With the recent emergence of powerful instruction-tuned large language models (LLMs), various helpful conversational Artificial Intelligence (AI) systems have been deployed across many applications. When prompted by users, these AI systems successfully perform a wide range of tasks as part of a conversation. To provide some sort of memory and context, such approaches typically condition their output on the entire conversational history. Although this sensitivity to the conversational history can often lead to improved performance on subsequent tasks, we find that performance can in fact also be negatively impacted, if there is a task-switch. To the best of our knowledge, our work makes the first attempt to formalize the study of such vulnerabilities and interference of tasks in conversational LLMs caused by task-switches in the conversational history. Our experiments across 5 datasets with 15 task switches using popular LLMs reveal that many of the task-switches can lead to significant performance degradation.

Lack of audio-video synchronization is a common problem during television broadcasts and video conferencing, leading to an unsatisfactory viewing experience. A widely accepted paradigm is to create an error detection mechanism that identifies the cases when audio is leading or lagging. We propose ModEFormer, which independently extracts audio and video embeddings using modality-specific transformers. Different from the other transformer-based approaches, ModEFormer preserves the modality of the input streams which allows us to use a larger batch size with more negative audio samples for contrastive learning. Further, we propose a trade-off between the number of negative samples and number of unique samples in a batch to significantly exceed the performance of previous methods. Experimental results show that ModEFormer achieves state-of-the-art performance, 94.5% for LRS2 and 90.9% for LRS3. Finally, we demonstrate how ModEFormer can be used for offset detection for test clips.

A drawback of these methods that perform important tasks related to methods however is that, they tend not to generalise well drug design such as receptor binding site detection, to data that does not resemble the data distribution used for small molecule docking and binding affinity training. The viability of such models therefore depend on prediction. However, these methods are usually well curated training data that translates well into real world trained on only ligand bound (or holo) conformations applications. of the protein and therefore are not guaranteed to perform well when the protein structure Deep Learning models pertaining to SBDD workflows are is in its native unbound conformation (or apo), usually trained on datasets containing 3D structures of which is usually the conformation available for protein-ligand complexes (Batool et al., 2019). PDBbind a newly identified receptor. A primary reason (Wang et al., 2005) is a predominantly used dataset that provides for this is that the local structure of the binding experimental binding affinity values for protein-ligand site usually changes upon ligand binding. To facilitate co-crystal structures present in the Protein Data Bank (PDB) solutions for this problem, we propose a (Berman et al., 2000). Deep learning architectures usually dataset called APObind that aims to provide apo use voxelized (Jiménez et al., 2018) or graph like representations conformations of proteins present in the PDBbind (Son & Kim, 2021) of the 3D structures present in dataset, a popular dataset used in drug design. Furthermore, PDBbind for computation to get benchmark performances.

Sepsis is one of the leading causes of death in Intensive Care Units (ICU). The strategy for treating sepsis involves the infusion of intravenous (IV) fluids and administration of antibiotics. Determining the optimal quantity of IV fluids is a challenging problem due to the complexity of a patient's physiology. In this study, we develop a data-driven optimization solution that derives the optimal quantity of IV fluids for individual patients. The proposed method minimizes the probability of severe outcomes by controlling the prescribed quantity of IV fluids and utilizes human-in-the-loop artificial intelligence. We demonstrate the performance of our model on 1122 ICU patients with sepsis diagnosis extracted from the MIMIC-III dataset. The results show that, on average, our model can reduce mortality by 22%. This study has the potential to help physicians synthesize optimal, patient-specific treatment strategies.