A Compact LSTM-SVM Fusion Model for Long-Duration Cardiovascular Diseases Detection

Globally, cardiovascular diseases (CVDs) are the leading cause of mortality, accounting for an estimated 17.9 million deaths annually. One critical clinical objective is the early detection of CVDs using electrocardiogram (ECG) data, an area that has received significant attention from the research community. Recent advancements based on machine learning and deep learning have achieved great progress in this domain. However, existing methodologies exhibit inherent limitations, including inappropriate model evaluations and instances of data leakage. In this study, we present a streamlined workflow paradigm for preprocessing ECG signals into consistent 10-second durations, eliminating the need for manual feature extraction/beat detection. We also propose a hybrid model of Long Short-Term Memory (LSTM) with Support Vector Machine (SVM) for fraud detection. This architecture consists of two LSTM layers and an SVM classifier, which achieves a SOTA results with an Average precision score of 0.9402 on the MIT-BIH arrhythmia dataset and 0.9563 on the MIT-BIH atrial fibrillation dataset. Based on the results, we believe our method can significantly benefit the early detection and management of CVDs

Quantitative Value Strategy Beats the Market

The objective of this paper is to test if Quantitative Value Strategy works in Canadian market. Combining different value investment strategies from researchers and industry pioneers, a slightly revised existing strategy is used to back-­‐test historical returns in the Canadian stock market. All the value strategies, commonly used by others, are small deviations from three main strategies mentioned in the literature review.  In order to identify stocks that have greater intrinsic value but at cheap prices, numerous ratios have been used in the screening process. There are main three steps for identifying the wanted stocks. First of all, eliminate all stocks with high possible earning manipulations. Next, identify cheap stocks using various financial ratios. Lastly, find the best quality stocks. Rankings are based on prior year-­‐end financial data collected from Bloomberg. A long-­‐short strategy is applied with one hundred twenty percent long and twenty percent in short positions. Based on prior year-­‐end market capitalization, weights are assigned to each selected stocks. Once the portfolio is formed, these securities follow buy-­‐and-­‐hold strategy until the next rebalance cycle, which is the following year end.  After back-­‐testing ten-­‐year data from 2002 to 2012, the 120/20 portfolio annualized return is 11.53%, which is 4.92% higher than the annual SP/TSX index return. When looking at the returns alone, the constructed portfolio does outperform; however, the excess return is not risk adjusted as indicated in regression test. The value strategy is able to beat the market, but the excess returns mainly come from excess risks that the strategy has been taken. After risk adjustment, the constructed portfolio no longer beats the market; in other words, the famous value strategy fails to beat the market after taking consideration of risk factors.  Moreover, there are several potential implementation issues that could erode the performance return, such as transaction costs, dividend reinvestment and look-­‐ ahead issue. Also, some financial behaviors are observed to prevent from beating the benchmark returns.&nbsp

TBC-YOLOv7: a refined YOLOv7-based algorithm for tea bud grading detection

IntroductionAccurate grading identification of tea buds is a prerequisite for automated tea-picking based on machine vision system. However, current target detection algorithms face challenges in detecting tea bud grades in complex backgrounds. In this paper, an improved YOLOv7 tea bud grading detection algorithm TBC-YOLOv7 is proposed.MethodsThe TBC-YOLOv7 algorithm incorporates the transformer architecture design in the natural language processing field, integrating the transformer module based on the contextual information in the feature map into the YOLOv7 algorithm, thereby facilitating self-attention learning and enhancing the connection of global feature information. To fuse feature information at different scales, the TBC-YOLOv7 algorithm employs a bidirectional feature pyramid network. In addition, coordinate attention is embedded into the critical positions of the network to suppress useless background details while paying more attention to the prominent features of tea buds. The SIOU loss function is applied as the bounding box loss function to improve the convergence speed of the network.ResultThe results of the experiments indicate that the TBC-YOLOv7 is effective in all grades of samples in the test set. Specifically, the model achieves a precision of 88.2% and 86.9%, with corresponding recall of 81% and 75.9%. The mean average precision of the model reaches 87.5%, 3.4% higher than the original YOLOv7, with average precision values of up to 90% for one bud with one leaf. Furthermore, the F1 score reaches 0.83. The model’s performance outperforms the YOLOv7 model in terms of the number of parameters. Finally, the results of the model detection exhibit a high degree of correlation with the actual manual annotation results ( R2 =0.89), with the root mean square error of 1.54.DiscussionThe TBC-YOLOv7 model proposed in this paper exhibits superior performance in vision recognition, indicating that the improved YOLOv7 model fused with transformer-style module can achieve higher grading accuracy on densely growing tea buds, thereby enables the grade detection of tea buds in practical scenarios, providing solution and technical support for automated collection of tea buds and the judging of grades

