgs and Z factory that can precisely measure the Higgs boson properties and the electroweak precision observables. A fast-simulation toolkit dedicated to the CEPC detector has been developed using Delphes. The comparison shows that this fast simulation tool is highly consistent with the full simulation, on a set of benchmark distributions. Therefore, we recommend this fast simulation toolkit for CEPC phenomenological investigations

1,4-Bis[(2-ethyl-1H-benzimidazol-1-yl)meth­yl]benzene

In the title mol­ecule, C26H26N4, the central benzene ring forms dihedral angles of 89.9 (2) and 85.4 (2)° with the two benzimidazole rings

catena-Poly[[[aqua­bis­(4,4′-bipyridine-κN)zinc]-μ-l-tyrosinato-κ3 N,O 1:O 1′] nitrate dihydrate]

In the title compound, {[Zn(C9H10NO3)(C10H8N2)2(H2O)]NO3·2H2O}n, the ZnII atom is six-coordinated in a distorted octa­hedral geometry by two carboxyl­ate O atoms and one amino N atom from two l-tyrosinate ligands, two N atoms from two 4,4′-bipyridine ligands, and one water mol­ecule. Adjacent Zn atoms are bridged by the bidentate carboxyl­ate groups into a cationic chain extending along [010]. N—H⋯N, O—H⋯N and O—H⋯O hydrogen bonds link the cationic chains, nitrate anions and uncoordinated water mol­ecules into a supra­molecular network. π–π inter­actions between the pyridine rings and between the pyridine and benzene rings [centroid–centroid distances = 3.615 (4) and 3.636 (4) Å] are present

Mars Atmospheric Entry Integrated Navigation with Partial Intermittent Measurements

Signal degradation suffered by the vehicle is a combination brownout and blackout during Mars atmospheric entry. The communications brownout means that signal fades and blackout means that the signal is lost completely. The communications brownout and blackout periods are analyzed and predicted with an altitude and velocity profiles. In the brownout period, the range measurements between the vehicle and the orbiters are modeled as intermittent measurements with the radio signal arrival probabilities, which are distributed as a Rayleigh distribution of the electron number density around the entry vehicle. A new integrated navigation strategy during the Mars atmospheric entry phase is proposed to consider the probabilities of the radio measurements in the communications brownout and blackout periods under the IMU/beacon scenario based on the information filter with intermittent measurements. Numerical navigation simulations are designed to show the performance of the proposed navigation strategy under the integrated navigation scenario

Impact of the Staphylococcus epidermidis LytSR two-component regulatory system on murein hydrolase activity, pyruvate utilization and global transcriptional profile

<p>Abstract</p> <p>Background</p> <p><it>Staphylococcus epidermidis </it>has emerged as one of the most important nosocomial pathogens, mainly because of its ability to colonize implanted biomaterials by forming a biofilm. Extensive studies are focused on the molecular mechanisms involved in biofilm formation. The LytSR two-component regulatory system regulates autolysis and biofilm formation in <it>Staphylococcus aureus</it>. However, the role of LytSR played in <it>S. epidermidis </it>remained unknown.</p> <p>Results</p> <p>In the present study, we demonstrated that <it>lytSR </it>knock-out in <it>S. epidermidis </it>did not alter susceptibility to Triton X-100 induced autolysis. Quantitative murein hydrolase assay indicated that disruption of <it>lytSR </it>in <it>S. epidermidis </it>resulted in decreased activities of extracellular murein hydrolases, although zymogram showed no apparent differences in murein hydrolase patterns between <it>S. epidermidis </it>strain 1457 and its <it>lytSR </it>mutant. Compared to the wild-type counterpart, 1457<it>ΔlytSR</it> produced slightly more biofilm, with significantly decreased dead cells inside. Microarray analysis showed that <it>lytSR </it>mutation affected the transcription of 164 genes (123 genes were upregulated and 41 genes were downregulated). Specifically, genes encoding proteins responsible for protein synthesis, energy metabolism were downregulated, while genes involved in amino acid and nucleotide biosynthesis, amino acid transporters were upregulated. Impaired ability to utilize pyruvate and reduced activity of arginine deiminase was observed in 1457<it>ΔlytSR</it>, which is consistent with the microarray data.</p> <p>Conclusions</p> <p>The preliminary results suggest that in <it>S. epidermidis </it>LytSR two-component system regulates extracellular murein hydrolase activity, bacterial cell death and pyruvate utilization. Based on the microarray data, it appears that <it>lytSR </it>inactivation induces a stringent response. In addition, LytSR may indirectly enhance biofilm formation by altering the metabolic status of the bacteria.</p

Exact algorithms to minimize interference in wireless sensor networks

AbstractFinding a low-interference connected topology is a fundamental problem in wireless sensor networks (WSNs). The problem of reducing interference through adjusting the nodes’ transmission radii in a connected network is one of the most well-known open algorithmic problems in wireless sensor network optimization. In this paper, we study minimization of the average interference and the maximum interference for the highway model, where all the nodes are arbitrarily distributed on a line. First, we prove that there is always an optimal topology with minimum interference that is planar. Then, two exact algorithms are proposed. The first one is an exact algorithm to minimize the average interference in polynomial time, O(n3Δ), where n is the number of nodes and Δ is the maximum node degree. The second one is an exact algorithm to minimize the maximum interference in sub-exponential time, O(n3ΔO(k)), where k=O(Δ) is the minimum maximum interference. All the optimal topologies constructed are planar

Accelerated Sparse Recovery via Gradient Descent with Nonlinear Conjugate Gradient Momentum

This paper applies an idea of adaptive momentum for the nonlinear conjugate gradient to accelerate optimization problems in sparse recovery. Specifically, we consider two types of minimization problems: a (single) differentiable function and the sum of a non-smooth function and a differentiable function. In the first case, we adopt a fixed step size to avoid the traditional line search and establish the convergence analysis of the proposed algorithm for a quadratic problem. This acceleration is further incorporated with an operator splitting technique to deal with the non-smooth function in the second case. We use the convex $\ell_1$ and the nonconvex $\ell_1-\ell_2$ functionals as two case studies to demonstrate the efficiency of the proposed approaches over traditional methods

Evoke: Evoking Critical Thinking Abilities in LLMs via Reviewer-Author Prompt Editing

Large language models (LLMs) have made impressive progress in natural language processing. These models rely on proper human instructions (or prompts) to generate suitable responses. However, the potential of LLMs are not fully harnessed by commonly-used prompting methods: many human-in-the-loop algorithms employ ad-hoc procedures for prompt selection; while auto prompt generation approaches are essentially searching all possible prompts randomly and inefficiently. We propose Evoke, an automatic prompt refinement framework. In Evoke, there are two instances of a same LLM: one as a reviewer (LLM-Reviewer), it scores the current prompt; the other as an author (LLM-Author), it edits the prompt by considering the edit history and the reviewer's feedback. Such an author-reviewer feedback loop ensures that the prompt is refined in each iteration. We further aggregate a data selection approach to Evoke, where only the hard samples are exposed to the LLM. The hard samples are more important because the LLM can develop deeper understanding of the tasks out of them, while the model may already know how to solve the easier cases. Experimental results show that Evoke significantly outperforms existing methods. For instance, in the challenging task of logical fallacy detection, Evoke scores above 80, while all other baseline methods struggle to reach 20