Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanism of Upgrading Low-Grade Solar Thermal Energy and Experimental Validation

Solar thermochemical processes inherently included the conversion of solar thermal energy into chemical energy. In this paper a new mechanism of upgrading the energy level of solar thermal energy at around 200 degrees C was revealed based on the second law thermodynamics and was then experimentally proven. An expression was derived to describe the upgrading of the energy level from low-grade solar thermal energy to high-grade chemical energy. The resulting equation explicitly reveals the interrelations of energy levels between middle-temperature solar thermal energy and methanol fuel, and identifies the interactions of mean solar flux and the reactivity of methanol decomposition. The proposed mechanism was experimentally verified by using the fabricated 5 kW prototype of the receiver/reactor The agreement between the theoretical and the experimental results proves the validity of the mechanism for upgrading the energy level of low-grade solar thermal energy by integrating clean synthetic fuel. Moreover the application of this new middle-temperature solar/methanol hybrid thermochemical process into a combined cycle is expected to have a net solar-to-electric efficiency of about 27.8%, which is competitive with other solar-hybrid thermal power plants using high-temperature solar thermal energy. The results obtained here indicate the possibility of utilizing solar thermal energy at around 200 degrees C for electricity generation with high efficiency by upgrading the energy level of solar thermal energy, and provide an enhancement to solar thermal power plants with the development of this low-grade solar thermochemical technology in the near future.
Charge-sheet model for silicon carbide inversion layers

The charge-sheet model for metal-oxide-semiconductor (MOS) inversion layers is extended to silicon carbide. The generalized model is based on an analytical solution of the Poisson equation for the case of incomplete ionization of dopant impurities and incorporating Fermi-Dirac statistics. The results are compared with the conventional charge-sheet model which assumes complete impurity ionization and nondegenerate statistics. It is found that, at room temperature and for gate voltages in weak and moderate inversion, the present model predicts higher inversion-layer charge density at a given gate voltage. However, the relationship between the inversion charge and the surface Fermi potential is essentially independent of the degree of impurity ionization. In strong inversion or at temperatures above /spl sim/600 K, the differences between the two models are small. A formula is given for the threshold voltage as a function of the impurity ionization energy. The effects of several different interface state energy distributions on inversion charge are investigated. It is found that a slowly-varying interface-state density has an effect on threshold voltage of a MOSFET similar to that of a fixed oxide charge, while an interface-state density that increases at least exponentially with energy has the effect of lowering the field-effect mobility and transconductance.
Improve light load efficiency for synchronous rectifier buck converter

A DC/DC converter with high efficiency over very wide load range is necessary for many low voltage applications, such as battery supplied systems and microprocessor power supplies-voltage regulator module (VRM). In order to improve the efficiency of low-voltage power converters, synchronous rectifier technology is widely used. The disadvantage of this technology is low efficiency at light load. This paper proposes a new technology that can improve light load efficiency with very simple implementation. Since current sensors are not required, high density and high efficiency can be achieved that makes the whole circuit suitable for integration. In the paper, two application examples are given. Experimental results verified that this technology significantly improves synchronous rectifier buck converters light load efficiency.
LIO-CSI: LiDAR inertial odometry with loop closure combined with semantic information

