pwc-archive/papers-with-abstracts huggingface dataset:
DANA: Domain-Aware Neurosymbolic Agents for Consistency and Accuracy
Authors: Vinh Luong, Sang Dinh, Shruti Raghavan, William Nguyen, Zooey Nguyen, Quynh Le, Hung Vo, Kentaro Maegaito, Loc Nguyen, Thao Nguyen, Anh Hai Ha, Christopher Nguyen
Date: 2024-09-27 00:00:00
Tasks: Financial Analysis

Large Language Models (LLMs) have shown remarkable capabilities, but their inherent probabilistic nature often leads to inconsistency and inaccuracy in complex problem-solving tasks. This paper introduces DANA (Domain-Aware Neurosymbolic Agent), an architecture that addresses these issues by integrating domain-specific knowledge with neurosymbolic approaches. We begin by analyzing current AI architectures, including AutoGPT, LangChain ReAct and OpenAI's ChatGPT, through a neurosymbolic lens, highlighting how their reliance on probabilistic inference contributes to inconsistent outputs. In response, DANA captures and applies domain expertise in both natural-language and symbolic forms, enabling more deterministic and reliable problem-solving behaviors. We implement a variant of DANA using Hierarchical Task Plans (HTPs) in the open-source OpenSSA framework. This implementation achieves over 90\% accuracy on the FinanceBench financial-analysis benchmark, significantly outperforming current LLM-based systems in both consistency and accuracy. Application of DANA in physical industries such as semiconductor shows that its flexible architecture for incorporating knowledge is effective in mitigating the probabilistic limitations of LLMs and has potential in tackling complex, real-world problems that require reliability and precision.
MedBench: A Comprehensive, Standardized, and Reliable Benchmarking System for Evaluating Chinese Medical Large Language Models
Authors: Mianxin Liu, Jinru Ding, Jie Xu, Weiguo Hu, Xiaoyang Li, Lifeng Zhu, Zhian Bai, Xiaoming Shi, Benyou Wang, Haitao Song, PengFei Liu, Xiaofan Zhang, Shanshan Wang, Kang Li, Haofen Wang, Tong Ruan, Xuanjing Huang, Xin Sun, Shaoting Zhang
Date: 2024-06-24 00:00:00
Tasks: Benchmarking

Ensuring the general efficacy and goodness for human beings from medical large language models (LLM) before real-world deployment is crucial. However, a widely accepted and accessible evaluation process for medical LLM, especially in the Chinese context, remains to be established. In this work, we introduce "MedBench", a comprehensive, standardized, and reliable benchmarking system for Chinese medical LLM. First, MedBench assembles the currently largest evaluation dataset (300,901 questions) to cover 43 clinical specialties and performs multi-facet evaluation on medical LLM. Second, MedBench provides a standardized and fully automatic cloud-based evaluation infrastructure, with physical separations for question and ground truth. Third, MedBench implements dynamic evaluation mechanisms to prevent shortcut learning and answer remembering. Applying MedBench to popular general and medical LLMs, we observe unbiased, reproducible evaluation results largely aligning with medical professionals' perspectives. This study establishes a significant foundation for preparing the practical applications of Chinese medical LLMs. MedBench is publicly accessible at https://medbench.opencompass.org.cn.
Privacy-Preserving SAM Quantization for Efficient Edge Intelligence in Healthcare
Authors: Zhikai Li, Jing Zhang, Qingyi Gu
Date: 2024-09-14 00:00:00
Tasks: Data Free Quantization, Image Segmentation, Model Compression, Privacy Preserving, Quantization, Semantic Segmentation

