A New Method to Estimate Risk and Return of Non-Traded Assets from Cash Flows: The Case of Private Equity Funds

We develop a new GMM-style methodology with good small-sample properties to assess the abnormal performance and risk exposure of a non-traded asset from a cross-section of cash flow data. We apply this method to a sample of 958 mature private equity funds spanning 24 years. Our methodology uses actual cash flow data and not intermediary self-reported Net Asset Values. In addition, it does not require a distributional assumption for returns. For venture capital funds, we find a high market beta and significant under-performance. For buyout funds, we find a low beta and no abnormal performance, but the sample is small. Larger funds have higher returns due to higher risk exposures and not higher alphas. We also find that Net Asset Values significantly overstate fund market values for the subset of mature and inactive funds.

A Gell-Mann & Low Theorem Perspective on Quantum Computing: New Paradigm for Designing Quantum Algorithm

The Gell-Mann & Low theorem is a cornerstone of Quantum Field Theory (QFT) and condensed matter physics, and many-body perturbation theory is a foundational tool for treating interactions. However, their integration into quantum algorithms remains a largely unexplored area of research, with current quantum simulation algorithms predominantly operating in the Schr\"odinger picture, leaving the potential of the interaction picture largely untapped. Our Variational Interaction-Picture S-matrix Ansatz (VIPSA) now fills this gap, specifically in the context of the Fermi-Hubbard model -- a canonical paradigm in condensed matter physics which is intricately connected to phenomena such as high-temperature superconductivity and Mott insulator transitions. This work offers a new conceptual perspective for variational quantum computing based upon the Gell-Mann & Low theorem. We achieve this by employing an innovative mathematical technique to explicitly unfold the normalized S-matrix, thereby enabling the systematic reconstruction of the Dyson series on a quantum computer, order by order. This method stands in contrast to the conventional reliance on Trotter expansion for adiabatic time evolution, marking a conceptual shift towards more sophisticated quantum algorithmic design. We leverage the strengths of the recently developed ADAPT-VQE algorithm, tailoring it to reconstruct perturbative terms effectively. Our simulations indicate that this method not only successfully recovers the Dyson series but also exhibits robust and stable convergence. We believe that our approach shows great promise in generalizing to more complex scenarios without increasing algorithmic complexity.Comment: 12 pages, 10 figure

CCDWT-GAN: Generative Adversarial Networks Based on Color Channel Using Discrete Wavelet Transform for Document Image Binarization

To efficiently extract the textual information from color degraded document images is an important research topic. Long-term imperfect preservation of ancient documents has led to various types of degradation such as page staining, paper yellowing, and ink bleeding; these degradations badly impact the image processing for information extraction. In this paper, we present CCDWT-GAN, a generative adversarial network (GAN) that utilizes the discrete wavelet transform (DWT) on RGB (red, green, blue) channel splited images. The proposed method comprises three stages: image preprocessing, image enhancement, and image binarization. This work conducts comparative experiments in the image preprocessing stage to determine the optimal selection of DWT with normalization. Additionally, we perform an ablation study on the results of the image enhancement stage and the image binarization stage to validate their positive effect on the model performance. This work compares the performance of the proposed method with other state-of-the-art (SOTA) methods on DIBCO and H-DIBCO ((Handwritten) Document Image Binarization Competition) datasets. The experimental results demonstrate that CCDWT-GAN achieves a top two performance on multiple benchmark datasets, and outperforms other SOTA methods

In vitro assay to estimate tea astringency via observing flotation of artificial oil bodies sheltered by caleosin fused with histatin 3