Accurate and reliable state estimation and mapping are the foundation of most autonomous driving systems. In recent years, researchers have focused on pose estimation through geometric feature matching. However, most of the works in the literature assume a static scenario. Moreover, a registration based on a geometric feature is vulnerable to the interference of a dynamic object, resulting in a decline of accuracy. With the development of a deep semantic segmentation network, we can conveniently obtain the semantic information from the point cloud in addition to geometric information. Semantic features can be used as an accessory to geometric features that can improve the performance of odometry and loop closure detection. In a more realistic environment, semantic information can filter out dynamic objects in the data, such as pedestrians and vehicles, which lead to information redundancy in generated map and map-based localization failure. In this paper, we propose a method called LiDAR inertial odometry (LIO) with loop closure combined with semantic information (LIO-CSI), which integrates semantic information to facilitate the front-end process as well as loop closure detection. First, we made a local optimization on the semantic labels provided by the Sparse Point-Voxel Neural Architecture Search (SPVNAS) network. The optimized semantic information is combined into the front-end process of tightly-coupled light detection and ranging (LiDAR) inertial odometry via smoothing and mapping (LIO-SAM), which allows us to filter dynamic objects and improve the accuracy of the point cloud registration. Then, we proposed a semantic assisted scan-context method to improve the accuracy and robustness of loop closure detection. The experiments were conducted on an extensively used dataset KITTI and a self-collected dataset on the Jilin University (JLU) campus. The experimental results demonstrate that our method is better than the purely geometric method, especially in dynamic scenarios, and it has a good generalization ability.
Distributed MIMO Systems Based on Quantize-Map-and-Forward (QMF) Relaying

이동 단말기에 다중 안테나를 장착하는 것은 단말기의 크기와 전력 소모 문제로 현실적인 어려움을 안고 있다 . 본 논문에서는 셀룰러 네트워크에서 단일 안테나를 가지는 이동 단말기의 전송 용량 증대를 위해 , 다중안테나 통신 (MIMO)을 통해서 얻을 수 있는 선형적인 전송 용량 증대를 달성하는 신호 중계 기반 분산 다중 안테나기법을 제안하고 성능을 분석한다. 분산 다중안테나 시스템을 위한 단말 간 신호 중계에 증폭 후 전달 기법 (Amplify-and-Forward)을 사용할 경우에 비해 양자화 후 전달 (Quantize-Map-and-Forward) 기법을 사용하는 경우, 처리율 측면에서 보다 우수한 성능을 달성할 수 있음을 실험을 통해서 보인다 . 또한, 선형적 전송 용량 증대폭을 보다 향상시키기 위해 양자화 후 전달 중계 방식을 활용하여 다중접속채널을 구성하는 방법을 제안하고 , 이러한 다중접속채널 구성이 가능한 조건을 충족시키기 위한 단말기 선택을 가능하게 하는 후보 이동 단말기 수를 분석하였다. 이러한 분산 다중 안테나 통신 기법은 셀룰러 시스템에서 지역적으로 분리된 클러스터들을 서비스하기 때문에 주파수 재사용을 가능하게 하며 , 이로 인해 클러스터의 숫자에 비례하는 전체 셀룰러 용량 증대에 기여한다 . Key Words : Distributed Multiple-Input Multiple-Output (MIMO), quantize-map-and-forward (QMF),compress-and-forward (CF), spatial reuse, user relayingABSTRACT
High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learning

This paper proposes a Deep Reinforcement Learning algorithm for financial portfolio trading based on Deep Q-learning [1]. The algorithm is capable of trading high-dimensional portfolios from cross-sectional datasets of any size which may include data gaps and non-unique history lengths in the assets. We sequentially set up environments by sampling one asset for each environment while rewarding investments with the resulting asset’s return and cash reservation with the average return of the set of assets. This enforces the agent to strategically assign capital to assets that it predicts to perform above-average. We apply our methodology in an out-of-sample analysis to 48 US stock portfolio setups, varying in the number of stocks from ten up to 500 stocks, in the selection criteria and in the level of transaction costs. The algorithm on average outperforms all considered passive and active benchmark investment strategies by a large margin using only one hyperparameter setup for all portfolios.
Low-loss high-impedance circuit for quantum transduction between optical and microwave photons

Quantum transducers between microwave and optical photons are essential for long-distance quantum networks based on superconducting qubits. An optically active self-assembled quantum dot molecule (QDM) is an attractive platform for the implementation of a quantum transducer because an exciton in a QDM can be efficiently coupled to both optical and microwave fields at the single-photon level. Recently, the transduction between microwave and optical photons has been demonstrated with a QDM integrated with a superconducting resonator. In this paper, we present a design of a QD-high impedance resonator device with a low microwave loss and an expected large single-microwave photon coupling strength of 100s of MHz. We integrate self-assembled QDs onto a high-impedance superconducting resonator using a transfer printing technique and demonstrate a low-microwave loss rate of 1.8 MHz and gate tunability of the QDs. The corresponding microwave photon decay time of 88 ns is longer than the time necessary for the optical-microwave transduction process as well as the transmon-resonator swap operation time. This feature will facilitate efficient quantum transduction between an optical and microwave qubit.
Reconstructing partonic kinematics at colliders with machine learning