The disparity in healthcare personnel expertise and medical resources across different regions of the world is a pressing social issue. Artificial intelligence technology offers new opportunities to alleviate this issue. Segment Anything Model (SAM), which excels in intelligent image segmentation, has demonstrated exceptional performance in medical monitoring and assisted diagnosis. Unfortunately, the huge computational and storage overhead of SAM poses significant challenges for deployment on resource-limited edge devices. Quantization is an effective solution for model compression; however, traditional methods rely heavily on original data for calibration, which raises widespread concerns about medical data privacy and security. In this paper, we propose a data-free quantization framework for SAM, called DFQ-SAM, which learns and calibrates quantization parameters without any original data, thus effectively preserving data privacy during model compression. Specifically, we propose pseudo-positive label evolution for segmentation, combined with patch similarity, to fully leverage the semantic and distribution priors in pre-trained models, which facilitates high-quality data synthesis as a substitute for real data. Furthermore, we introduce scale reparameterization to ensure the accuracy of low-bit quantization. We perform extensive segmentation experiments on various datasets, and DFQ-SAM consistently provides significant performance on low-bit quantization. DFQ-SAM eliminates the need for data transfer in cloud-edge collaboration, thereby protecting sensitive data from potential attacks. It enables secure, fast, and personalized healthcare services at the edge, which enhances system efficiency and optimizes resource allocation, and thus facilitating the pervasive application of artificial intelligence in worldwide healthcare.
Adversarial Attacks and Robust Defenses in Speaker Embedding based Zero-Shot Text-to-Speech System
Authors: Ze Li, Yao Shi, Yunfei Xu, Ming Li
Date: 2024-10-05 00:00:00
Tasks: Adversarial Purification, Speech Synthesis, text-to-speech, Text to Speech

Speaker embedding based zero-shot Text-to-Speech (TTS) systems enable high-quality speech synthesis for unseen speakers using minimal data. However, these systems are vulnerable to adversarial attacks, where an attacker introduces imperceptible perturbations to the original speaker's audio waveform, leading to synthesized speech sounds like another person. This vulnerability poses significant security risks, including speaker identity spoofing and unauthorized voice manipulation. This paper investigates two primary defense strategies to address these threats: adversarial training and adversarial purification. Adversarial training enhances the model's robustness by integrating adversarial examples during the training process, thereby improving resistance to such attacks. Adversarial purification, on the other hand, employs diffusion probabilistic models to revert adversarially perturbed audio to its clean form. Experimental results demonstrate that these defense mechanisms can significantly reduce the impact of adversarial perturbations, enhancing the security and reliability of speaker embedding based zero-shot TTS systems in adversarial environments.
Variable Resolution Pixel Quantization for Low Power Machine Vision Application on Edge
Authors: Senorita Deb, Sai Sanjeet, Prabir Kumar Biswas, Bibhu Datta Sahoo
Date: 2024-10-07 00:00:00
Tasks: Edge-computing, image-classification, Image Classification, Quantization

This work describes an approach towards pixel quantization using variable resolution which is made feasible using image transformation in the analog domain. The main aim is to reduce the average bits-per-pixel (BPP) necessary for representing an image while maintaining the classification accuracy of a Convolutional Neural Network (CNN) that is trained for image classification. The proposed algorithm is based on the Hadamard transform that leads to a low-resolution variable quantization by the analog-to-digital converter (ADC) thus reducing the power dissipation in hardware at the sensor node. Despite the trade-offs inherent in image transformation, the proposed algorithm achieves competitive accuracy levels across various image sizes and ADC configurations, highlighting the importance of considering both accuracy and power consumption in edge computing applications. The schematic of a novel 1.5 bit ADC that incorporates the Hadamard transform is also proposed. A hardware implementation of the analog transformation followed by software-based variable quantization is done for the CIFAR-10 test dataset. The digitized data shows that the network can still identify transformed images with a remarkable 90% accuracy for 3-BPP transformed images following the proposed method.
Bi-Directional MS Lesion Filling and Synthesis Using Denoising Diffusion Implicit Model-based Lesion Repainting
Authors: Jinwei Zhang, Lianrui Zuo, Yihao Liu, Samuel Remedios, Bennett A. Landman, Jerry L. Prince, Aaron Carass
Date: 2024-10-07 00:00:00
Tasks: Denoising, Image Inpainting, Lesion Segmentation, Segmentation

