Class-Incremental Learning based on Label Generation

Despite the great success of pre-trained language models, it is still a challenge to use these models for continual learning, especially for the class-incremental learning (CIL) setting due to catastrophic forgetting (CF). This paper reports our finding that if we formulate CIL as a continual label generation problem, CF is drastically reduced and the generalizable representations of pre-trained models can be better retained. We thus propose a new CIL method (VAG) that also leverages the sparsity of vocabulary to focus the generation and creates pseudo-replay samples by using label semantics. Experimental results show that VAG outperforms baselines by a large margin.Comment: 12 pages, ACL 2023 Main Conferenc

Learning to Program with Natural Language

Large Language Models (LLMs) have shown remarkable performance in various basic natural language tasks, which raises hope for achieving Artificial General Intelligence. For completing the complex task, we still need a program for the task first and then ask LLMs to follow the program to generate the specific solution. We propose using natural language as a new programming language to describe task procedures, making them easily understandable to both humans and LLMs. ~The LLM is capable of directly generating natural language programs, but these programs may still contain factual errors or incomplete steps. Therefore, we further propose the Learning to Program (\text{LP}) method to ask LLMs themselves to learn the natural language program based on the training dataset of the complex task first and then use the learned program to guide the inference. Our experiments on the reasoning tasks of five different reasoning types (8 datasets) demonstrate the effectiveness of our approach. Further, our analysis experiment shows that the learned program can be directly used to guide another LLM to improve its performance, which reveals a new transfer learning paradigm.Comment: Work in progres

A Novel STAP Algorithm for Airborne MIMO Radar Based on Temporally Correlated Multiple Sparse Bayesian Learning

In a heterogeneous environment, to efficiently suppress clutter with only one snapshot, a novel STAP algorithm for multiple-input multiple-output (MIMO) radar based on sparse representation, referred to as MIMOSR-STAP in this paper, is presented. By exploiting the waveform diversity of MIMO radar, each snapshot at the tested range cell can be transformed into multisnapshots for the phased array radar, which can estimate the high-resolution space-time spectrum by using multiple measurement vectors (MMV) technique. The proposed approach is effective in estimating the spectrum by utilizing Temporally Correlated Multiple Sparse Bayesian Learning (TMSBL). In the sequel, the clutter covariance matrix (CCM) and the corresponding adaptive weight vector can be efficiently obtained. MIMOSR-STAP enjoys high accuracy and robustness so that it can achieve better performance of output signal-to-clutter-plus-noise ratio (SCNR) and minimum detectable velocity (MDV) than the single measurement vector sparse representation methods in the literature. Thus, MIMOSR-STAP can deal with badly inhomogeneous clutter scenario more effectively, especially suitable for insufficient independent and identically distributed (IID) samples environment

AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models

Evaluating the general abilities of foundation models to tackle human-level tasks is a vital aspect of their development and application in the pursuit of Artificial General Intelligence (AGI). Traditional benchmarks, which rely on artificial datasets, may not accurately represent human-level capabilities. In this paper, we introduce AGIEval, a novel benchmark specifically designed to assess foundation model in the context of human-centric standardized exams, such as college entrance exams, law school admission tests, math competitions, and lawyer qualification tests. We evaluate several state-of-the-art foundation models, including GPT-4, ChatGPT, and Text-Davinci-003, using this benchmark. Impressively, GPT-4 surpasses average human performance on SAT, LSAT, and math competitions, attaining a 95% accuracy rate on the SAT Math test and a 92.5% accuracy on the English test of the Chinese national college entrance exam. This demonstrates the extraordinary performance of contemporary foundation models. In contrast, we also find that GPT-4 is less proficient in tasks that require complex reasoning or specific domain knowledge. Our comprehensive analyses of model capabilities (understanding, knowledge, reasoning, and calculation) reveal these models' strengths and limitations, providing valuable insights into future directions for enhancing their general capabilities. By concentrating on tasks pertinent to human cognition and decision-making, our benchmark delivers a more meaningful and robust evaluation of foundation models' performance in real-world scenarios. The data, code, and all model outputs are released in https://github.com/microsoft/AGIEval.Comment: 19 page

Body mass index-associated responses to an ABVD-like regimen in newly-diagnosed patients with Hodgkin lymphoma

