Vertex corrections in localized and extended systems

Within many-body perturbation theory we apply vertex corrections to various closed-shell atoms and to jellium, using a local approximation for the vertex consistent with starting the many-body perturbation theory from a DFT-LDA Green's function. The vertex appears in two places -- in the screened Coulomb interaction, W, and in the self-energy, \Sigma -- and we obtain a systematic discrimination of these two effects by turning the vertex in \Sigma on and off. We also make comparisons to standard GW results within the usual random-phase approximation (RPA), which omits the vertex from both. When a vertex is included for closed-shell atoms, both ground-state and excited-state properties demonstrate only limited improvements over standard GW. For jellium we observe marked improvement in the quasiparticle band width when the vertex is included only in W, whereas turning on the vertex in \Sigma leads to an unphysical quasiparticle dispersion and work function. A simple analysis suggests why implementation of the vertex only in W is a valid way to improve quasiparticle energy calculations, while the vertex in \Sigma is unphysical, and points the way to development of improved vertices for ab initio electronic structure calculations.Comment: 8 Pages, 6 Figures. Updated with quasiparticle neon results, extended conclusions and references section. Minor changes: Updated references, minor improvement

A MOC-based neutron kinetics model for noise analysis

A 2-D noise model is implemented in the deterministic reactor code APOLLO3® to simulate a periodic oscillation of a structural component. The Two/Three Dimensional Transport (TDT) solver, using the Method of Characteristics, is adopted for the calculation of the case studies, constituted by a moving detector and control-rod bundle. The periodic movement is built by properly linking the geometries corresponding to the temporal positions. The calculation is entirely performed in the real time domain, without resorting to the traditional frequency approach. A specifically defined dynamic eigenvalue is used to renormalize in average the reactivity over a period. The algorithm is accelerated by the DPN synthetic method. For each cell of the domain, the time values of fission rates are analysed to determine the noise extent. Moreover we propose a systematic approach to the definition of the macroscopic cross sections to be used in dynamical calculations starting from library data. As an aside of our work we have found that even in static calculation this approach can produce significant changes

An Experimentally Verified Attack on Full Grain-128 Using Dedicated Reconfigurable Hardware

In this paper we describe the first single-key attack which can recover the full key of the full version of Grain-128 for arbitrary keys by an algorithm which is significantly faster than exhaustive search (by a factor of about 238). It is based on a new version of a cube tester, which uses an improved choice of dynamic variables to eliminate the previously made assumption that ten particular key bits are zero. In addition, the new attack is much faster than the previous weak-key attack, and has a simpler key recovery process. Since it is extremely difficult to mathemat-ically analyze the expected behavior of such attacks, we implemented it on RIVYERA, which is a new massively parallel reconfigurable hardware, and tested its main components for dozens of random keys. These tests experimentally verified the correctness and expected complexity of the attack, by finding a very significant bias in our new cube tester for about 7.5 % of the keys we tested. This is the first time that the main compo-nents of a complex analytical attack are successfully realized against a full-size cipher with a special-purpose machine. Moreover, it is also the first attack that truly exploits the configurable nature of an FPGA-based cryptanalytical hardware

Modern computing: Vision and challenges

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress

Dose escalation and pharmacokinetic study of a humanized anti-HER2 monoclonal antibody in patients with HER2/neu-overexpressing metastatic breast cancer

We conducted a phase I pharmacokinetic dose escalation study of a recombinant humanized anti-p185HER2 monoclonal antibody (MKC-454) in 18 patients with metastatic breast cancer refractory to chemotherapy. Three or six patients at each dose level received 1, 2, 4 and 8 mg kg–1 of MKC-454 as 90-min intravenous infusions. The first dose was followed in 3 weeks by nine weekly doses. Target trough serum concentration has been set at 10 μg ml–1 based on in vitro observations. The mean value of minimum trough serum concentrations at each dose level were 3.58 ± 0.63, 6.53 ± 5.26, 40.2 ± 7.12 and 87.9 ± 23.5 μg ml–1 respectively. At 2 mg kg–1, although minimum trough serum concentrations were lower than the target trough concentration with a wide range of variation, trough concentrations increased and exceeded the target concentration, as administrations were repeated weekly. Finally 2 mg kg–1 was considered to be sufficient to achieve the target trough concentration by the weekly dosing regimen. One patient receiving 1 mg kg–1 had grade 3 fever, one at the 1 mg kg–1 level had severe fatigue defined as grade 3, and one at 8 mg kg–1 had severe bone pain of grade 3. No antibodies against MKC-454 were detected in any patients. Objective tumour responses were observed in two patients; one receiving 4 mg kg–1 had a partial response in lung metastases and the other receiving 8 mg kg–1 had a complete response in soft tissue metastases. These results indicate that MKC-454 is well tolerated and effective in patients with refractory metastatic breast cancers overexpressing the HER2 proto-oncogene. Further evaluation of this agent with 2–4 mg kg–1 weekly intravenous infusion is warranted. © 1999 Cancer Research Campaig