A robust modulation classification method using convolutional neural networks

Automatic modulation classification (AMC) is a core technique in noncooperative communication systems. In particular, feature-based (FB) AMC algorithms have been widely studied. Current FB AMC methods are commonly designed for a limited set of modulation and lack of generalization ability; to tackle this challenge, a robust AMC method using convolutional neural networks (CNN) is proposed in this paper. In total, 15 different modulation types are considered. The proposed method can classify the received signal directly without feature extracion, and it can automatically learn features from the received signals. The features learned by the CNN are presented and analyzed. The robust features of the received signals in a specific SNR range are studied. The accuracy of classification using CNN is shown to be remarkable, particularly for low SNRs. The generalization ability of robust features is also proven to be excellent using the support vector machine (SVM). Finally, to help us better understand the process of feature learning, some outputs of intermediate layers of the CNN are visualized

Visual Dexterity: In-hand Dexterous Manipulation from Depth

In-hand object reorientation is necessary for performing many dexterous manipulation tasks, such as tool use in unstructured environments that remain beyond the reach of current robots. Prior works built reorientation systems that assume one or many of the following specific circumstances: reorienting only specific objects with simple shapes, limited range of reorientation, slow or quasistatic manipulation, the need for specialized and costly sensor suites, simulation-only results, and other constraints which make the system infeasible for real-world deployment. We overcome these limitations and present a general object reorientation controller that is trained using reinforcement learning in simulation and evaluated in the real world. Our system uses readings from a single commodity depth camera to dynamically reorient complex objects by any amount in real time. The controller generalizes to novel objects not used during training. It is successful in the most challenging test: the ability to reorient objects in the air held by a downward-facing hand that must counteract gravity during reorientation. The results demonstrate that the policy transfer from simulation to the real world can be accomplished even for dynamic and contact-rich tasks. Lastly, our hardware only uses open-source components that cost less than five thousand dollars. Such construction makes it possible to replicate the work and democratize future research in dexterous manipulation. Videos are available at: https://taochenshh.github.io/projects/visual-dexterity

Management and outcomes of proteasome inhibitor associated chalazia and blepharitis: a case series.

BACKGROUND: The purpose of this case series was to further characterize proteasome inhibitor associated chalazia and blepharitis, to investigate outcomes of different management strategies, and to propose a treatment algorithm for eyelid complications in this patient population. METHODS: This retrospective case series included sixteen patients found to have chalazia and/or blepharitis while receiving proteasome inhibitors for plasma cell disorders at Mount Sinai Hospital in New York, NY from January 2010 through January 2017. Main outcomes were complete resolution of eyelid complications and time to resolution. Students t-test was used to compare average values and Fishers exact test was used to compare proportions. RESULTS: Fourteen patients had chalazia and 10 had blepharitis. Chalazia averaged 5.4 mm, and 11 patients with chalazia experienced two or more lesions. Median follow-up time was 17 months. Average time from bortezomib exposure to onset of first eyelid complication was 3.4 months. Chalazia episodes were more likely to completely resolve than blepharitis episodes (p = 0.03). Ocular therapy alone was trialed for an average of 1.8 months before proceeding to bortezomib omission. Average time to eyelid complication resolution using ocular therapy alone was 1.8 months versus 3.1 months after bortezomib omission. In this series, the combination of ocular therapy and bortezomib omission led to complete resolution of eyelid complications more often than ocular therapy alone. CONCLUSION: Proteasome inhibitor associated eyelid complications were identified in sixteen patients with plasma cell disorders. Eyelid complications may be treated with a 2-month trial of conservative ocular therapies alone, followed by continuation of ocular therapy in combination with bortezomib omission if eyelid signs persist

You Are What You Annotate: Towards Better Models through Annotator Representations

Annotator disagreement is ubiquitous in natural language processing (NLP) tasks. There are multiple reasons for such disagreements, including the subjectivity of the task, difficult cases, unclear guidelines, and so on. Rather than simply aggregating labels to obtain data annotations, we instead try to directly model the diverse perspectives of the annotators, and explicitly account for annotators' idiosyncrasies in the modeling process by creating representations for each annotator (annotator embeddings) and also their annotations (annotation embeddings). In addition, we propose TID-8, The Inherent Disagreement - 8 dataset, a benchmark that consists of eight existing language understanding datasets that have inherent annotator disagreement. We test our approach on TID-8 and show that our approach helps models learn significantly better from disagreements on six different datasets in TID-8 while increasing model size by fewer than 1% parameters. By capturing the unique tendencies and subjectivity of individual annotators through embeddings, our representations prime AI models to be inclusive of diverse viewpoints.Comment: Accepted to Findings of EMNLP 202

In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation

Large language models (LLMs) frequently hallucinate and produce factual errors, yet our understanding of why they make these errors remains limited. In this study, we delve into the underlying mechanisms of LLM hallucinations from the perspective of inner representations, and discover a salient pattern associated with hallucinations: correct generations tend to have sharper context activations in the hidden states of the in-context tokens, compared to the incorrect ones. Leveraging this insight, we propose an entropy-based metric to quantify the ``sharpness'' among the in-context hidden states and incorporate it into the decoding process to formulate a constrained decoding approach. Experiments on various knowledge-seeking and hallucination benchmarks demonstrate our approach's consistent effectiveness, for example, achieving up to an 8.6 point improvement on TruthfulQA. We believe this study can improve our understanding of hallucinations and serve as a practical solution for hallucination mitigation.Comment: code repo is available at: https://github.com/hkust-nlp/Activation_decoding.gi