Unipolar Resistance Switching in Amorphous High-k dielectrics Based on Correlated Barrier Hopping Theory

We have proposed a kind of nonvolatile resistive switching memory based on amorphous LaLuO3, which has already been established as a promising candidate of high-k gate dielectric employed in transistors. Well-developed unipolar switching behaviors in amorphous LaLuO3 make it suited for not only logic but memory applications using the conventional semiconductor or the emerging nano/CMOS architectures. The conduction transition between high- and low- resistance states is attributed to the change in the separation between oxygen vacancy sites in the light of the correlated barrier hopping theory. The mean migration distances of vacancies responsible for the resistive switching are demonstrated in nanoscale, which could account for the ultrafast programming speed of 6 ns. The origin of the distributions in switching parameters in oxides can be well understood according to the switching principle. Furthermore, an approach has also been developed to make the operation voltages predictable for the practical applications of resistive memories.Comment: 18 pages, 6 figure

Integrating Chemistry Knowledge in Large Language Models via Prompt Engineering

This paper presents a study on the integration of domain-specific knowledge in prompt engineering to enhance the performance of large language models (LLMs) in scientific domains. A benchmark dataset is curated to encapsulate the intricate physical-chemical properties of small molecules, their drugability for pharmacology, alongside the functional attributes of enzymes and crystal materials, underscoring the relevance and applicability across biological and chemical domains.The proposed domain-knowledge embedded prompt engineering method outperforms traditional prompt engineering strategies on various metrics, including capability, accuracy, F1 score, and hallucination drop. The effectiveness of the method is demonstrated through case studies on complex materials including the MacMillan catalyst, paclitaxel, and lithium cobalt oxide. The results suggest that domain-knowledge prompts can guide LLMs to generate more accurate and relevant responses, highlighting the potential of LLMs as powerful tools for scientific discovery and innovation when equipped with domain-specific prompts. The study also discusses limitations and future directions for domain-specific prompt engineering development.Comment: 43 pages, 17 figure

Fast Chain-of-Thought: A Glance of Future from Parallel Decoding Leads to Answers Faster

In this work, we propose FastCoT, a model-agnostic framework based on parallel decoding without any further training of an auxiliary model or modification to the LLM itself. FastCoT uses a size-varying context window whose size changes with position to conduct parallel decoding and auto-regressive decoding simultaneously, thus fully utilizing GPU computation resources. In FastCoT, the parallel decoding part provides the LLM with a quick glance of the future composed of approximate tokens, which could lead to faster answers compared to regular autoregressive decoding used by causal transformers. We also provide an implementation of parallel decoding within LLM, which supports KV-cache generation and batch processing. Through extensive experiments, we demonstrate that FastCoT saves inference time by nearly 20% with only a negligible performance drop compared to the regular approach. Additionally, we show that the context window size exhibits considerable robustness for different tasks

Teaching Algorithm Design: A Literature Review

Algorithm design is a vital skill developed in most undergraduate Computer Science (CS) programs, but few research studies focus on pedagogy related to algorithms coursework. To understand the work that has been done in the area, we present a systematic survey and literature review of CS Education studies. We search for research that is both related to algorithm design and evaluated on undergraduate-level students. Across all papers in the ACM Digital Library prior to August 2023, we only find 94 such papers. We first classify these papers by topic, evaluation metric, evaluation methods, and intervention target. Through our classification, we find a broad sparsity of papers which indicates that many open questions remain about teaching algorithm design, with each algorithm topic only being discussed in between 0 and 10 papers. We also note the need for papers using rigorous research methods, as only 38 out of 88 papers presenting quantitative data use statistical tests, and only 15 out of 45 papers presenting qualitative data use a coding scheme. Only 17 papers report controlled trials. We then synthesize the results of the existing literature to give insights into what the corpus reveals about how we should teach algorithms. Much of the literature explores implementing well-established practices, such as active learning or automated assessment, in the algorithms classroom. However, there are algorithms-specific results as well: a number of papers find that students may under-utilize certain algorithmic design techniques, and studies describe a variety of ways to select algorithms problems that increase student engagement and learning. The results we present, along with the publicly available set of papers collected, provide a detailed representation of the current corpus of CS Education work related to algorithm design and can orient further research in the area

