Optimized thermoelectric properties of Mo_3Sb_(7-x)Te_x with significant phonon scattering by electrons

Heavily doped compounds Mo_3Sb_(7−x)Te_x (x = 0, 1.0, 1.4, 1.8) were synthesized by solid state reaction and sintered by spark plasma sintering. Both X-ray diffraction and electron probe microanalysis indicated the maximum solubility of Te was around x = 1.8. The trends in the electrical transport properties can generally be understood using a single parabolic band model, which predicts that the extremely high carrier concentration of Mo_3Sb_7 (~10^(22) cm^(−3)) can be reduced to a nearly optimized level (~2 × 10^(21) cm^(−3)) for thermoelectric figure of merit (zT) by Te-substitution with x = 1.8. The increased lattice thermal conductivity by Te-doping was found to be due to the decreased Umklapp and electron–phonon scattering, according to a Debye model fitting. The thermoelectric figure of merit (zT) monotonously increased with increasing temperature and reached its highest value of about 0.51 at 850 K for the sample with x = 1.8, making these materials competitive with the state-of-the-art thermoelectric SiGe alloys. Evidence of significant electron–phonon scattering is found in the thermal conductivity

Group Signatures: Unconditional Security for Members

First a detailed definition of group signatures, originally suggested by Chaum and van {Heijst}, is given. Such signatures allow members of a group to sign messages anonymously on behalf of the group subject to the constraint that, in case of disputes later on, a designated authority can identify the signer. It is shown that if such schemes are to provide information theoretic anonymity, then the length of the secret information of the members and the authority increases with the number of members and the number of signatures each member is allowed to make. A dynamic scheme meeting these lower bounds is described. Unlike previous suggestions it protects each member unconditionally against framing, i.e.\ being held responsible for a signature made by someone else

Plastic Inorganic Semiconductors for Flexible Electronics

Featured with bendability and deformability, smartness and lightness, flexible materials and devices have wide applications in electronics, optoelectronics, and energy utilization. The key for flexible electronics is the integration of flexibility and decent electrical performance of semiconductors. It has long been realized that high-performance inorganic semiconductors are brittle, and the thinning-down-induced flexibility does not change the intrinsic brittleness. This inconvenient fact severely restricts the fabrication and service of inorganic semiconductors in flexible and deformable electronics. By contrast, flexible and soft polymers can be readily deformed but behave poorly in terms of electrical properties. Recently, Ag2S was discovered as the room-temperature ductile inorganic semiconductor. The intrinsic flexibility and plasticity of Ag2S are attributed to multicentered chemical bonding and solid linkage among easy slip planes. Furthermore, the electrical and thermoelectric properties of Ag2S can be readily optimized by Se/Te alloying while the ductility is maintained, giving birth to a high-efficiency full inorganic flexible thermoelectric device. This chapter briefly reviews this big discovery, relevant backgrounds, and research advances and tries to demonstrate a clear structure-performance correlation between crystal structure/chemical bonding and mechanical/electrical properties

Exploring the Potential of Large Language Models in Computational Argumentation

Computational argumentation has become an essential tool in various fields, including artificial intelligence, law, and public policy. It is an emerging research field in natural language processing (NLP) that attracts increasing attention. Research on computational argumentation mainly involves two types of tasks: argument mining and argument generation. As large language models (LLMs) have demonstrated strong abilities in understanding context and generating natural language, it is worthwhile to evaluate the performance of LLMs on various computational argumentation tasks. This work aims to embark on an assessment of LLMs, such as ChatGPT, Flan models and LLaMA2 models, under zero-shot and few-shot settings within the realm of computational argumentation. We organize existing tasks into 6 main classes and standardise the format of 14 open-sourced datasets. In addition, we present a new benchmark dataset on counter speech generation, that aims to holistically evaluate the end-to-end performance of LLMs on argument mining and argument generation. Extensive experiments show that LLMs exhibit commendable performance across most of these datasets, demonstrating their capabilities in the field of argumentation. We also highlight the limitations in evaluating computational argumentation and provide suggestions for future research directions in this field

A Differential Private Method for Distributed Optimization in Directed Networks via State Decomposition

In this paper, we study the problem of consensus-based distributed optimization where a network of agents, abstracted as a directed graph, aims to minimize the sum of all agents' cost functions collaboratively. In existing distributed optimization approaches (Push-Pull/AB) for directed graphs, all agents exchange their states with neighbors to achieve the optimal solution with a constant stepsize, which may lead to the disclosure of sensitive and private information. For privacy preservation, we propose a novel state-decomposition based gradient tracking approach (SD-Push-Pull) for distributed optimzation over directed networks that preserves differential privacy, which is a strong notion that protects agents' privacy against an adversary with arbitrary auxiliary information. The main idea of the proposed approach is to decompose the gradient state of each agent into two sub-states. Only one substate is exchanged by the agent with its neighbours over time, and the other one is kept private. That is to say, only one substate is visible to an adversary, protecting the privacy from being leaked. It is proved that under certain decomposition principles, a bound for the sub-optimality of the proposed algorithm can be derived and the differential privacy is achieved simultaneously. Moreover, the trade-off between differential privacy and the optimization accuracy is also characterized. Finally, a numerical simulation is provided to illustrate the effectiveness of the proposed approach

Self-absorption in the solar transition region

Transient brightenings in the transition region of the Sun have been studied for decades and are usually related to magnetic reconnection. Recently, absorption features due to chromospheric lines have been identified in transition region emission lines raising the question of the thermal stratification during such reconnection events. We analyse data from the Interface Region Imaging Spectrograph (IRIS) in an emerging active region. Here the spectral profiles show clear self-absorption features in the transition region lines of Si\,{\sc{iv}}. While some indications existed that opacity effects might play some role in strong transition region lines, self-absorption has not been observed before. We show why previous instruments could not observe such self-absorption features, and discuss some implications of this observation for the corresponding structure of reconnection events in the atmosphere. Based on this we speculate that a range of phenomena, such as explosive events, blinkers or Ellerman bombs, are just different aspects of the same reconnection event occurring at different heights in the atmosphere.Comment: Accepted for publication in Ap

CLEX: Continuous Length Extrapolation for Large Language Models

Transformer-based Large Language Models (LLMs) are pioneering advances in many natural language processing tasks, however, their exceptional capabilities are restricted within the preset context window of Transformer. Position Embedding (PE) scaling methods, while effective in extending the context window to a specific length, demonstrate either notable limitations in their extrapolation abilities or sacrificing partial performance within the context window. Length extrapolation methods, although theoretically capable of extending the context window beyond the training sequence length, often underperform in practical long-context applications. To address these challenges, we propose Continuous Length EXtrapolation (CLEX) for LLMs. We generalise the PE scaling approaches to model the continuous dynamics by ordinary differential equations over the length scaling factor, thereby overcoming the constraints of current PE scaling methods designed for specific lengths. Moreover, by extending the dynamics to desired context lengths beyond the training sequence length, CLEX facilitates the length extrapolation with impressive performance in practical tasks. We demonstrate that CLEX can be seamlessly incorporated into LLMs equipped with Rotary Position Embedding, such as LLaMA and GPT-NeoX, with negligible impact on training and inference latency. Experimental results reveal that CLEX can effectively extend the context window to over 4x or almost 8x training length, with no deterioration in performance. Furthermore, when evaluated on the practical LongBench benchmark, our model trained on a 4k length exhibits competitive performance against state-of-the-art open-source models trained on context lengths up to 32k