Transverse conductance in a three-dimensional Weyl semimetal and Weyl superconductor hybrid under a strong magnetic field

There are two competing pairing mechanisms for the superconductivity of doped Weyl semimetals, i.e., the internode Bardeen-Cooper-Schrieffer (BCS) pairing and the intranode Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) pairing. To understand the edge excitations at the interface between the Weyl semimetal and the superconducting Weyl semimetal (WSM/SWSM) mediated by two different pairings, we study the energy dispersions and the density of states under a strong magnetic field. It is found that only the chiral zeroth Landau level exhibits a significant difference for the two pairings; the excitation spectra of higher Landau levels are insensitive to the way of pairings. In the vicinity of interface in the hybrid of WSM/SWSM, the spatial distributions of transverse current and the transverse conductance are independent of pairing mechanism. The pairing independence in the macroscopic conductance can be understood with the quantum effect of phase-coherent electron-hole states at the WSM/SWSM interface, which is responsible for the magnetically induced edge states supported by the Weyl Landau levels

Solar Thermal Energy Storage Using Paraffins as Phase Change Materials for Air Conditioning in the Built Environment

Thermal energy storage (TES) using phase change materials (PCMs) has received increasing attention since the last decades, due to its great potential for energy savings and energy management in the building sector. As one of the main categories of organic PCMs, paraffins exhibit favourable phase change temperatures for solar thermal energy storage. Its application is therefore effective to overcome the intermittent problem of solar energy utilisation, thereby reducing the power consumption of heating, ventilation and air conditioning (HVAC) systems and domestic hot water (DHW) systems. This chapter reviews the development and performance evaluation of solar thermal energy storage using paraffin-based PCMs in the built environment. Two case studies of solar-assisted radiant heating and desiccant cooling systems with integrated paraffin-based PCM TES were also presented. The results showed that paraffin-based PCM TES systems can rationalise the utilisation of solar thermal energy for air conditioning while maintaining a comfortable indoor environment

Mapping and functional characterization of structural variation in 1060 pig genomes

BACKGROUND: Structural variations (SVs) have significant impacts on complex phenotypes by rearranging large amounts of DNA sequence.RESULTS: We present a comprehensive SV catalog based on the whole-genome sequence of 1060 pigs (Sus scrofa) representing 101 breeds, covering 9.6% of the pig genome. This catalog includes 42,487 deletions, 37,913 mobile element insertions, 3308 duplications, 1664 inversions, and 45,184 break ends. Estimates of breed ancestry and hybridization using genotyped SVs align well with those from single nucleotide polymorphisms. Geographically stratified deletions are observed, along with known duplications of the KIT gene, responsible for white coat color in European pigs. Additionally, we identify a recent SINE element insertion in MYO5A transcripts of European pigs, potentially influencing alternative splicing patterns and coat color alterations. Furthermore, a Yorkshire-specific copy number gain within ABCG2 is found, impacting chromatin interactions and gene expression across multiple tissues over a stretch of genomic region of ~200 kb. Preliminary investigations into SV's impact on gene expression and traits using the Pig Genotype-Tissue Expression (PigGTEx) data reveal SV associations with regulatory variants and gene-trait pairs. For instance, a 51-bp deletion is linked to the lead eQTL of the lipid metabolism regulating gene FADS3, whose expression in embryo may affect loin muscle area, as revealed by our transcriptome-wide association studies.CONCLUSIONS: This SV catalog serves as a valuable resource for studying diversity, evolutionary history, and functional shaping of the pig genome by processes like domestication, trait-based breeding, and adaptive evolution.</p

A compendium of genetic regulatory effects across pig tissues

The Farm Animal Genotype-Tissue Expression (FarmGTEx) project has been established to develop a public resource of genetic regulatory variants in livestock, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biological discovery and exploitation in animal breeding and human biomedicine. Here we show results from the pilot phase of PigGTEx by processing 5,457 RNA-sequencing and 1,602 whole-genome sequencing samples passing quality control from pigs. We build a pig genotype imputation panel and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We evaluate tissue specificity of regulatory effects and elucidate molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying 207 pig complex phenotypes and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, supporting the importance of pigs as a human biomedical model.</p