Background: The role of body mass index (BMI) in the treatment outcomes of lymphoma patients is controversial. While investigating the efficacy of ABVD-like regimen in Hodgkin lymphoma (HL) patients, we observed that obese patients had poor responses. To better understand this clinical phenomenon, we evaluated the effect of BMI on responses to ABVD-like chemotherapy in HL patients.Methods: This retrospective cohort study evaluated the clinical outcomes of all 67 patients with confirmed HL who were treated at the First Affiliated Hospital of Soochow University from November 2016 to March 2023 with an ABVD-like regimen as first-line chemotherapy. Baseline patient characteristics and clinical outcomes were compared across different BMI categories. The primary end-point was the overall response rate defined as the proportion of the HL patients who achieved complete response or partial response. The additional end-points included progression-free survival and overall survival.Results: The median age of the HL patients was 31 years old. Of the patients, 10.4% were obese, and 17.9% patients were overweight. Interim and end-term response evaluations revealed overall response rates of 98.5% and 83.6%, respectively. The proportion of patients with potential poor prognostic factors (IPS risk factors) did not differ significantly in the responders versus non-responders. However, non-responders had a higher average BMI when compared with responders (p = 0.002). Poor overall response rates in higher BMI patients indeed manifested with shorter progression free survival (p = 0.013). The minimum relative dose of the ABVD-like regimen in the overweight and obese groups was significantly lower than in the normal weight group (p < 0.001).Conclusion: Our analyses show that >80% of newly-diagnosed HL patients responded to the ABVD-like regimen. We find that being obese or overweight at the time of diagnosis correlated with a poorer overall response rate and that BMI was an independent risk factor in HL patients treated with the ABVD-like regimen. Lower doses of ABVD-like regimen contributed to the discrepant findings of responses in the high BMI groups. These findings indicate that newly-diagnosed, obese HL patients receiving an ABVD-like regimen require personalized treatment

Low-Complexity 2D DOA Estimation and Self-Calibration for Uniform Rectangle Array with Gain-Phase Error

Most subspace-based algorithms need exact array manifold for direction of arrival (DOA) estimation, while, in practical applications, the gain-phases of different array elements are usually inconsistent, degrading their estimation performance. In this paper, a novel low-complexity 2D DOA and gain-phase error estimation algorithm is proposed by adding auxiliary array elements in a uniform rectangular array (URA). Firstly, the URA is modeled as the Kronecker product of two uniform linear arrays (ULAs) to decouple the 2D DOA estimation. Then, several well-calibrated auxiliary array elements are added in the two ULAs, based on which the rotation invariant factor of the URA destroyed by the gain-phase error is reconstructed by solving constrained optimization problems. Lastly, ESPRIT is used to estimate the 2-D DOA and the gain-phase error coefficients. The closed-form expressions of the estimation CRBs are also derived, providing insight into the impact of gain-phase error on DOA estimation. Simulation results are used to validate the effectiveness of the proposed algorithm and the correctness of the theoretical analysis

An Angle Estimation Method for Monostatic MIMO Radar Based on RCC-FLOM Algorithm

The performance of the angle estimation algorithm based on the two-order or higher order cumulants in the impact noise background will decline sharply. Therefore, it is necessary to study the new algorithm to estimate target angle in the impact noise background. In order to solve the angle estimation problem of coherent sources in the impulse noise background, a conjugate rotation invariant subspace algorithm based on reduced order fractional lower order covariance matrix is proposed. Use the reduced dimension lower order fraction covariance matrix to reduce the impulse noise influence. And according to the conjugate rotation invariant subspace, the coherent source is decohered. The Monte-Carlo experiments show that the proposed algorithm has the advantages of high estimation probability and low root mean square error in the case of low signal-to-noise ratio, compared with the existing FLOM-MUSIC algorithm and FLOM-Unitary ESPRIT algorithm

Local Degree of Freedom of Clutter for Reduced-Dimension Space-Time Adaptive Processing with MIMO Radar

Degree of freedom (DOF) of clutter in the reduced-dimension (RD) domain, which is called local DOF (LDOF), is of great importance for RD MIMO-STAP (space-time adaptive processing for multiple-input multiple-output radar) algorithms. In this paper, the LDOF equivalence of different RD MIMO-STAP algorithms are firstly proved, and then a generalized LDOF estimation rule under different conditions is developed to estimate the clutter LDOF for MIMO radar effectively. The accuracy of the proposed rule is verified, and how to design RD MIMO-STAP processors under the guidance of the proposed rule is presented through numerical simulations

Unified Theoretical Frame of a Joint Transmitter-Receiver Reduced Dimensional STAP Method for an Airborne MIMO Radar

The unified theoretical frame of a joint transmitter-receiver reduced dimensional Space-Time Adaptive Processing (STAP) method is studied for an airborne Multiple-Input Multiple-Output (MIMO) radar. First, based on the transmitted waveform diverse characteristics of the transmitted waveform of the airborne MIMO radar, a uniform theoretical frame structure for the reduced dimensional joint adaptive STAP is constructed. Based on it, three reduced dimensional STAP fixed structures are established. Finally, three reduced rank STAP algorithms, which are suitable for a MIMO system, are presented corresponding to the three reduced dimensional STAP fixed structures. The simulations indicate that the joint adaptive algorithms have preferable clutter suppression and anti-interference performance