In the context of high-energy physics, a reliable description of the parton-level kinematics plays a crucial role for understanding the internal structure of hadrons and improving the precision of the calculations. In proton-proton collisions, this represents a challenging task since extracting such information from experimental data is not straightforward. With this in mind, we propose to tackle this problem by studying the production of one hadron and a direct photon in proton-proton collisions, including up to Next-to-Leading Order Quantum Chromodynamics and Leading-Order Quantum Electrodynamics corrections. Using Monte-Carlo integration, we simulate the collisions and analyze the events to determine the correlations among measurable and partonic quantities. Then, we use these results to feed three different Machine Learning algorithms that allow us to find the momentum fractions of the partons involved in the process, in terms of suitable combinations of the final state momenta. Our results are compatible with previous findings and suggest a powerful application of Machine-Learning to model high-energy collisions at the partonic-level with high-precision.
Revisiting small-scale fluctuations in α-attractor models of inflation

Cosmological α-attractors stand out as particularly compelling models to describe inflation in the very early universe, naturally meeting tight observational bounds from cosmic microwave background (CMB) experiments. We investigate α-attractor potentials in the presence of an inflection point, leading to enhanced curvature perturbations on small scales. We study both single- and multi-field models, driven by scalar fields living on a hyperbolic field space. In the single-field case, ultra-slow-roll dynamics at the inflection point is responsible for the growth of the power spectrum, while in the multi-field set-up we study the effect of geometrical destabilisation and non-geodesic motion in field space. The two mechanisms can in principle be distinguished through the spectral shape of the resulting scalar power spectrum on small scales. These enhanced scalar perturbations can lead to primordial black hole (PBH) production and second-order gravitational wave (GW) generation. Due to the existence of universal predictions in α-attractors, consistency with current CMB constraints on the large-scale spectral tilt implies that PBHs can only be produced with masses smaller than 108 g and are accompanied by ultra-high frequency GWs, with a peak expected to be at frequencies of order 10 kHz or above.
OPTT: Optimal Piecewise Transformation Technique for Analyzing Numerical Data under Local Differential Privacy

Privacy preserving data analysis (PPDA) has received increasing attention due to a great variety of applications. Local differential privacy (LDP), as an emerging standard that is suitable for PPDA, has been widely deployed into various real-world scenarios to analyze massive data while protecting against many forms of privacy breach. In this study, we are mainly concerned with piecewise transformation technique (PTT) for analyzing numerical data under local differential privacy. We provide a principled framework for PTT in the context of LDP, based on which PTT is studied systematically. As a result, we show that (1) many members in PTTs are asymptotically optimal when used to obtain an unbiased estimator for mean of numerical data, and (2) for a given privacy budget, there is PTT that reaches the theoretical low bound with respect to variance. Next, we prove by studying two classes of PTTs in detail that (1) there do not exist optimal PTTs compared to the well-used technique, i.e., Duchi's scheme, in terms of the consistency noisy variance, (2) on the other hand, one has the ability to find a great number of PTTs that are consistently more optimal than the latter with regard to the worst-case noisy variance, which is never reported so far. When we are restricted to consider only the high privacy level, enough PTTs turn out to be optimal than the well-known Laplace mechanism. Lastly, we prove that for a family of PTTs, the correspondingly theoretical low bound of noisy variance follows $O(\epsilon^{-2})$ when considering the high privacy level.
A data aware approach to salvage the endurance of Phase Change Memory