The magneto-thermoelectric effect of graphene with intra-valley scattering

We present a qualitative and quantitative study of the magneto-thermoelectric effect of graphene. In the limit of impurity scattering length being much longer than the lattice constant, the intra-valley scattering dominates the charge and thermal transport. The self-energy and the Green\u27s functions are calculated in the self-consistent Born approximation. It is found that the longitudinal thermal conductivity splits into double peaks at high Landau levels and exhibits oscillations which are out of phase with the electric conductivity. The chemical potential-dependent electrical resistivity, the thermal conductivities, the Seebeck coefficient, and the Nernst coefficient are obtained. The results are in good agreement with the experimental observations

Thermoelectric and thermal transport properties of graphene under strong magnetic field

We investigate theoretically the thermoelectric and thermal transport properties of graphene under strong magnetic field in the presence of short-range scatterers. The numerical results are in good agreement with available experimental data for all thermoelectric quantities except for the Seebeck coefficient near zero chemical potential. This anomaly is attributed to the overestimation of the longitudinal resistivity. Furthermore, we find an anomalous oscillation in the transverse thermal conductivity at the lowest Landau level and double peaks in the longitudinal thermal conductivity at higher Landau levels, which are expected to be observed in future experiments on high mobility graphene samples. An important finding of our work is that the thermoelectric figure of merit ZT can be as high as 2.4 for a magnetic field under which the chemical potential is pinned to the [n=1] Landau level. This finding can greatly advance the thermoelectric application of graphene

A forward error compensation approach for fault resilient deep neural network accelerator design

Deep learning accelerator is a key enabler of a variety of safety-critical applications such as self-driving car and video surveillance. However, recently reported hardware-oriented attack vectors, e.g., fault injection attacks, have extended the threats on deployed deep neural network (DNN) systems beyond the software attack boundary by input data perturbation. Existing fault mitigation schemes including data masking, zeroing-on-error and circuit level time-borrowing techniques exploit the noise-tolerance of neural network models to resist random and sparse errors. Such noise tolerant-based schemes are not sufficiently effective to suppress intensive transient errors if a DNN accelerator is blasted with malicious and deliberate faults. In this paper, we conduct comprehensive investigations on reported resilient designs and propose a more robust countermeasure to fault injection attacks. The proposed design utilizes shadow flip flops for error detection and lightweight circuit for timely error correction. Our forward error compensation scheme rectifies the incorrect partial sum of the multiply-accumulation operation by estimating the difference between the correct and error-inflicted computation. The difference is added back to the final accumulated result at a later cycle without stalling the execution pipeline. We implemented our proposed design and the existing fault-mitigation schemes on the same Intel FPGA-based DNN accelerator to demonstrate its substantially enhanced resiliency against deliberate fault attacks on two popular DNN models, ResNet50 and VGG16, trained with ImageNet.National Research Foundation (NRF)Submitted/Accepted versionThis research is supported by the National Research Foundation, Singapore, under its National Cybersecurity Research & Development Programme/Cyber-Hardware Forensic & Assurance Evaluation R&D Programme (Award: CHFA-GC1-AW01)

Analysis of circuit aging on accuracy degradation of deep neural network accelerator

Deep neural networks have achieved phenomenal successes in vision recognition tasks, which motivate the deployment of deep learning in portable and smart wearable devices. To overcome the fundamental challenges of power and resource limitation, application-specific integrated circuit accelerators have emerged to compact the model and use lower precision arithmetic to increase the throughput of computation with reduced power consumption. Although very high energy efficiency has been achieved by removing redundant weights, compressing data and even sacrificing timing margin, such trend in hardware acceleration that pushes the deep learning systems to the error threshold can be disastrous for the tasks they performed due to failure or degraded performance of circuit components. Concerned by the lack of attention on the evolving unreliability effects in artificial intelligent accelerators implemented by the continuously scaled CMOS technology, this paper is the first to evaluate the effect of circuit aging on performance degradation of deep learning accelerator. Our findings indicate that DNN system running at their peak throughput rate can experience up to 84% accuracy drop after a year of aging and the accumulation of errors aggravates with the depth of learning. It is also found that relaxation of throughput rate can slow down the loss of classification accuracy considerably.MOE (Min. of Education, S’pore)Accepted versio