Integration of audio perception into large language models (LLMs) is an emerging research area for enabling machine listening applications, yet efficient transfer of rich audio semantics from audio encoders to LLMs remains underexplored. The most widely used integration paradigm projects the audio encoder output tokens into the LLM input space (e.g., via an MLP or a Q-Former), then prepends or inserts them to the text tokens. We refer to this generic scheme as Prepend to the LLM's input token space (PLITS) integration. We propose an efficient alternative, Lightweight Audio LLM Integration (LAL). LAL introduces audio representations solely via the attention mechanism within different layers of the LLM, bypassing its feedforward module. LAL encodes rich audio semantics at an appropriate level of abstraction for integration into different blocks of LLMs. Our design significantly reduces computational overhead compared to existing integration approaches. Observing with Whisper that the speech encoder benefits from PLITS integration, we propose an audio encoder aware approach for efficiently Probing Audio encoders via LLM (PAL), which employs PLITS integration for Whisper and LAL for general audio encoders. Under an identical training curriculum, LAL consistently maintains performance or outperforms existing integration approaches across multiple base LLMs and tasks. For general audio tasks, LAL improvement is up to 30% over a strong PLITS baseline while reducing memory usage by up to 64.1% and increasing throughput by up to 247.5%. Furthermore, for general audio-music-speech LLM, PAL performs on par with a fully PLITS integration-based system but with substantially improved computational and memory efficiency. Project page: https://ta012.github.io/PAL/

Variation in language use, shaped by speakers' sociocultural background and specific context of use, offers a rich lens into cultural perspectives, values, and opinions. However, the computational study of these Sociocultural Linguistic Phenomena (SLP) has often been limited to bespoke analyses of specific groups or topics, hindering the pace of scientific discovery. To address this, we introduce Splits!, a 9.7 million-post dataset from Reddit designed for systematic and flexible research. The dataset contains posts from over 53,000 users across 6 demographic groups, organized into 89 discussion topics to enable comparative analysis. We validate Splits! via self-identification and by successfully replicating several known SLPs from existing literature. We complement this dataset with a framework that leverages efficient retrieval methods to rapidly validate potential SLPs (PSLPs) by automatically evaluating whether a given hypothesis is supported by our data. Crucially, to distinguish between novel and obvious insights, the framework incorporates a human-validated measure of a hypothesis's ``unexpectedness.'' We demonstrate that the two-stage process reduces the number of statistically significant findings requiring manual inspection by a factor of 1.5-1.8x, streamlining the discovery of promising phenomena for further investigation.

Wearable sensing devices, such as Holter monitors, will play a crucial role in the future of digital health. Unsupervised learning frameworks such as Self-Supervised Learning (SSL) are essential to map these single-lead electrocardiogram (ECG) signals with their anticipated clinical outcomes. These signals are characterized by a tempo-variant component whose patterns evolve through the recording and an invariant component with patterns that remain unchanged. However, existing SSL methods only drive the model to encode the invariant attributes, leading the model to neglect tempo-variant information which reflects subject-state changes through time. In this paper, we present Parallel-Learning of Invariant and Tempo-variant Attributes (PLITA), a novel SSL method designed for capturing both invariant and tempo-variant ECG attributes. The latter are captured by mandating closer representations in space for closer inputs on time. We evaluate both the capability of the method to learn the attributes of these two distinct kinds, as well as PLITA's performance compared to existing SSL methods for ECG analysis. PLITA performs significantly better in the set-ups where tempo-variant attributes play a major role.

The recent popularity of Wordle has revived interest in guessing games. We develop a general method for finding optimal strategies for guessing games while avoiding an exhaustive search. Our main contributions are several theorems that build towards a general theory to prove the optimality of a strategy for a guessing game. This work is developed to apply to any guessing game, but we use Wordle as an example to present concrete results.

Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs which play a significant role in several biological processes. RNA-seq based transcriptome sequencing has been extensively used for identification of lncRNAs. However, accurate identification of lncRNAs in RNA-seq datasets is crucial for exploring their characteristic functions in the genome as most coding potential computation (CPC) tools fail to accurately identify them in transcriptomic data. Well-known CPC tools such as CPC2, lncScore, CPAT are primarily designed for prediction of lncRNAs based on the GENCODE, NONCODE and CANTATAdb databases. The prediction accuracy of these tools often drops when tested on transcriptomic datasets. This leads to higher false positive results and inaccuracy in the function annotation process. In this study, we present a novel tool, PLIT, for the identification of lncRNAs in plants RNA-seq datasets. PLIT implements a feature selection method based on L1 regularization and iterative Random Forests (iRF) classification for selection of optimal features. Based on sequence and codon-bias features, it classifies the RNA-seq derived FASTA sequences into coding or long non-coding transcripts. Using L1 regularization, 31 optimal features were obtained based on lncRNA and protein-coding transcripts from 8 plant species. The performance of the tool was evaluated on 7 plant RNA-seq datasets using 10-fold cross-validation. The analysis exhibited superior accuracy when evaluated against currently available state-of-the-art CPC tools.