Phase Change Memory (PCM) is an emerging non-volatile memory technology that could either replace or augment DRAM and NAND flash that are hindered by scalability challenges. PCM suffers from a limited endurance problem that needs to be alleviated before it can be endorsed into the memory stack. This thesis is based on the observation that the endurance problem and its ramification depend on the write data. Accordingly, a data-aware approach is applied to salvage the endurance of PCM at three different stages: pre-write fault avoidance, post-write fault tolerance and post-failure recovery. The pre-write fault avoidance stage aims at reducing the endurance cost of servicing write requests. To this end, Cost Aware Flip Optimization (CAFO) is presented as an efficient technique to lessen the endurance degradation. Essentially, CAFO relies on a cost model that captures the endurance cost of programming memory cells based on their already stored values. Subsequently,the write data is encoded into a form that incurs a lower endurance cost than the original write data. Overall, CAFO is capable of reducing the endurance cost by up to 65% more than the existing schemes. Worn out PCM cells exhibit a stuck-at fault model which makes the manifestation of errors dependent on the values that cells are stuck at. When a write fails, the data is rewritten inverted. This dissertation shows that applying data inversion at the post-write fault tolerance stage exploits the data dependent nature of errors which enables ECCs to tolerate faults up to double their nominal capability. Furthermore, extensions to RDIS which is an ECC designed specifically for the stuck-at fault model are presented. At the post-failure recovery stage, Data Dependent Sparing is presented to manage bad blocks in PCM. Departing from the observation that defective blocks in the context of the stuck-at fault model still exhibit a low write failure probability due to the data dependent nature of errors, this thesis takes the approach of reusing blocks that are defective yet better-than-bad through a dynamic management of the reserve spare space. Data Dependent Sparing is capable of increasing the lifetime of PCM by up to 18%.
Empirical analyses of executable acceptance test driven development

This research investigates the process of Executable Acceptance Test-Driven Development (EATDD) in the context of specifying functional requirements using the FIT framework, when developing line-of-business applications. It is guided by three key research questions: (1) how business and technology experts utilize EATDD in the software development lifecycle; (2) what kind of benefits and limitations EATDD manifests, and (3) to what extent improvements in software quality (if any) are associated with EATDD? The research employs methods of quantitative and qualitative inquiries. Based on the findings of two academic observational studies, one academic quasiexperiment, and three industrial multi-case studies, the following main conclusions are drawn: (1) the use of Executable Acceptance Test-Driven Development is correlated with the enhanced communication in software teams; (2) executable acceptance tests are suitable for specifying functional requirements and are in fact unambiguous, consistent, verifiable, and usable (from both the business experts' and technology experts' perspectives); (3) EATDD provides sufficient evidence of requirements traceability in regulated environments; and (4) current state of tool support negatively impacts the maintainability and scalability of the artifacts produced in the course of EATDD. In addition, our contribution includes emerged conceptualizations and the sociotechnical model of the EATDD process. These not only help to explain the ways how EATDD is used in practice, but also form a base for future investigations.
PM and AM noise in common base amplifier

Theoretical analysis of PM and AM noise introduced by common base (CB) bipolar transistor amplifier and comparison of CB and common emitter (CE) amplifiers are presented. The analysis is based on the approach and the models developed and used in the papers (1997, 1998). Power spectral densities (PSD) of PM and AM noises of CB amplifier (both wideband and 1/f type) are found as functions of a harmonic signal frequency, available power of the signal source and parameters of biasing circuits. Comparison of PM and AM noises, introduced by CB amplifier and CE amplifier with unbypassed emitter resistor is also presented and discussed. PM noise of the amplifiers is discussed taking into consideration available power of signal source that is necessary to provide the same output voltage across the same load impedance.
Process Mining-Driven Analysis of the COVID19 Impact on the Vaccinations of Victorian Patients

