Smartphone acoustic impedance sensing based on additive sum mixing technique

We have developed a method to perform impedance measurement using general purpose smartphones that will not require the presence of external power source. The need for battery-less impedance sensing methods is greatly demanded given recent years’ advancements in impedance based sensing technologies, such as impedance tomography, which will enable patients to perform medical tests such as breast cancer self-detection using only a smartphone. The work discussed in this thesis is an early prototype of the impedance sensing method done using MATLAB simulation of both hardware and algorithm for impedance sensing. The battery-less acoustic impedance sensing technique is a combination of both hardware circuit design as well as control software algorithm. The circuit hardware technology is based on the additive summing mixer technology that is widely used in professional audio production mixing consoles. The results presented in this thesis can be accurate to within 0.1% of target device characteristics in simulation as far as tested. Although not discussed in this work, in our early physical hardware prototypes, the measurement based on the method discussed in this thesis has achieved accuracy within 10% of the target value with all the noise and parameter approximations

The impacts of non-fossil energy, economic growth, energy consumption, and oil price on carbon intensity: evidence from a panel quantile regression analysis of EU 28

This study investigates some determinants of carbon intensity in 28 countries in the European Union (EU), including non-fossil energy, economic growth, energy consumption, and oil price. A panel quantile regression method, which considers both individual heterogeneity and distributional heterogeneity, is applied in this paper. The empirical results imply that the influences of these determinants on carbon intensity are heterogeneous and asymmetric across different quantiles. Specifically, non-fossil energy can significantly decrease carbon intensity, but shows a U-shaped relationship. Economic growth has a negative impact on carbon intensity, especially for medium-emission and high-emission countries. The effects of heating degree days on carbon intensity are positive, although the coefficients are not significant at low quantiles, they become significant from medium quantiles. Besides, we find an inverse U-shaped relationship between crude oil price and carbon intensity. Finally, several relevant policy recommendations are proposed based on the empirical results

Can digital economic attention spillover to financial markets? Evidence from the time-varying Granger test

The digital economy is pervasive, all-encompassing, and a pan-industrial revolution. This paper pioneers constructing a digital economy concern index by extracting the web search volumes of keywords through crawler technology and analyzes the dynamic causal relationship with the Chinese stock markets via time-varying Granger tests. The results reveal that digital economy attention has a significant predictive effect on stock prices in a time-varying pattern and that the causal spillover varies across industry segments, with higher success rates and longer duration of causal detection under recursive algorithms. Moreover, the causal impact of digital economy attention on stock prices is generally limited in sluggish market states, mainly reflected during the COVID-19 pandemic and again after the epidemic had passed for some time with significant causality. This paper provides new evidence and analytical perspectives on the performance of the digital economy in financial markets, informing the digital transformation of various industries and investment decisions of investors

PRS-Net: planar reflective symmetry detection net for 3D models

In geometry processing, symmetry is a universal type of high-level structural information of 3D models and benefits many geometry processing tasks including shape segmentation, alignment, matching, and completion. Thus it is an important problem to analyze various symmetry forms of 3D shapes. Planar reflective symmetry is the most fundamental one. Traditional methods based on spatial sampling can be time-consuming and may not be able to identify all the symmetry planes. In this paper, we present a novel learning framework to automatically discover global planar reflective symmetry of a 3D shape. Our framework trains an unsupervised 3D convolutional neural network to extract global model features and then outputs possible global symmetry parameters, where input shapes are represented using voxels. We introduce a dedicated symmetry distance loss along with a regularization loss to avoid generating duplicated symmetry planes. Our network can also identify generalized cylinders by predicting their rotation axes. We further provide a method to remove invalid and duplicated planes and axes. We demonstrate that our method is able to produce reliable and accurate results. Our neural network based method is hundreds of times faster than the state-of-the-art methods, which are based on sampling. Our method is also robust even with noisy or incomplete input surfaces

