System-adapted correlation energy density functionals from effective pair interactions

We present and discuss some ideas concerning an ``average-pair-density functional theory'', in which the ground-state energy of a many-electron system is rewritten as a functional of the spherically and system-averaged pair density. These ideas are further clarified with simple physical examples. We then show that the proposed formalism can be combined with density functional theory to build system-adapted correlation energy functionals. A simple approximation for the unknown effective electron-electron interaction that enters in this combined approach is described, and results for the He series and for the uniform electron gas are briefly reviewed.Comment: to appear in Phil. Mag. as part of Conference proceedings for the "Electron Correlations and Materials Properties", Kos Greece, July 5-9, 200

Social networks and labour productivity in Europe: An empirical investigation

This paper uses firm-level data recorded in the AMADEUS database to investigate the distribution of labour productivity in different European countries. We find that the upper tail of the empirical productivity distributions follows a decaying power-law, whose exponent $\alpha$ is obtained by a semi-parametric estimation technique recently developed by Clementi et al. (2006). The emergence of "fat tails" in productivity distribution has already been detected in Di Matteo et al. (2005) and explained by means of a model of social network. Here we show that this model is tested on a broader sample of countries having different patterns of social network structure. These different social attitudes, measured using a social capital indicator, reflect in the power-law exponent estimates, verifying in this way the existence of linkages among firms' productivity performance and social network.Comment: LaTeX2e; 18 pages with 3 figures; Journal of Economic Interaction and Coordination, in pres

Quantifying Temporal Entropy in Neuromorphic Memory Forgetting: Exploring Advanced Forgetting Models for Robust Long-term Information Storage

This paper presents a progression of a popular neuromorphic memory structure by exploring advanced forgetting models for robust long-term information storage. Inspired by biological neuronal systems, neuromorphic sensors efficiently capture and transmit sensory information using event-based communication. Managing the decay of information over time is a critical aspect, and forgetting models play a vital role in this process. Building upon the foundation of an existing popular neuromorphic memory structure, this study introduces and evaluates four advanced forgetting models: ROT, adaptive, emotional memory enhancement, and context-dependent memory forgetting models. Each model incorporates different factors to modulate the rate of decay or forgetting. Through rigorous experimentation and analysis, these models are compared with the original ROT forgetting model to assess their effectiveness in retaining relevant information while discarding irrelevant or outdated data. The results provide insights into the strengths, limitations, and potential applications of these advanced forgetting models in the context of neuromorphic memory systems, thereby contributing to the progression of this popular neuromorphic memory structure

Neuromorphic Event Alarm Time-Series Suppression

The field of neuromorphic vision systems aims to replicate the functionality of biological visual systems by mimicking their physical structure and electrical behaviour. Unlike traditional full-frame sensors, neuromorphic systems process data asynchronously and at the pixel level, modelling biological signalling processes. This allows for high-speed operation with lower energy consumption, making them suitable for applications like autonomous vehicles and embedded robotics. This work introduces the Neuromorphic Event Alarm Time-Series Suppression (NEATS) framework, designed to filter noise and detect outlier behaviours in event data without the need for 2-D transformations. NEATS employs rolling statistics and advanced neuromorphic data structures to minimise noise while identifying changes in scene dynamics. This framework injects attention into scene processing, similar to summarisation frameworks in traditional image processing. A novel event-vision alarm change collection (EACC) database is presented, containing controlled stimuli pattern changes captured using leading neuromorphic imaging devices. This database facilitates future benchmarking of neuromorphic attention frameworks, advancing the development of efficient and accurate artificial vision systems