Automatic magnetic resonance (MR) image processing pipelines are widely used to study people with multiple sclerosis (PwMS), encompassing tasks such as lesion segmentation and brain parcellation. However, the presence of lesion often complicates these analysis, particularly in brain parcellation. Lesion filling is commonly used to mitigate this issue, but existing lesion filling algorithms often fall short in accurately reconstructing realistic lesion-free images, which are vital for consistent downstream analysis. Additionally, the performance of lesion segmentation algorithms is often limited by insufficient data with lesion delineation as training labels. In this paper, we propose a novel approach leveraging Denoising Diffusion Implicit Models (DDIMs) for both MS lesion filling and synthesis based on image inpainting. Our modified DDIM architecture, once trained, enables both MS lesion filing and synthesis. Specifically, it can generate lesion-free T1-weighted or FLAIR images from those containing lesions; Or it can add lesions to T1-weighted or FLAIR images of healthy subjects. The former is essential for downstream analyses that require lesion-free images, while the latter is valuable for augmenting training datasets for lesion segmentation tasks. We validate our approach through initial experiments in this paper and demonstrate promising results in both lesion filling and synthesis, paving the way for future work.
Editing Music with Melody and Text: Using ControlNet for Diffusion Transformer
Authors: Siyuan Hou, Shansong Liu, Ruibin Yuan, Wei Xue, Ying Shan, Mangsuo Zhao, Chao Zhang
Date: 2024-10-07 00:00:00
Tasks: Music Generation, Music Style Transfer, Style Transfer, Text-to-Music Generation

Despite the significant progress in controllable music generation and editing, challenges remain in the quality and length of generated music due to the use of Mel-spectrogram representations and UNet-based model structures. To address these limitations, we propose a novel approach using a Diffusion Transformer (DiT) augmented with an additional control branch using ControlNet. This allows for long-form and variable-length music generation and editing controlled by text and melody prompts. For more precise and fine-grained melody control, we introduce a novel top-$k$ constant-Q Transform representation as the melody prompt, reducing ambiguity compared to previous representations (e.g., chroma), particularly for music with multiple tracks or a wide range of pitch values. To effectively balance the control signals from text and melody prompts, we adopt a curriculum learning strategy that progressively masks the melody prompt, resulting in a more stable training process. Experiments have been performed on text-to-music generation and music-style transfer tasks using open-source instrumental recording data. The results demonstrate that by extending StableAudio, a pre-trained text-controlled DiT model, our approach enables superior melody-controlled editing while retaining good text-to-music generation performance. These results outperform a strong MusicGen baseline in terms of both text-based generation and melody preservation for editing. Audio examples can be found at https://stable-audio-control.github.io.
Distribution Grids May Be a Barrier To Residential Electrification
Authors: Priyadarshan, Constance Crozier, Kyri Baker, Kevin Kircher
Date: 2024-10-06 00:00:00
Tasks: Management

Replacing fossil-fueled appliances and vehicles with electric alternatives can reduce greenhouse gas emissions and air pollution in many settings. However, residential electrification can also raise electricity demand beyond the safe limits of electrical infrastructure. This can increase the risk of blackouts or require grid reinforcement that is often slow and expensive. Here, we estimate the physical and economic impacts on distribution grids of electrifying all housing and personal vehicles in each county of the lower 48 United States. We find that space heating is the main driver of grid impacts, with the coldest regions seeing demand peaks up to five times higher than today's peaks. Accommodating electrification of all housing and personal vehicles is estimated to require 600 GW of distribution grid reinforcement nationally, at a cost of \$350 to \$790 billion, or \$2,800 to \$6,400 per household (95% confidence intervals). However, demand-side management could eliminate three-quarters of grid reinforcement costs.
Deviance Voronoi Residuals for Space-Time Point Process Models: An Application to Earthquake Insurance Risk
Authors: Roba Bairakdar, Debbie Dupuis, Melina Mailhot
Date: 2024-10-06 00:00:00

