Reviewerly/exHarmony huggingface dataset:
Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training

Visual question answering (VQA) is a hallmark of vision and language reasoning and a challenging task under the zero-shot setting.We propose Plug-and-Play VQA (PNP-VQA), a modular framework for zero-shot VQA.In contrast to most existing works, which require substantial adaptation of pretrained language models (PLMs) for the vision modality, PNP-VQA requires no additional training of the PLMs.Instead, we propose to use natural language and network interpretation as an intermediate representation that glues pretrained models together.We first generate question-guided informative image captions, and pass the captions to a PLM as context for question answering.Surpassing end-to-end trained baselines, PNP-VQA achieves state-of-the-art results on zero-shot VQAv2 (Goyal et al., 2017) and GQA (Hudson and Manning, 2019).With 11B parameters, it outperforms the 80B-parameter Flamingo model (Alayrac et al., 2022) by 8.5% on VQAv2.With 738M PLM parameters, PNP-VQA achieves an improvement of 9.1% on GQA over FewVLM (Jin et al., 2022) with 740M PLM parameters.
A Digital Twin-Assisted Intelligent Partial Offloading Approach for Vehicular Edge Computing

Vehicle Edge Computing (VEC) is a promising paradigm that exposes Mobile Edge Computing (MEC) to road scenarios. In VEC, task offloading can enable vehicles to offload the computing tasks to nearby Roadside Units (RSUs) that deploy computing capabilities. However, the highly dynamic network topology, strict low-delay constraints, and massive data of tasks of VEC pose significant challenges for implementing efficient offloading. Digital Twin-based VEC is emerging as a promising solution that enables real-time monitoring of the state of the VEC network through mapping and interaction between the physical and virtual worlds, thus assisting in making sound offload decisions in the physical world. Thus, this paper proposes an intelligent partial offloading scheme, namely, Digital Twin-Assisted Intelligent Partial Offloading (IGNITE). First, to find the optimal offloading space in advance, we combine the improved clustering algorithm with the Digital Twin (DT) technique, in which unreasonable decisions can be avoided by reducing the size of the decision space. Second, to reduce the overall cost of the system, Deep Reinforcement Learning (DRL) algorithm is employed to train the offloading strategy, allowing for automatic optimization of computational delay and vehicle service price. To improve the efficiency of cooperation between digital and physical spaces, a feedback mechanism is established. It can adjust the parameters of the clustering algorithm based on the final offloading results in this clustering. To the best of our knowledge, this is the first study on DT-assisted vehicle offloading that proposes a feedback mechanism, forming a complete closed loop as prediction-offloading-feedback. Extensive experiments demonstrate that IGNITE has significant advantages in terms of total system computational cost, total computational delay, and offloading success rate compared with its counterparts.
BASALISC: Programmable Hardware Accelerator for BGV Fully Homomorphic Encryption

Fully Homomorphic Encryption (FHE) allows for secure computation on encrypted data. Unfortunately, huge memory size, computational cost and bandwidth requirements limit its practicality. We present BASALISC, an architecture family of hardware accelerators that aims to substantially accelerate FHE computations in the cloud. BASALISC is the first to implement the BGV scheme with fully-packed bootstrapping – the noise removal capability necessary for arbitrary-depth computation. It supports a customized version of bootstrapping that can be instantiated with hardware multipliers optimized for area and power.BASALISC is a three-abstraction-layer RISC architecture, designed for a 1 GHz ASIC implementation and underway toward 150mm2 die tape-out in a 12nm GF process. BASALISC’s four-layer memory hierarchy includes a two-dimensional conflict-free inner memory layer that enables 32 Tb/s radix-256 NTT computations without pipeline stalls. Its conflict-resolution permutation hardware is generalized and re-used to compute BGV automorphisms without throughput penalty. BASALISC also has a custom multiply-accumulate unit to accelerate BGV key switching.The BASALISC toolchain comprises a custom compiler and a joint performance and correctness simulator. To evaluate BASALISC, we study its physical realizability, emulate and formally verify its core functional units, and we study its performance on a set of benchmarks. Simulation results show a speedup of more than 5,000× over HElib – a popular software FHE library.
LibFewShot: A Comprehensive Library for Few-Shot Learning

Few-shot learning, especially few-shot image classification, has received increasing attention and witnessed significant advances in recent years. Some recent studies implicitly show that many generic techniques or “tricks”, such as data augmentation, pre-training, knowledge distillation, and self-supervision, may greatly boost the performance of a few-shot learning method. Moreover, different works may employ different software platforms, backbone architectures and input image sizes, making fair comparisons difficult and practitioners struggle with reproducibility. To address these situations, we propose a comprehensive library for few-shot learning (LibFewShot) by re-implementing eighteen state-of-the-art few-shot learning methods in a unified framework with the same single codebase in PyTorch. Furthermore, based on LibFewShot, we provide comprehensive evaluations on multiple benchmarks with various backbone architectures to evaluate common pitfalls and effects of different training tricks. In addition, with respect to the recent doubts on the necessity of meta- or episodic-training mechanism, our evaluation results confirm that such a mechanism is still necessary especially when combined with pre-training. We hope our work can not only lower the barriers for beginners to enter the area of few-shot learning but also elucidate the effects of nontrivial tricks to facilitate intrinsic research on few-shot learning.
Observation of a Superradiant Phase Transition with Emergent Cat States