BRCA1 and BRCA2 mutations in a population-based study of male breast cancer

Background: The contribution of BRCA1 and BRCA2 to the incidence of male breast cancer (MBC) in the United Kingdom is not known, and the importance of these genes in the increased risk of female breast cancer associated with a family history of breast cancer in a male first-degree relative is unclear. Methods: We have carried out a population-based study of 94 MBC cases collected in the UK. We screened genomic DNA for mutations in BRCA1 and BRCA2 and used family history data from these cases to calculate the risk of breast cancer to female relatives of MBC cases. We also estimated the contribution of BRCA1 and BRCA2 to this risk. Results: Nineteen cases (20%) reported a first-degree relative with breast cancer, of whom seven also had an affected second-degree relative. The breast cancer risk in female first-degree relatives was 2.4 times (95% confidence interval [CI] = 1.4–4.0) the risk in the general population. No BRCA1 mutation carriers were identified and five cases were found to carry a mutation in BRCA2. Allowing for a mutation detection sensitivity frequency of 70%, the carrier frequency for BRCA2 mutations was 8% (95% CI = 3–19). All the mutation carriers had a family history of breast, ovarian, prostate or pancreatic cancer. However, BRCA2 accounted for only 15% of the excess familial risk of breast cancer in female first-degree relatives. Conclusion: These data suggest that other genes that confer an increased risk for both female and male breast cancer have yet to be found

Optical signature of symmetry variations and spin-valley coupling in atomically thin tungsten dichalcogenides

Motivated by the triumph and limitation of graphene for electronic applications, atomically thin layers of group VI transition metal dichalcogenides are attracting extensive interest as a class of graphene-like semiconductors with a desired band-gap in the visible frequency range. The monolayers feature a valence band spin splitting with opposite sign in the two valleys located at corners of 1st Brillouin zone. This spin-valley coupling, particularly pronounced in tungsten dichalcogenides, can benefit potential spintronics and valleytronics with the important consequences of spin-valley interplay and the suppression of spin and valley relaxations. Here we report the first optical studies of WS2 and WSe2 monolayers and multilayers. The efficiency of second harmonic generation shows a dramatic even-odd oscillation with the number of layers, consistent with the presence (absence) of inversion symmetry in even-layer (odd-layer). Photoluminescence (PL) measurements show the crossover from an indirect band gap semiconductor at mutilayers to a direct-gap one at monolayers. The PL spectra and first-principle calculations consistently reveal a spin-valley coupling of 0.4 eV which suppresses interlayer hopping and manifests as a thickness independent splitting pattern at valence band edge near K points. This giant spin-valley coupling, together with the valley dependent physical properties, may lead to rich possibilities for manipulating spin and valley degrees of freedom in these atomically thin 2D materials

Novel Branches of (0,2) Theories

We show that recently proposed linear sigma models with torsion can be obtained from unconventional branches of conventional gauge theories. This observation puts models with log interactions on firm footing. If non-anomalous multiplets are integrated out, the resulting low-energy theory involves log interactions of neutral fields. For these cases, we find a sigma model geometry which is both non-toric and includes brane sources. These are heterotic sigma models with branes. Surprisingly, there are massive models with compact complex non-Kahler target spaces, which include brane/anti-brane sources. The simplest conformal models describe wrapped heterotic NS5-branes. We present examples of both types.Comment: 36 pages, LaTeX, 2 figures; typo in Appendix fixed; references added and additional minor change

Direct exfoliation and dispersion of two-dimensional materials in pure water via temperature control

The high-volume synthesis of two-dimensional (2D) materials in the form of platelets is desirable for various applications. While water is considered an ideal dispersion medium, due to its abundance and low cost, the hydrophobicity of platelet surfaces has prohibited its widespread use. Here we exfoliate 2D materials directly in pure water without using any chemicals or surfactants. In order to exfoliate and disperse the materials in water, we elevate the temperature of the sonication bath, and introduce energy via the dissipation of sonic waves. Storage stability greater than one month is achieved through the maintenance of high temperatures, and through atomic and molecular level simulations, we further discover that good solubility in water is maintained due to the presence of platelet surface charges as a result of edge functionalization or intrinsic polarity. Finally, we demonstrate inkjet printing on hard and flexible substrates as a potential application of water-dispersed 2D materials.close1