AbstractAstringency, a sensory characteristic of food and beverages rich in polyphenols, mainly results from the formation of complexes between polyphenols and salivary proteins, causing a reduction of the lubricating properties of saliva. To develop an in vitro assay to estimate the astringency of oolong tea infusion, artificial oil bodies were constituted with sesame oil sheltered by a modified caleosin fused with histatin 3, one of the human salivary small peptides. Aggregation of artificial oil bodies was induced when they were mixed with oolong tea infusion or its major polyphenolic compound, (−)-epigallocatechin gallate (EGCG) of 100μM as observed in light microscopy. The aggregated artificial oil bodies gradually floated on top of the solution and formed a visible milky layer whose thickness was in proportion to the concentrations of tea infusion. This assay system was applied to test four different oolong tea infusions with sensory astringency corresponding to their EGCG contents. The result showed that relative astringency of the four tea infusions was correlated to the thickness of floated artificial oil bodies, and could be estimated according to the standard curve generated by simultaneously observing a serial dilution of the tea infusion with the highest astringency

Towards General-Purpose Text-Instruction-Guided Voice Conversion

This paper introduces a novel voice conversion (VC) model, guided by text instructions such as "articulate slowly with a deep tone" or "speak in a cheerful boyish voice". Unlike traditional methods that rely on reference utterances to determine the attributes of the converted speech, our model adds versatility and specificity to voice conversion. The proposed VC model is a neural codec language model which processes a sequence of discrete codes, resulting in the code sequence of converted speech. It utilizes text instructions as style prompts to modify the prosody and emotional information of the given speech. In contrast to previous approaches, which often rely on employing separate encoders like prosody and content encoders to handle different aspects of the source speech, our model handles various information of speech in an end-to-end manner. Experiments have demonstrated the impressive capabilities of our model in comprehending instructions and delivering reasonable results.Comment: Accepted to ASRU 202

A Polymer-Based Capacitive Sensing Array for Normal and Shear Force Measurement

In this work, we present the development of a polymer-based capacitive sensing array. The proposed device is capable of measuring normal and shear forces, and can be easily realized by using micromachining techniques and flexible printed circuit board (FPCB) technologies. The sensing array consists of a polydimethlysiloxane (PDMS) structure and a FPCB. Each shear sensing element comprises four capacitive sensing cells arranged in a 2 × 2 array, and each capacitive sensing cell has two sensing electrodes and a common floating electrode. The sensing electrodes as well as the metal interconnect for signal scanning are implemented on the FPCB, while the floating electrodes are patterned on the PDMS structure. This design can effectively reduce the complexity of the capacitive structures, and thus makes the device highly manufacturable. The characteristics of the devices with different dimensions were measured and discussed. A scanning circuit was also designed and implemented. The measured maximum sensitivity is 1.67%/mN. The minimum resolvable force is 26 mN measured by the scanning circuit. The capacitance distributions induced by normal and shear forces were also successfully captured by the sensing array

Real-Time Telemetry System for Amperometric and Potentiometric Electrochemical Sensors

A real-time telemetry system, which consists of readout circuits, an analog-to-digital converter (ADC), a microcontroller unit (MCU), a graphical user interface (GUI), and a radio frequency (RF) transceiver, is proposed for amperometric and potentiometric electrochemical sensors. By integrating the proposed system with the electrochemical sensors, analyte detection can be conveniently performed. The data is displayed in real-time on a GUI and optionally uploaded to a database via the Internet, allowing it to be accessed remotely. An MCU was implemented using a field programmable gate array (FPGA) to filter noise, transmit data, and provide control over peripheral devices to reduce power consumption, which in sleep mode is 70 mW lower than in operating mode. The readout circuits, which were implemented in the TSMC 0.18-μm CMOS process, include a potentiostat and an instrumentation amplifier (IA). The measurement results show that the proposed potentiostat has a detectable current range of 1 nA to 100 μA, and linearity with an R2 value of 0.99998 in each measured current range. The proposed IA has a common-mode rejection ratio (CMRR) greater than 90 dB. The proposed system was integrated with a potentiometric pH sensor and an amperometric nitrite sensor for in vitro experiments. The proposed system has high linearity (an R2 value greater than 0.99 was obtained in each experiment), a small size of 5.6 cm × 8.7 cm, high portability, and high integration