Superradiant phase transitions (SPTs) are important for understanding light-matter interactions at the quantum level, and play a central role in criticality-enhanced quantum sensing. So far, SPTs have been observed in driven-dissipative systems, but the emergent light fields did not show any nonclassical characteristic due to the presence of strong dissipation. Here we report an experimental demonstration of the SPT featuring the emergence of a highly nonclassical photonic field, realized with a resonator coupled to a superconducting qubit, implementing the quantum Rabi model. We fully characterize the light-matter state by Wigner matrix tomography. The measured matrix elements exhibit quantum interference intrinsic of a photonic mesoscopic superposition, and reveal light-matter entanglement.
Principles of artificial intelligence and its application in cardiovascular medicine

Abstract Artificial intelligence (AI) represents a rapidly developing field. Its use can improve diagnosis and therapy in many areas of medicine. Despite this enormous progress, many physicians perceive it as a black box and are skeptical about it. This review will present the basics of machine learning. Different classifications of artificial intelligence, such as supervised versus unsupervised and discriminative versus generative AI, are given. Analogies to human intelligence are discussed as far as algorithms are oriented toward it. In the second step, the most common models like random forest, k‐means clustering, convolutional neural network, and transformers will be presented in a way that the underlying idea can be understood. Corresponding medical applications in cardiovascular medicine will be named for all models, respectively. The overview is intended to show that the term artificial intelligence covers a wide range of different concepts. It should help physicians understand the principles of AI to make up one's minds about its application in cardiology. It should also enable them to evaluate results obtained with AI's help critically.
Multi-modal medical Transformers: A meta-analysis for medical image segmentation in oncology

Multi-modal medical image segmentation is a crucial task in oncology that enables the precise localization and quantification of tumors. The aim of this work is to present a meta-analysis of the use of multi-modal medical Transformers for medical image segmentation in oncology, specifically focusing on multi-parametric MR brain tumor segmentation (BraTS2021), and head and neck tumor segmentation using PET-CT images (HECKTOR2021). The multi-modal medical Transformer architectures presented in this work exploit the idea of modality interaction schemes based on visio-linguistic representations: (i) single-stream, where modalities are jointly processed by one Transformer encoder, and (ii) multiple-stream, where the inputs are encoded separately before being jointly modeled. A total of fourteen multi-modal architectures are evaluated using different ranking strategies based on dice similarity coefficient (DSC) and average symmetric surface distance (ASSD) metrics. In addition, cost indicators such as the number of trainable parameters and the number of multiply-accumulate operations (MACs) are reported. The results demonstrate that multi-path hybrid CNN-Transformer-based models improve segmentation accuracy when compared to traditional methods, but come at the cost of increased computation time and potentially larger model size.
Quantum Flow Algorithms for Simulating Many-Body Systems on Quantum Computers

We conducted quantum simulations of strongly correlated systems using the quantum flow (QFlow) approach, which enables sampling large subspaces of the Hilbert space through coupled variational problems in reduced dimensionality active spaces. Our QFlow algorithms significantly reduce circuit complexity and pave the way for scalable and constant-circuit-depth quantum computing. Our simulations show that QFlow can optimize the collective number of wave function parameters without increasing the required qubits using active spaces having an order of magnitude fewer number of parameters.
SCBC: Smart city monitoring with blockchain using Internet of Things for and neuro fuzzy procedures

&lt;abstract&gt; &lt;p&gt;The security of the Internet of Things (IoT) is crucial in various application platforms, such as the smart city monitoring system, which encompasses comprehensive monitoring of various conditions. Therefore, this study conducts an analysis on the utilization of blockchain technology for the purpose of monitoring Internet of Things (IoT) systems. The analysis is carried out by employing parametric objective functions. In the context of the Internet of Things (IoT), it is imperative to establish well-defined intervals for job execution, ensuring that the completion status of each action is promptly monitored and assessed. The major significance of proposed method is to integrate a blockchain technique with neuro-fuzzy algorithm thereby improving the security of data processing units in all smart city applications. As the entire process is carried out with IoT the security of data in both processing and storage units are not secured therefore confidence level of monitoring units are maximized at each state. Due to the integration process the proposed system model is implemented with minimum energy conservation where 93% of tasks are completed with improved security for about 90%.&lt;/p&gt; &lt;/abstract&gt;
The Internal State of an LLM Knows When It’s Lying

