Aspect Sentiment Triplet Extraction (ASTE) aims to co-extract the sentiment triplets in a given corpus. Existing approaches within the pretraining-finetuning paradigm tend to either meticulously craft complex tagging schemes and classification heads, or incorporate external semantic augmentation to enhance performance. In this study, we, for the first time, re-evaluate the redundancy in tagging schemes and the internal enhancement in pretrained representations. We propose a method to improve and utilize pretrained representations by integrating a minimalist tagging scheme and a novel token-level contrastive learning strategy. The proposed approach demonstrates comparable or superior performance compared to state-of-the-art techniques while featuring a more compact design and reduced computational overhead. Additionally, we are the first to formally evaluate GPT-4's performance in few-shot learning and Chain-of-Thought scenarios for this task. The results demonstrate that the pretraining-finetuning paradigm remains highly effective even in the era of large language models.

With the attention mechanism, transformers achieve significant empirical successes. Despite the intuitive understanding that transformers perform relational inference over long sequences to produce desirable representations, we lack a rigorous theory on how the attention mechanism achieves it. In particular, several intriguing questions remain open: (a) What makes a desirable representation? (b) How does the attention mechanism infer the desirable representation within the forward pass? (c) How does a pretraining procedure learn to infer the desirable representation through the backward pass? We observe that, as is the case in BERT and ViT, input tokens are often exchangeable since they already include positional encodings. The notion of exchangeability induces a latent variable model that is invariant to input sizes, which enables our theoretical analysis. - To answer (a) on representation, we establish the existence of a sufficient and minimal representation of input tokens. In particular, such a representation instantiates the posterior distribution of the latent variable given input tokens, which plays a central role in predicting output labels and solving downstream tasks. - To answer (b) on inference, we prove that attention with the desired parameter infers the latent posterior up to an approximation error, which is decreasing in input sizes. In detail, we quantify how attention approximates the conditional mean of the value given the key, which characterizes how it performs relational inference over long sequences. - To answer (c) on learning, we prove that both supervised and self-supervised objectives allow empirical risk minimization to learn the desired parameter up to a generalization error, which is independent of input sizes. Particularly, in the self-supervised setting, we identify a condition number that is pivotal to solving downstream tasks.

This paper proposes two important contributions for conditional Generative Adversarial Networks (cGANs) to improve the wide variety of applications that exploit this architecture. The first main contribution is an analysis of cGANs to show that they are not explicitly conditional. In particular, it will be shown that the discriminator and subsequently the cGAN does not automatically learn the conditionality between inputs. The second contribution is a new method, called acontrario, that explicitly models conditionality for both parts of the adversarial architecture via a novel acontrario loss that involves training the discriminator to learn unconditional (adverse) examples. This leads to a novel type of data augmentation approach for GANs (acontrario learning) which allows to restrict the search space of the generator to conditional outputs using adverse examples. Extensive experimentation is carried out to evaluate the conditionality of the discriminator by proposing a probability distribution analysis. Comparisons with the cGAN architecture for different applications show significant improvements in performance on well known datasets including, semantic image synthesis, image segmentation and monocular depth prediction using different metrics including Fr\'echet Inception Distance(FID), mean Intersection over Union (mIoU), Root Mean Square Error log (RMSE log) and Number of statistically-Different Bins (NDB)

Many plants become limp or droop through heat, loss of water, or disease. This is also known as wilting. In this paper, we examine plant wilting caused by bacterial infection. In particular, we want to design a metric for wilting based on images acquired of the plant. A quantifiable wilting metric will be useful in studying bacterial wilt and identifying resistance genes. Since there is no standard way to estimate wilting, it is common to use ad hoc visual scores. This is very subjective and requires expert knowledge of the plants and the disease mechanism. Our solution consists of using various wilting metrics acquired from RGB images of the plants. We also designed several experiments to demonstrate that our metrics are effective at estimating wilting in plants.

I recently went to a Data & Healthcare meetup to see how a renowned cancer research institute, Memorial Sloan Kettering Cancer Center (MSK), applies and uses Data Science. One of the first uses they mentioned was predictive modeling for post-hospital care. Whether it is from being discharged after an acute condition that required a hospital stay or perhaps going home after a chemotherapy treatment, MSK made it clear that their treatment does not end just because you left their premises. MSK collects data that tracks how often a patient may call, the reason for their call, and the outcome -- whether it is to come into the hospital for immediate care or otherwise. With this data, they can use predictive modeling to assess the needs and interventions a patient may need in the future. Data Science and machine learning can also be used to help predict pain crises.

Machine learning on encrypted data has received a lot of attention thanks to recent breakthroughs in homomorphic encryption and secure multi-party computation. It allows outsourcing computation to untrusted servers without sacrificing privacy of sensitive data. We propose a practical framework to perform partially encrypted and privacy-preserving predictions which combines adversarial training and functional encryption. We first present a new functional encryption scheme to efficiently compute quadratic functions so that the data owner controls what can be computed but is not involved in the calculation: it provides a decryption key which allows one to learn a specific function evaluation of some encrypted data. We then show how to use it in machine learning to partially encrypt neural networks with quadratic activation functions at evaluation time, and we provide a thorough analysis of the information leaks based on indistinguishability of data items of the same label. Last, since most encryption schemes cannot deal with the last thresholding operation used for classification, we propose a training method to prevent selected sensitive features from leaking, which adversarially optimizes the network against an adversary trying to identify these features. This is interesting for several existing works using partially encrypted machine learning as it comes with little reduction on the model's accuracy and significantly improves data privacy.

STAN is a Graphplan-based planner, so-called because it uses a variety of STate ANalysis techniques to enhance its performance. STAN competed in the AIPS-98 planning competition where it compared well with the other competitors in terms of speed, finding solutions fastest to many of the problems posed. Although the domain analysis techniques STAN exploits are an important factor in its overall performance, we believe that the speed at which STAN solved the competition problems is largely due to the implementation of its plan graph. The implementation is based on two insights: that many of the graph construction operations can be implemented as bit-level logical operations on bit vectors, and that the graph should not be explicitly constructed beyond the fix point. This paper describes the implementation of STAN's plan graph and provides experimental results which demonstrate the circumstances under which advantages can be obtained from using this implementation.