A Research on Community-Based Livestock of Qinghai-Tibet Plateau

Qinghai-Tibet Plateau locates in Southwestern China, covering the whole area of Tibet Autonomous Region, Qinghai Province, Southern part of Gansu Province, Northwestern part of Sichuan Province and Northwestern part of Yunnan Province, with an area of around 139.08 million hectares of natural grassland, accounting for 39% of the total area of natural grassland in China. It is also the largest natural ecozones in China and one of the least disturbed regions by human activities, with its air, water sources, soil, grassland, wildlife in their pristine state. Qinghai-Tibet Plateau is the native home for Tibetan people. Grassland animal husbandry is the foundation of the economy of QTP and the main source of livelihood for local nomadic people. During the long term of concerted evolution with the nature, Tibetan people living on Qinghai-Tibet Plateau have formed a uniquely holistic grassland ecological culture that is compatible with their production system and the ecosystem. The majority of Tibetan people observe Tibetan Buddhism. Their respect for nature and their belief in that all sentient beings are equal take deep root in their traditional culture. Their harmonious co-existence with nature exemplifies the eco-civilization ideas and provides a solid cultural foundation for both ecology conservation and featured animal husbandry development. On Qinghai-Tibet Plateau, national policies and initiatives such as dual contract of livestock and forage, natural grassland vegetation recovery, returning grazing land to grassland, grassland ecosystem subsidy and rewarding mechanism have been implemented, playing an important role in promoting grassland ecosystem conservation and grassland animal husbandry development. However, since grassland animal husbandry is a complex system involving grassland, farm animal, environment, society, economy, culture, etc, there are still many outstanding problems to be solved

Research on Direct Detection Method and Performance of Single-photon Counting Terahertz Radar

The conventional terahertz radar suffers from limited operation range for long-distance, noncooperative target detection due to the low transmitter power and atmospheric attenuation effect, both of which pose a hindrance in meeting the requirements of warning detection applications. To improve the radar detection capability, this paper studies an ultrasensitive target detection method based on single-photon detectors to replace traditional radar receivers. The method is expected to considerably expand the operation range of terahertz radars. First, the statistical law of the number of echo photons of a terahertz single-photon radar system is analyzed, and the echo characteristics of the target are expounded from a microscopic perspective. Furthermore, a terahertz single-photon target detection model, incorporating the characteristics of a quantum capacitor detector, is established. In addition, the mathematical expression of the target detection performance is derived, and the performance is evaluated via simulations. Further, a target detection performance curve is obtained. Finally, a time-resolved terahertz photon-counting mechanism experiment is performed, wherein we realize high-precision ranging by counting echo pulses. This work can provide support for the research and development of ultrasensitive target detection technologies and single-photon radar systems in the terahertz band

Ultra-compact lithium niobate photonic chip for high-capacity and energy-efficient wavelength-division-multiplexing transmitters

Recently, high-performance thin-film lithium niobate optical modulators have emerged that, together with advanced multiplexing technologies, are highly expected to satisfy the ever-growing demand for high-capacity optical interconnects utilizing multiple channels. Accordingly, in this study, a compact lithium-niobate-on-insulator (LNOI) photonic chip was adopted to establish four-channel wavelength-division-multiplexing (WDM) transmitters, comprising four optical modulators based on ultracompact 2 × 2 Fabry-Perot cavities and a four-channel WDM filter based on multimode waveguide gratings. The fabricated chip with four wavelength channels has a total footprint as compact as 0.3 × 2.8 mm2, and exhibits an excess loss of ~0.8 dB as well as low inter-channel crosstalk of < –22 dB. Using this LNOI photonic chip, high-capacity data transmissions of 320 Gbps (4 × 80 Gbps) on-off-keying signals and 400 Gbps (4 × 100 Gbps) four-level pulse amplitude signals were successfully realized with the ultra-low power consumption of 11.9 fJ/bit

Evolutionary Characterization of the Pandemic H1N1/ 2009 Influenza Virus in Humans Based on Non-Structural Genes

The 2009 influenza pandemic had a tremendous social and economic impact. To study the genetic diversity and evolution of the 2009 H1N1 virus, a mutation network for the non-structural (NS) gene of the virus was constructed. Strains of the 2009 H1N1 pandemic influenza A virus could be divided into two categories based on the V123I mutation in the NS1 gene: G1 (characterized as 123 Val) and G2 (characterized as 123 Ile). Sequence homology analysis indicated that one type of NS sequence, primarily isolated from Mexico, was likely the original type in this pandemic. The two genotypes of the virus presented distinctive clustering features in their geographic distributions. These results provide additional insight into the genetics and evolution of human pandemic influenza H1N1

Environmental Molecular Effect on the Macroscale Friction Behaviors of Graphene

This study investigated the friction behavior of graphene in air and nitrogen atmosphere environments. The microstructural evolution caused by the variation of atmosphere environments and its effect on the friction coefficient of the graphene is explored. It is demonstrated that graphene can exhibit excellent lubricating properties both in air and nitrogen atmosphere environments. In air, a highly ordered layer-by-layer slip structure can be formed at the sliding interface. Oxygen and H2O molecules can make edge dangling bonds and defects passive. Thus the interaction between the nanosheets and the layers of nanosheets is weak and the friction coefficient is low (0.06–0.07). While the friction coefficient increases to 0.14–0.15 in a nitrogen atmosphere due to the interaction of defects generated in the sliding process, the nitrogen molecules with lone pair electrons can only make the nanosheets passive to a certain degree, thus the ordered slip structure is destroyed and friction is higher. This work reveals the influence of environmental molecules on the macroscale tribological performances of graphene and its effect on the microstructure at the sliding interface, which could shed light on the lubricating performance of graphene in environmental atmospheres and help us to understand the tribological behaviors of graphite at the macroscale