While Large Language Models (LLMs) have shown exceptional performance in various tasks, one of their most prominent drawbacks is generating inaccurate or false information with a confident tone. In this paper, we provide evidence that the LLM’s internal state can be used to reveal the truthfulness of statements. This includes both statements provided to the LLM, and statements that the LLM itself generates. Our approach is to train a classifier that outputs the probability that a statement is truthful, based on the hidden layer activations of the LLM as it reads or generates the statement. Experiments demonstrate that given a set of test sentences, of which half are true and half false, our trained classifier achieves an average of 71% to 83% accuracy labeling which sentences are true versus false, depending on the LLM base model. Furthermore, we explore the relationship between our classifier’s performance and approaches based on the probability assigned to the sentence by the LLM. We show that while LLM-assigned sentence probability is related to sentence truthfulness, this probability is also dependent on sentence length and the frequencies of words in the sentence, resulting in our trained classifier providing a more reliable approach to detecting truthfulness, highlighting its potential to enhance the reliability of LLM-generated content and its practical applicability in real-world scenarios.
Zero-Shot Cross-Lingual Summarization via Large Language Models

Given a document in a source language, cross-lingual summarization (CLS) aims to generate a summary in a different target language. Recently, the emergence of Large Language Models (LLMs), such as GPT-3.5, ChatGPT and GPT-4, has attracted wide attention from the computational linguistics community. However, it is not yet known the performance of LLMs on CLS. In this report, we empirically use various prompts to guide LLMs to perform zero-shot CLS from different paradigms (i.e., end-to-end and pipeline), and provide a preliminary evaluation on the generated summaries. We find that ChatGPT and GPT-4 originally prefer to produce lengthy summaries with detailed information. These two LLMs can further balance informativeness and conciseness with the help of an interactive prompt, significantly improving their CLS performance. Experimental results on three widely-used CLS datasets show that GPT-4 achieves state-of-the-art zero-shot CLS performance, and performs competitively compared with the fine-tuned mBART-50. Moreover, we also find some multi-lingual and bilingual LLMs (i.e., BLOOMZ, ChatGLM-6B, Vicuna-13B and ChatYuan) have limited zero-shot CLS ability. Due to the composite nature of CLS, which requires models to perform summarization and translation simultaneously, accomplishing this task in a zero-shot manner is even a challenge for LLMs. Therefore, we sincerely hope and recommend future LLM research could use CLS as a testbed.
Query2doc: Query Expansion with Large Language Models

This paper introduces a simple yet effective query expansion approach, denoted as query2doc, to improve both sparse and dense retrieval systems. The proposed method first generates pseudo-documents by few-shot prompting large language models (LLMs), and then expands the query with generated pseudo documents. LLMs are trained on web-scale text corpora and are adept at knowledge memorization. The pseudo-documents from LLMs often contain highly relevant information that can aid in query disambiguation and guide the retrievers. Experimental results demonstrate that query2doc boosts the performance of BM25 by 3% to 15% on ad-hoc IR datasets, such as MS-MARCO and TREC DL, without any model fine-tuning. Furthermore, our method also benefits state-of-the-art dense retrievers in terms of both in-domain and out-of-domain results.
Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data

Chat models, such as ChatGPT, have shown impressive capabilities and have been rapidly adopted across numerous domains. However, these models are only accessible through a restricted API, creating barriers for new research and progress in the field. We propose a pipeline that can automatically generate a high-quality multi-turn chat corpus by leveraging ChatGPT to engage in a conversation with itself. Subsequently, we employ parameter-efficient tuning to enhance LLaMA, an open-source large language model. The resulting model, named Baize, demonstrates good performance in multi-turn dialogues with guardrails that minimize potential risks. Additionally, we propose a new technique called Self-Distill with Feedback, to further improve the performance of the Baize models with feedback from ChatGPT.
Digitized PDE Method for Data Restoration

Towards finding the connections between wavelets and the PDE method in image processing, we bring the two subjects closer in this chapter by developing the digital version of the PDE method. Continuous image domains are digitized to graphs and the Euler-Lagrange PDE equations become algebraic ones. Algorithms of these equations connect the digital PDE method to iterations of local digital filters (linear or nonlinear). We develop a self-contained theory of existence and uniqueness of the solutions, which avoids the technical difficulties appearing in classical continuous models and thus makes it easier for general readers without PDE backgrounds to understand and apply it. The PDE method has various applications in image analysis. In this chapter, we focus on denoising and restoration.
Learning and Playing in Wubble World