Insurance risk arising from catastrophes such as earthquakes a component of the Minimum Capital Test for federally regulated property and casualty insurance companies. Analyzing earthquake insurance risk requires well-fitted spatio-temporal point process models. Given the spatial heterogeneity of earthquakes, the ability to assess whether the fits are adequate in certain locations is crucial in obtaining usable models. Accordingly, we extend the use of Voronoi residuals to calculate deviance Voronoi residuals. We also create a simulation-based approach, in which losses and insurance claim payments are calculated by relying on earthquake hazard maps of Canada. As an alternative to the current guidelines of OSFI, a formula to calculate the country-wide minimum capital test is proposed based on the correlation between the provinces. Finally, an interactive web application is provided which allows the user to simulate earthquake damage and the resulting financial losses and insurance claims, at a chosen epicenter location.
A Reinforcement Learning Engine with Reduced Action and State Space for Scalable Cyber-Physical Optimal Response
Authors: Shining Sun, Khandaker Akramul Haque, Xiang Huo, Leen Al Homoud, Shamina Hossain-McKenzie, Ana Goulart, Katherine Davis
Date: 2024-10-06 00:00:00
Tasks: Reinforcement Learning (RL)

Numerous research studies have been conducted to enhance the resilience of cyber-physical systems (CPSs) by detecting potential cyber or physical disturbances. However, the development of scalable and optimal response measures under power system contingency based on fusing cyber-physical data is still in an early stage. To address this research gap, this paper introduces a power system response engine based on reinforcement learning (RL) and role and interaction discovery (RID) techniques. RL-RID-GridResponder is designed to automatically detect the contingency and assist with the decision-making process to ensure optimal power system operation. The RL-RID-GridResponder learns via an RL-based structure and achieves enhanced scalability by integrating an RID module with reduced action and state spaces. The applicability of RL-RID-GridResponder in providing scalable and optimal responses for CPSs is demonstrated on power systems in the context of Denial of Service (DoS) attacks. Moreover, simulations are conducted on a Volt-Var regulation problem using the augmented WSCC 9-bus and augmented IEEE 24-bus systems based on fused cyber and physical data sets. The results show that the proposed RL-RID-GridResponder can provide fast and accurate responses to ensure optimal power system operation under DoS and can extend to other system contingencies such as line outages and loss of loads.
Bias-VarianceTrade-off in Kalman Filter-Based Disturbance Observers
Authors: Shilei Li, Dawei Shi, Xiaoxu Lyu, Jiawei Tang, Ling Shi
Date: 2024-10-07 00:00:00
Tasks: State Estimation

The performance of disturbance observers is strongly influenced by the level of prior knowledge about the disturbance model. The simultaneous input and state estimation (SISE) algorithm is widely recognized for providing unbiased minimum-variance estimates under arbitrary disturbance models. In contrast, the Kalman filter-based disturbance observer (KF-DOB) achieves minimum mean-square error estimation when the disturbance model is fully specified. However, practical scenarios often fall between these extremes, where only partial knowledge of the disturbance model is available. This paper investigates the inherent bias-variance trade-off in KF-DOB when the disturbance model is incomplete. We further show that SISE can be interpreted as a special case of KF-DOB, where the disturbance noise covariance tends to infinity. To address this trade-off, we propose two novel estimators: the multi-kernel correntropy Kalman filter-based disturbance observer (MKCKF-DOB) and the interacting multiple models Kalman filter-based disturbance observer (IMMKF-DOB). Simulations verify the effectiveness of the proposed methods.
Tom Efrati
Authors: Tom Efrati
Date: 2024-10-09 00:00:00
Proceeding: Tom Efrati 2024 10
Tasks: Marketing, Navigate

A Finnish-Israeli entrepreneur with a passion for innovation and a keen eye for emerging trends, I am also a thoughtful commentator on current events. My diverse background allows me to blend insights from both cultures, providing a unique perspective on global issues. Through my work, I aim to not only drive business success but also foster meaningful discussions about the events shaping our world today.

Tom Efrati is a Finnish-Israeli entrepreneur and a well-known commentator on current events. He was born in Israel in 1998 and spent his early childhood there before moving to Finland with his family in 2007. This transition not only introduced him to a new culture and language but also laid the foundation for his diverse worldview.

Growing up in Finland, Tom embraced the local culture while maintaining a strong connection to his Israeli roots. This unique blend of influences has shaped his identity and informed his approach to business and public discourse. He completed his education in Finland, where he developed a keen interest in technology and entrepreneurship.

In 2018, at the age of 20, Tom took a significant step in his career by founding Maatio Oy, a digital marketing company based in Helsinki. His vision for Maatio Oy was to help businesses navigate the evolving digital landscape. Under his leadership as CEO, the company quickly gained a reputation for its innovative strategies and effective marketing solutions. Tom's focus on client relationships and understanding market trends has allowed Maatio Oy to grow and adapt in a competitive environment.