DiQAD: A Benchmark Dataset for End-to-End Open-domain Dialogue Assessment

Dialogue assessment plays a critical role in the development of open-domain dialogue systems. Existing work are uncapable of providing an end-to-end and human-epistemic assessment dataset, while they only provide sub-metrics like coherence or the dialogues are conversed between annotators far from real user settings. In this paper, we release a large-scale dialogue quality assessment dataset (DiQAD), for automatically assessing open-domain dialogue quality. Specifically, we (1) establish the assessment criteria based on the dimensions conforming to human judgements on dialogue qualities, and (2) annotate large-scale dialogues that conversed between real users based on these annotation criteria, which contains around 100,000 dialogues. We conduct several experiments and report the performances of the baselines as the benchmark on DiQAD. The dataset is openly accessible at https://github.com/yukunZhao/Dataset_Dialogue_quality_evaluation.Comment: Accepted to Findings of EMNLP 202

Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method

Large Language Models (LLMs) have shown great potential in Natural Language Processing (NLP) tasks. However, recent literature reveals that LLMs generate nonfactual responses intermittently, which impedes the LLMs' reliability for further utilization. In this paper, we propose a novel self-detection method to detect which questions that a LLM does not know that are prone to generate nonfactual results. Specifically, we first diversify the textual expressions for a given question and collect the corresponding answers. Then we examine the divergencies between the generated answers to identify the questions that the model may generate falsehoods. All of the above steps can be accomplished by prompting the LLMs themselves without referring to any other external resources. We conduct comprehensive experiments and demonstrate the effectiveness of our method on recently released LLMs, e.g., Vicuna, ChatGPT, and GPT-4

On dynamic linkages of the state natural gas markets in the USA: evidence from an empirical spatio-temporal network quantile analysis

We empirically investigate the dynamic linkages of the state-level natural gas markets in the USA. By introducing a novel spatio-temporal network quantile econometric model, we can estimate the dynamic cross-state dependency or market integration of the state-level natural gas markets and the dependence of the state natural gas markets on the national crude oil market at different quantile levels. We find that significant local dynamic neighbouring market integrations exist in the natural gas markets not only in the eastern and central states as evidenced in the literature but also in some western and southwest states. Our results also show that there are significant linkages of the state-level natural gas markets to the national crude oil market through the lagged price shocks and the long-run price equilibrium with the national gas markets under varying price shock propagations. The results can help local government and energy users to mitigate the negative impacts from the expected or unexpected fluctuations in the oil and the neighbouring natural gas markets, which will enact appropriate state-level price discovery and energy policy and investment decision makings

Dynamic Budget Throttling in Repeated Second-Price Auctions

Throttling is one of the most popular budget control methods in today's online advertising markets. When a budget-constrained advertiser employs throttling, she can choose whether or not to participate in an auction after the advertising platform recommends a bid. This paper focuses on the dynamic budget throttling process in repeated second-price auctions from a theoretical view. An essential feature of the underlying problem is that the advertiser does not know the distribution of the highest competing bid upon entering the market. To model the difficulty of eliminating such uncertainty, we consider two different information structures. The advertiser could obtain the highest competing bid in each round with full-information feedback. Meanwhile, with partial information feedback, the advertiser could only have access to the highest competing bid in the auctions she participates in. We propose the OGD-CB algorithm, which involves simultaneous distribution learning and revenue optimization. In both settings, we demonstrate that this algorithm guarantees an $O(\sqrt{T\log T})$ regret with probability $1 - O(1/T)$ relative to the fluid adaptive throttling benchmark. By proving a lower bound of $\Omega(\sqrt{T})$ on the minimal regret for even the hindsight optimum, we establish the near optimality of our algorithm. Finally, we compare the fluid optimum of throttling to that of pacing, another widely adopted budget control method. The numerical relationship of these benchmarks sheds new light on the understanding of different online algorithms for revenue maximization under budget constraints.Comment: 29 pages, 1 tabl