Children do not learn the meanings of words from parsing and understanding gigabytes of text; instead meanings are learned from competent speakers who relate language to what's happening in the child's environment. We present a word learning algorithm that operates in a video game environment where the players fill the role of the competent speakers and train softbots to learn language as a child would. We provide empirical evidence that the word learning algorithm successfully learns the meanings for some words in this environment and the children enjoy playing the game.
Performing with Technology: Lessons Learned from the GenJam Project

The author has been performing with GenJam, the Genetic Jammer, for nearly 20 years and has accumulated a wealth of experiences in performing live jazz with technology. This paper presents a discussion of the use of technology in jazz, both from the performer’s and from the audience’s perspective, and it proposes a classification scheme for live performance that is geared to mainstream performing situations.
Active Learning from Oracle with Knowledge Blind Spot

Active learning traditionally assumes that an oracle is capable of providing labeling information for each query instance. This paper formulates a new research problem which allows an oracle admit that he/she is incapable of labeling some query instances or simply answer "I don't know the label." We define a unified objectivefunction to ensure that each query instance submitted to the oracleis the one mostly needed for labeling and the oracle should also hasthe knowledge to label. Experiments based on different types of knowledge blind spot (KBS) models demonstrate the effectiveness of theproposed design.
Joint application design (JAD)

IBM coined the term joint application design (JAD) in 1970, but some experts prefer joint application development. The cost and time associated with data collection, analysis, and requirements definition can be significantly reduced by using the JAD technique. JAD is particularly suited to projects that face tight time and scheduling constraints and it is an excellent choice for developing a system from scratch. JAD is used to determine the system requirements during the problem definition phase of the system development life cycle. The members of a JAD team consist of end users from the relevant business functional areas, managers from those same functional areas, systems analysts or information consultants, and appropriate systems specialists. The JAD team is divided into several brainstorming sub-teams, and a friendly competition is launched to see which sub-team can list the most ideas. Following a brainstorming session, the JAD team divides into sub-groups to investigate the ideas on the various lists.
Armored Target Detection in Battlefield Environment Based on Top-Down Aggregation Network and Hierarchical Scale Optimization

Armored equipment plays a crucial role in the ground battlefield. The fast and accurate detection of enemy armored targets is significant to take the initiative in the battlefield. Comparing to general object detection and vehicle detection, armored target detection in battlefield environment is more challenging due to the long distance of observation and the complicated environment. In this paper, an accurate and robust automatic detection method is proposed to detect armored targets in battlefield environment. Firstly, inspired by Feature Pyramid Network (FPN), we propose a top-down aggregation (TDA) network which enhances shallow feature maps by aggregating semantic information from deeper layers. Then, using the proposed TDA network in a basic Faster R-CNN framework, we explore the further optimization of the approach for armored target detection: for the Region of Interest (RoI) Proposal Network (RPN), we propose a multi-branch RPNs framework to generate proposals that match the scale of armored targets and the specific receptive field of each aggregated layer and design hierarchical loss for the multi-branch RPNs; for RoI Classifier Network (RCN), we apply RoI pooling on the single finest scale feature map and construct a light and fast detection network. To evaluate our method, comparable experiments with state-of-art detection methods were conducted on a challenging dataset of images with armored targets. The experimental results demonstrate the effectiveness of the proposed method in terms of detection accuracy and recall rate.
High throughput fault‐resilient AES architecture

As more and more confidential information is being transmitted securely, the use of cryptographic algorithms is expanded. However, existing cryptographic algorithms are subject to various malicious attacks. Fault injection attack is one of the most effective attacks that are able to extract private information with the inexpensive requirement and short amount of time. AES is a block cipher that is used in many critical applications. Here, a lightweight error-detection architecture for AES has been proposed; the authors call it as high throughput fault-resilient AES (HFA). In the proposed architecture, the authors use parallel AES architecture, which contains four equivalent blocks and split each block into two pipeline stages. The authors have shown that HFA achieves high error-detection rate while keeping overheads reasonable.
Computational Complexity Analysis of Genetic Programming

Genetic programming (GP) is an evolutionary computation technique to solve problems in an automated, domain-independent way. Rather than identifying the optimum of a function as in more traditional evolutionary optimization, the aim of GP is to evolve computer programs with a given functionality. While many GP applications have produced human competitive results, the theoretical understanding of what problem characteristics and algorithm properties allow GP to be effective is comparatively limited. Compared with traditional evolutionary algorithms for function optimization, GP applications are further complicated by two additional factors: the variable-length representation of candidate programs, and the difficulty of evaluating their quality efficiently. Such difficulties considerably impact the runtime analysis of GP, where space complexity also comes into play. As a result, initial complexity analyses of GP have focused on restricted settings such as the evolution of trees with given structures or the estimation of solution quality using only a small polynomial number of input/output examples. However, the first computational complexity analyses of GP for evolving proper functions with defined input/output behavior have recently appeared. In this chapter, we present an overview of the state of the art.