On the Meaning of Berry Force For Unrestricted Systems Treated With Mean-Field Electronic Structure

We show that the Berry force as computed by an approximate, mean-field electronic structure can be meaningful if properly interpreted. In particular, for a model Hamiltonian representing a molecular system with an even number of electrons interacting via a two-body (Hubbard) interaction and a spin-orbit coupling, we show that a meaningful nonzero Berry force emerges whenever there is spin unrestriction--even though the Hamiltonian is real-valued and formally the on-diagonal single-surface Berry force must be zero. Moreover, if properly applied, this mean-field Berry force yields roughly the correct asymptotic motion for scattering through an avoided crossing. That being said, within the context of a ground-state calculation, several nuances do arise as far interpreting the Berry force correctly, and as a practical matter, the Berry force diverges near the Coulson-Fisher point (which can lead to numerical instabilities). We do not address magnetic fields here

GLARE: A Dataset for Traffic Sign Detection in Sun Glare

Real-time machine learning detection algorithms are often found within autonomous vehicle technology and depend on quality datasets. It is essential that these algorithms work correctly in everyday conditions as well as under strong sun glare. Reports indicate glare is one of the two most prominent environment-related reasons for crashes. However, existing datasets, such as LISA and the German Traffic Sign Recognition Benchmark, do not reflect the existence of sun glare at all. This paper presents the GLARE traffic sign dataset: a collection of images with U.S based traffic signs under heavy visual interference by sunlight. GLARE contains 2,157 images of traffic signs with sun glare, pulled from 33 videos of dashcam footage of roads in the United States. It provides an essential enrichment to the widely used LISA Traffic Sign dataset. Our experimental study shows that although several state-of-the-art baseline methods demonstrate superior performance when trained and tested against traffic sign datasets without sun glare, they greatly suffer when tested against GLARE (e.g., ranging from 9% to 21% mean mAP, which is significantly lower than the performances on LISA dataset). We also notice that current architectures have better detection accuracy (e.g., on average 42% mean mAP gain for mainstream algorithms) when trained on images of traffic signs in sun glare

Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks

Social media has been developing rapidly in public due to its nature of spreading new information, which leads to rumors being circulated. Meanwhile, detecting rumors from such massive information in social media is becoming an arduous challenge. Therefore, some deep learning methods are applied to discover rumors through the way they spread, such as Recursive Neural Network (RvNN) and so on. However, these deep learning methods only take into account the patterns of deep propagation but ignore the structures of wide dispersion in rumor detection. Actually, propagation and dispersion are two crucial characteristics of rumors. In this paper, we propose a novel bi-directional graph model, named Bi-Directional Graph Convolutional Networks (Bi-GCN), to explore both characteristics by operating on both top-down and bottom-up propagation of rumors. It leverages a GCN with a top-down directed graph of rumor spreading to learn the patterns of rumor propagation, and a GCN with an opposite directed graph of rumor diffusion to capture the structures of rumor dispersion. Moreover, the information from the source post is involved in each layer of GCN to enhance the influences from the roots of rumors. Encouraging empirical results on several benchmarks confirm the superiority of the proposed method over the state-of-the-art approaches.Comment: 8 pages, 4 figures, AAAI 202

Hypertensive nephropathy treatment by heart-protecting musk pill: a study of anti-inflammatory therapy for target organ damage of hypertension

This study was designed to investigate the protective effect of the heart-protecting musk pill (HMP) on inflammatory injury of kidney from spontaneously hypertensive rat (SHR). Male SHRs aged 4 weeks were divided into SHR model group, HMP low-dosage group (13.5 mg/kg), and HMP high-dosage group (40 mg/kg). Age-matched Wistar–Kyoto rats were used as normal control. All rats were killed at 12 weeks of age. Tail-cuff method and enzyme-linked immunosorbent assay were used to determine rat systolic blood pressure and angiotensin II (Ang II) contents, respectively. Renal inflammatory damage was evaluated by the following parameters: protein expressions of inflammatory cytokines, carbonyl protein contents, nitrite concentration, infiltration of monocytes/macrophages in interstitium and glomeruli, kidney pathological changes, and excretion rate of urinary protein. HMP did not prevent the development of hypertension in SHR. However, this Chinese medicinal compound decreased renal Ang II content. Consistent with the change of renal Ang II, all the parameters of renal inflammatory injury were significantly decreased by HMP. This study indicates that HMP is a potent suppressor of renal inflammatory damage in SHR, which may serve as a basis for the advanced preventive and therapeutic investigation of HMP in hypertensive nephropathy

Co-Check: Collaborative Outsourced Data Auditing in Multicloud Environment

With the increasing demand for ubiquitous connectivity, wireless technology has significantly improved our daily lives. Meanwhile, together with cloud-computing technology (e.g., cloud storage services and big data processing), new wireless networking technology becomes the foundation infrastructure of emerging communication networks. Particularly, cloud storage has been widely used in services, such as data outsourcing and resource sharing, among the heterogeneous wireless environments because of its convenience, low cost, and flexibility. However, users/clients lose the physical control of their data after outsourcing. Consequently, ensuring the integrity of the outsourced data becomes an important security requirement of cloud storage applications. In this paper, we present Co-Check, a collaborative multicloud data integrity audition scheme, which is based on BLS (Boneh-Lynn-Shacham) signature and homomorphic tags. According to the proposed scheme, clients can audit their outsourced data in a one-round challenge-response interaction with low performance overhead. Our scheme also supports dynamic data maintenance. The theoretical analysis and experiment results illustrate that our scheme is provably secure and efficient