Beyond his work in digital marketing, Tom is passionate about discussing current events and societal issues. He is a regular public speaker, engaging with audiences on topics such as technology, entrepreneurship, and cultural exchange. His ability to communicate complex ideas in a clear and relatable way has made him a sought-after commentator. Tom often shares his insights through various platforms, contributing to discussions that bridge gaps between different cultures and perspectives.

As an advocate for collaboration and understanding, Tom believes in the importance of dialogue in today’s globalized world. He aims to promote cross-cultural connections, drawing from his own experiences as a Finnish-Israeli. His work and commentary reflect a commitment to fostering a more inclusive society.

Today, Tom Efrati continues to lead Maatio Oy while also expanding his role as a public figure. He remains dedicated to helping businesses succeed in the digital age and contributing positively to discussions about our world. Through his entrepreneurial journey and public speaking, he inspires others to embrace their unique backgrounds and use them to create meaningful change.
Experimental Investigation of the Oxidative Coupling of Methane in a Porous Membrane Reactor: Relevance of Back-Permeation
Authors: Aitor Cruellas, Wout Ververs, Martin van Sint Annaland and Fausto Gallucci
Date: 2020-07-14 00:00:00
Proceeding: membrane 2020 7

Novel reactor configurations for the oxidative coupling of methane (OCM), and in particular
membrane reactors, contribute toward reaching the yield required to make the process competitive
at the industrial scale. Therefore, in this work, the conventional OCM packed bed reactor using
a Mn-Na2WO4/SiO2 catalyst was experimentally compared with a membrane reactor, in which a
symmetric MgO porous membrane was integrated. The beneficial eects of distributive feeding
of oxygen along the membrane, which is the main advantage of the porous membrane reactor,
were demonstrated, although no significant dierences in terms of performance were observed
because of the adverse eects of back-permeation prevailing in the experiments. A sensitivity analysis
carried out on the eective diusion coecient also indicated the necessity of properly tuning the
membrane properties to achieve the expected promising results, highlighting how this tuning could
be addressed.
SMART: A Flexible Approach to Regression using Spline-Based Multivariate Adaptive Regression Trees
Authors: William Pattie, Arvind Krishna
Date: 2024-10-08 00:00:00
Tasks: regression

Decision trees are powerful for predictive modeling but often suffer from high variance when modeling continuous relationships. While algorithms like Multivariate Adaptive Regression Splines (MARS) excel at capturing such continuous relationships, they perform poorly when modeling discontinuities. To address the limitations of both approaches, we introduce Spline-based Multivariate Adaptive Regression Trees (SMART), which uses a decision tree to identify subsets of data with distinct continuous relationships and then leverages MARS to fit these relationships independently. Unlike other methods that rely on the tree structure to model interaction and higher-order terms, SMART leverages MARS's native ability to handle these terms, allowing the tree to focus solely on identifying discontinuities in the relationship. We test SMART on various datasets, demonstrating its improvement over state-of-the-art methods in such cases. Additionally, we provide an open-source implementation of our method to be used by practitioners.
Attainable Force Approximation and Full-Pose Tracking Control of an Over-Actuated Thrust-Vectoring Modular Team UAV
Authors: Yen-Cheng Chu, Kai-Cheng Fang, Feng-Li Lian
Date: 2024-10-04 00:00:00
Tasks: Pose Tracking

Traditional vertical take-off and landing (VTOL) aircraft can not achieve optimal efficiency for various payload weights and has limited mobility due to its under-actuation. With the thrust-vectoring mechanism, the proposed modular team UAV is fully actuated at certain attitudes. However, the attainable force space (AFS) differs according to the team configuration, which makes the controller design difficult. We propose an approximation to the AFS and a full-pose tracking controller with an attitude planner and a force projection, which guarantees the control force is feasible. The proposed approach can be applied to UAVs having multiple thrust-vectoring effectors with homogeneous agents. The simulation and experiment demonstrate a tilting motion during hovering for a 4-agent team.