Correlation Cube Attacks: From Weak-Key Distinguisher to Key Recovery

In this paper, we describe a new variant of cube attacks called correlation cube attack. The new attack recovers the secret key of a cryptosystem by exploiting conditional correlation properties between the superpoly of a cube and a specific set of low-degree polynomials that we call a basis, which satisfies that the superpoly is a zero constant when all the polynomials in the basis are zeros. We present a detailed procedure of correlation cube attack for the general case, including how to find a basis of the superpoly of a given cube. One of the most significant advantages of this new analysis technique over other variants of cube attacks is that it converts from a weak-key distinguisher to a key recovery attack. As an illustration, we apply the attack to round-reduced variants of the stream cipher Trivium. Based on the tool of numeric mapping introduced by Liu at CRYPTO 2017, we develop a specific technique to efficiently find a basis of the superpoly of a given cube as well as a large set of potentially good cubes used in the attack on Trivium variants, and further set up deterministic or probabilistic equations on the key bits according to the conditional correlation properties between the superpolys of the cubes and their bases. For a variant when the number of initialization rounds is reduced from 1152 to 805, we can recover about 7-bit key information on average with time complexity $2^{44}$, using $2^{45}$ keystream bits and preprocessing time $2^{51}$. For a variant of Trivium reduced to 835 rounds, we can recover about 5-bit key information on average with the same complexity. All the attacks are practical and fully verified by experiments. To the best of our knowledge, they are thus far the best known key recovery attacks for these variants of Trivium, and this is the first time that a weak-key distinguisher on Trivium stream cipher can be converted to a key recovery attack

EEG artifacts reduction by multivariate empirical mode decomposition and multiscale entropy for monitoring depth of anaesthesia during surgery

Electroencephalography (EEG) has been widely utilized to measure the depth of anaesthesia (DOA) during operation. However, the EEG signals are usually contaminated by artifacts which have a consequence on the measured DOA accuracy. In this study, an effective and useful filtering algorithm based on multivariate empirical mode decomposition and multiscale entropy (MSE) is proposed to measure DOA. Mean entropy of MSE is used as an index to find artifacts-free intrinsic mode functions. The effect of different levels of artifacts on the performances of the proposed filtering is analysed using simulated data. Furthermore, 21 patients' EEG signals are collected and analysed using sample entropy to calculate the complexity for monitoring DOA. The correlation coefficients of entropy and bispectral index (BIS) results show 0.14 ± 0.30 and 0.63 ± 0.09 before and after filtering, respectively. Artificial neural network (ANN) model is used for range mapping in order to correlate the measurements with BIS. The ANN method results show strong correlation coefficient (0.75 ± 0.08). The results in this paper verify that entropy values and BIS have a strong correlation for the purpose of DOA monitoring and the proposed filtering method can effectively filter artifacts from EEG signals. The proposed method performs better than the commonly used wavelet denoising method. This study provides a fully adaptive and automated filter for EEG to measure DOA more accuracy and thus reduce risk related to maintenance of anaesthetic agents.This research was financially supported by the Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is sponsored by Ministry of Science and Technology (Grant Number: NSC102-2911-I-008-001). Also, it was supported by Chung-Shan Institute of Science and Technology in Taiwan (Grant Numbers: CSIST-095-V301 and CSIST-095-V302) and National Natural Science Foundation of China (Grant Number: 51475342)

Inferring the dynamics of rising radical right-wing party support using Gaussian processes

The use of classical regression techniques in social science can prevent the discovery of complex, nonlinear mechanisms, and often relies too heavily on both the expertise and prior expectations of the data analyst. In this paper, we present a regression methodology that combines the interpretability of traditional, well used, statistical methods with the full predictability and flexibility of Bayesian statistics techniques. Our modelling approach allow us to find and explain the mechanisms behind the rise of Radical Right-wing Populist parties (RRPs), that we would have been unable to find using traditional methods. Using Swedish municipality level data (2002-2018) we find no evidence that the proportion of foreignborn residents is predictive of increases in RRP support. Instead, education levels and population density are the significant variables that impact the change in support for the RRP, in addition to spatial and temporal control variables. We argue that our methodology, which produces models with considerably better fit of the complexity and nonlinearities often found in social systems, provides a better tool for hypothesis testing and exploration of theories about RRPs and other social movements