Serum cystatin C and chemerin levels in diabetic retinopathy

Peer reviewedPublisher PD

Measuring the Angular Velocity of a Propeller with Video Camera Using Electronic Rolling Shutter

Noncontact measurement for rotational motion has advantages over the traditional method which measures rotational motion by means of installing some devices on the object, such as a rotary encoder. Cameras can be employed as remote monitoring or inspecting sensors to measure the angular velocity of a propeller because of their commonplace availability, simplicity, and potentially low cost. A defect of the measurement with cameras is to process the massive data generated by cameras. In order to reduce the collected data from the camera, a camera using ERS (electronic rolling shutter) is applied to measure angular velocities which are higher than the speed of the camera. The effect of rolling shutter can induce geometric distortion in the image, when the propeller rotates during capturing an image. In order to reveal the relationship between the angular velocity and the image distortion, a rotation model has been established. The proposed method was applied to measure the angular velocities of the two-blade propeller and the multiblade propeller. The experimental results showed that this method could detect the angular velocities which were higher than the camera speed, and the accuracy was acceptable

UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition

Multimodal sentiment analysis (MSA) and emotion recognition in conversation (ERC) are key research topics for computers to understand human behaviors. From a psychological perspective, emotions are the expression of affect or feelings during a short period, while sentiments are formed and held for a longer period. However, most existing works study sentiment and emotion separately and do not fully exploit the complementary knowledge behind the two. In this paper, we propose a multimodal sentiment knowledge-sharing framework (UniMSE) that unifies MSA and ERC tasks from features, labels, and models. We perform modality fusion at the syntactic and semantic levels and introduce contrastive learning between modalities and samples to better capture the difference and consistency between sentiments and emotions. Experiments on four public benchmark datasets, MOSI, MOSEI, MELD, and IEMOCAP, demonstrate the effectiveness of the proposed method and achieve consistent improvements compared with state-of-the-art methods.Comment: Accepted to EMNLP 2022 main conferenc

Factorial Connections in the Organizational Innovation: Proposed Systematization

Abstract In order to accomplish its innovational objectives, a company needs an optimal allocation of resources based on the appropriate mix of creativity and innovation factors. A multitude of theories, philosophies and approaches exists in the field of creativity and innovation (CRIN). The starting point of our study is the hypothesis of a critical mass for each innovational factor and for the whole aggregate; a second hypothesis states the existence of a critical mixture of organizational creativity and innovation factors. Our aim is to explore and systemize the arguments for the theoretical validation of these hypotheses and to propose a concrete manner of estimation for the costs and remunerations of innovational factors for an organization. We use the previous studies of Amabile (1998), Bouchard and Bose (2006), Ford (1993 Key words: innovational paradigms, organizational innovation factors, factorial connections Introduction There are quite numerous approaches of creativity and innovation (CRIN) in organizations. Different perspectives, different philosophies and also different classifications are almost always easy to recognize considering the researcher's country of origin. We are interested, in our approach, to find some common landmarks, in order to obtain a coherent list of creativity factors and organizational innovation variables, which would allow a company to take informed decisions concerning the convenient mixture of those factors and variables, together with the optimal allocation of resources. Our starting point is a research hypothesis based on a previous study (Zait D., Spalanzani A., 2010), which states that it exists a critical mass for each innovational factor and for the whole aggregate; a second research hypothesis states the existence of a critical mixture of organizational creativity and innovation factors. Our aim is to explore and systemize the arguments for the theoretical validation of these hypotheses and to propose a concrete manner of estimation for the costs and remunerations of innovational factors for an organization. Previous studies (Amabile, 1998, Bouchard and Bose, 2006, Ford, 199

UniSA: Unified Generative Framework for Sentiment Analysis

Sentiment analysis is a crucial task that aims to understand people's emotional states and predict emotional categories based on multimodal information. It consists of several subtasks, such as emotion recognition in conversation (ERC), aspect-based sentiment analysis (ABSA), and multimodal sentiment analysis (MSA). However, unifying all subtasks in sentiment analysis presents numerous challenges, including modality alignment, unified input/output forms, and dataset bias. To address these challenges, we propose a Task-Specific Prompt method to jointly model subtasks and introduce a multimodal generative framework called UniSA. Additionally, we organize the benchmark datasets of main subtasks into a new Sentiment Analysis Evaluation benchmark, SAEval. We design novel pre-training tasks and training methods to enable the model to learn generic sentiment knowledge among subtasks to improve the model's multimodal sentiment perception ability. Our experimental results show that UniSA performs comparably to the state-of-the-art on all subtasks and generalizes well to various subtasks in sentiment analysis.Comment: Accepted to ACM MM 202

API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs

Recent research has demonstrated that Large Language Models (LLMs) can enhance their capabilities by utilizing external tools. However, three pivotal questions remain unanswered: (1) How effective are current LLMs in utilizing tools? (2) How can we enhance LLMs' ability to utilize tools? (3) What obstacles need to be overcome to leverage tools? To address these questions, we introduce API-Bank, a groundbreaking benchmark, specifically designed for tool-augmented LLMs. For the first question, we develop a runnable evaluation system consisting of 73 API tools. We annotate 314 tool-use dialogues with 753 API calls to assess the existing LLMs' capabilities in planning, retrieving, and calling APIs. For the second question, we construct a comprehensive training set containing 1,888 tool-use dialogues from 2,138 APIs spanning 1,000 distinct domains. Using this dataset, we train Lynx, a tool-augmented LLM initialized from Alpaca. Experimental results demonstrate that GPT-3.5 exhibits improved tool utilization compared to GPT-3, while GPT-4 excels in planning. However, there is still significant potential for further improvement. Moreover, Lynx surpasses Alpaca's tool utilization performance by more than 26 pts and approaches the effectiveness of GPT-3.5. Through error analysis, we highlight the key challenges for future research in this field to answer the third question.Comment: EMNLP 202