Process mining is a discipline sitting between data mining and process science, whose goal is to provide theoretical methods and software tools to analyse process execution data, known as event logs. Although process mining was originally conceived to facilitate business process management activities, research studies have shown the benefit of leveraging process mining tools in different contexts, including healthcare. However, applying process mining tools to analyse healthcare process execution data is not straightforward. In this paper, we report the analysis of an event log recording more than 30 million events capturing the general practice healthcare processes of more than one million patients in Victoria--Australia--over five years. Our analysis allowed us to understand benefits and limitations of the state-of-the-art process mining techniques when dealing with highly variable processes and large data-sets. While we provide solutions to the identified limitations, the overarching goal of this study was to detect differences between the patients` health services utilization pattern observed in 2020--during the COVID-19 pandemic and mandatory lock-downs --and the one observed in the prior four years, 2016 to 2019. By using a combination of process mining techniques and traditional data mining, we were able to demonstrate that vaccinations in Victoria did not drop drastically--as other interactions did. On the contrary, we observed a surge of influenza and pneumococcus vaccinations in 2020, contradicting research findings of similar studies conducted in different geographical areas.
Dynamical characterisation of laser diode subject to double optical feedback for chaotic optical communications

A chaotic semiconductor laser subject to double optical feedback is characterised in terms of the statistical analysis, power spectrum analysis and forecast error analysis of the chaotic dynamics. Varying the relative lengths of the two external cavities, the dynamics are observed and compared to that of a single cavity. We show that in the case of integer multiple external-cavity lengths the round-trip time delays can be deduced from the forecast error analysis. In the case of fractional multiple cavity lengths, only the mean value of the cavity resonance frequencies can be deduced from the power spectra and hence the cavity time delays cannot be found. Cavity time delays that differ more than 0.4 ns can be obtained from the forecast error analysis. In the case of similar cavity lengths, the cavity time delays cannot be extracted. The results show that double optical feedback may offer particular advantages for enhancing security in chaos encryption.
A note on the integrability of exceptional potentials via polynomial bi-homogeneous potentials

This paper is concerned with the polynomial integrability of the two-dimensional Hamiltonian systems associated to complex homogeneous polynomial potentials of degree k of type Vk,l = α(q2 − iq1)(q2 + iq1) with α ∈ C and l = 0, 1, . , k, called exceptional potentials. Hietarinta [9] proved that the potentials with l = 0, 1, k − 1, k and l = k/2 for k even are polynomial integrable. We present an elementary proof of this fact in the context of the polynomial bi-homogeneous potentials, as was introduced by Combot et al. [8]. In addition, we take advantage of the fact that we can exchange the exponents to derive an additional first integral for V7,5, unknown so far. The paper concludes with a Galoisian analysis for l = k/2.
Effect of High-K Material on Gate Threshold Voltage for Double-Gate Tunnel FET

The effect of high-k material on gate threshold voltage for double gate tunnel field-effect transistor (DG-TFET) is studied in this paper. By physically derived the model of threshold voltage for DG-TFET, the quantitative relationship between threshold voltage and gate length is also discussed. It is shown that the proposed model is consistent with the simulation results, and can also easily predict the improved performance on the gate threshold voltage when using high-κ dielectrics and the limited effect on gate threshold voltage when changing the gate length.
Electron Correlation: Nature's Weird and Wonderful Chemical Glue

Prof. Dr. Jan M. L. Martin$ It can be argued that electron correlation, as a concept, deserves the same prominence in general chemistry as molecular orbital theory. We show how it acts as Nature's “chemical glue” at both the molecular and supramolecular levels. Electron correlation can be presented in a general chemistry course in an at least somewhat intuitive manner. We also propose a simple classi! cation of correlation e"ects based on their length scales and the size of the orbital gap (relative to the two-electron integrals). In the discussion, we also show how DFT can shed light on wavefunction theory, and conversely. We discuss two types of “honorary valence orbitals”, one related to small core-valence gaps, the other to the ability of empty 3d orbitals in 2 row elements to act as backbonding acceptors. Finally, we show why the pursuit of absolute total energies for their own sake becomes a sterile exercise, and why atomization energies are a more realistic “! x point”. nd Early View Online Version of Record before inclusion in an issue