Single Trace is All It Takes: Efficient Side-channel Attack on Dilithium

As the National Institute of Standards and Technology (NIST) concludes its post-quantum cryptography (PQC) competition, the winning algorithm, Dilithium, enters the deployment phase in 2024. This phase underscores the importance of conducting thorough practical security evaluations. Our study offers an in-depth side-channel analysis of Dilithium, showcasing the ability to recover the complete private key, ${s}_1$, within ten minutes using just two signatures and achieving a 60% success rate with a single signature. We focus on analyzing the polynomial addition in Dilithium, $z=y+{cs}_1$, by breaking down the attack into two main phases: the recovery of $y$ and ${cs}_1$ through side-channel attacks, followed by the resolution of a system of error-prone equations related to ${cs}_1$. Employing Linear Regression-based profiled attacks enables the successful recovery of the full $y$ value with a 40% success rate without the necessity for initial filtering. The extraction of ${cs}_1$ is further improved using a CNN model, which boasts an average success rate of 75%. A significant innovation of our research is the development of a constrained optimization-based residual analysis technique. This method efficiently recovers ${s}_1$ from a large set of error-containing equations concerning ${cs}_1$, proving effective even when only 10% of the equations are accurate. We conduct a practical attack on the Dilithium2 implementation on an STM32F4 platform, demonstrating that typically two signatures are sufficient for complete private key recovery, with a single signature sufficing in optimal conditions. Using a general-purpose PC, the full private key can be reconstructed in ten minutes

Fault Diagnosis of Motor Bearing by Analyzing a Video Clip

Conventional bearing fault diagnosis methods require specialized instruments to acquire signals that can reflect the health condition of the bearing. For instance, an accelerometer is used to acquire vibration signals, whereas an encoder is used to measure motor shaft speed. This study proposes a new method for simplifying the instruments for motor bearing fault diagnosis. Specifically, a video clip recording of a running bearing system is captured using a cellphone that is equipped with a camera and a microphone. The recorded video is subsequently analyzed to obtain the instantaneous frequency of rotation (IFR). The instantaneous fault characteristic frequency (IFCF) of the defective bearing is obtained by analyzing the sound signal that is recorded by the microphone. The fault characteristic order is calculated by dividing IFCF by IFR to identify the fault type of the bearing. The effectiveness and robustness of the proposed method are verified by a series of experiments. This study provides a simple, flexible, and effective solution for motor bearing fault diagnosis. Given that the signals are gathered using an affordable and accessible cellphone, the proposed method is proven suitable for diagnosing the health conditions of bearing systems that are located in remote areas where specialized instruments are unavailable or limited

Synthesis and performance of novel anion exchange membranes based on imidazolium ionic liquids for alkaline fuel cell applications

电子邮件地址:[email protected] anion exchange membranes (AEMs) based on two types of imidazolium ionic liquids, 1-vinyl-3-methylimidazolium iodide [VMI]I and 1-vinyl-3-butylimidazolium bromide [VBI]Br, have been synthesized by copolymerization. The obtained membranes are characterized in terms of water uptake, ion exchange capacity (IEC), ionic conductivity as well as thermal and chemical stability. The conductivity reaches 0.0226 Scm(-1) at 30 degrees C. All the membranes show excellent thermostability. The membranes are stable in 10 mol L-1 NaOH solution at 60 degrees C for 120 h without obvious changes in ion conductivity. Fuel cell performance using the resulting membrane has been investigated. The open circuit voltage (OCV) of the H-2/O-2 fuel cell is 1.07 V. A peek power density of 116 mW cm(-2) is obtained at a current density of 230 mA cm(-2) at 60 degrees C. The results demonstrate the brilliant prospect of the developed membranes for alkaline fuel cell applications.High-Tech Research and Development Program of China 2008AA05Z107 National Nature Science Foundation of China 20876129 21376195 2132106

Ancient mitochondrial genomes reveal extensive genetic influence of the steppe pastoralists in Western Xinjiang

The population prehistory of Xinjiang has been a hot topic among geneticists, linguists, and archaeologists. Current ancient DNA studies in Xinjiang exclusively suggest an admixture model for the populations in Xinjiang since the early Bronze Age. However, almost all of these studies focused on the northern and eastern parts of Xinjiang; the prehistoric demographic processes that occurred in western Xinjiang have been seldomly reported. By analyzing complete mitochondrial sequences from the Xiabandi (XBD) cemetery (3,500–3,300 BP), the up-to-date earliest cemetery excavated in western Xinjiang, we show that all the XBD mitochondrial sequences fall within two different West Eurasian mitochondrial DNA (mtDNA) pools, indicating that the migrants into western Xinjiang from west Eurasians were a consequence of the early expansion of the middle and late Bronze Age steppe pastoralists (Steppe_MLBA), admixed with the indigenous populations from Central Asia. Our study provides genetic links for an early existence of the Indo-Iranian language in southwestern Xinjiang and suggests that the existence of Andronovo culture in western Xinjiang involved not only the dispersal of ideas but also population movement.Introduction Materials and methods - Archaeological Background, Sampling, and Sequencing - Sequence Mapping and Mitochondrial DNA Haplogroup Determination - Analysis of Xiabandi Mitochondrial DNA Genomes Results - Mitochondrial DNA Authentication and Contamination Assessment - Major Bronze Age Steppe Pastoralist Origin of the Xiabandi Mitochondrial Haplogroups - Expansion of the Bronze Age Steppe Pastoralists as a Dynamic Process to Form the Genetic Landscape of Xiabandi Individuals Discussion Conclusion