Abstract

The maximum entropy principle, as applied to quantum systems, is a fundamental prescript positing that for a quantum system for which we only have partial knowledge, the maximum entropy state consistent with the partial knowledge is a valuable choice as the system's state. An intriguing result is that in case the only prior knowledge is of a fixed energy, the maximum entropy state turns out to be the thermal state, a ubiquitous state in several arenas, especially in statistical mechanics. We extend the consequences of this principle from quantum states to quantum processes. We establish that a quantum channel attains maximal output entropy under a fixed energy constraint if and only if it is an absolutely thermalizing channel, where the fixed output is the thermal state corresponding to that energy. Our results have potential implications for understanding the informational and thermodynamic utility of quantum channels under physical constraints. As an application, we examine the consequences for private randomness distillation from fixed energy constrained quantum processes.

Abstract

Developing Automatic Speech Recognition (ASR)
systems for specialized medical domains is challenging in low-
resource settings due to the limited availability of annotated
speech data. Synthetic speech generated using text to speech
(TTS) systems is often used to augment training data, but directly
mixing synthetic and real speech can introduce distribution
mismatch that degrades encoder representations. This work
introduces Real-Governed Representation-Calibrated Training
(RG-RCT), a training strategy that regulates the influence of syn-
thetic speech at the representation level. The proposed framework
combines reliability-based confidence estimation, representation
alignment, and layer-wise representation governance to stabilize
the encoder feature space while preserving acoustic diversity from
multi-speaker synthetic data. Experiments on medical-domain
speech datasets using Wav2Vec2 and Whisper demonstrate that
RG-RCT consistently outperforms conventional strategies such
as direct mixing and confidence-aware training. The proposed
method achieves the lowest Word Error Rate (WER), reaching
17.1% and 16.6% for Telugu and 23.3% and 25.6% for Kannada.
These results indicate that regulating encoder representation
geometry enables more effective utilization of synthetic speech
for low-resource medical ASR

Abstract

Natural and intuitive human-computer interaction
has created a growing demand for digital writing and gesture based input systems that can operate beyond the constraints of touchscreens and specialized tablets. Inertial sensing offers a low cost and flexible solution for enabling pen-based interaction on arbitrary surfaces; however, accurate motion tracking remains challenging due to the influence of gravitational acceleration during dynamic pen movements.
This work addresses the problem of dynamic tilt estimation in inertial sensing for pen-based trajectory localization. Inertial Measurement Units (IMUs), consisting of tri-axial accelerometers and gyroscopes, are commonly used to estimate motion by integrating acceleration signals. However, during handwriting, the pen undergoes continuous tilt, causing the gravitational acceleration vector to project onto all sensor axes. This contaminates the measured acceleration and makes direct integration unreliable, a challenge commonly referred to as the dynamic tilt problem.
In this paper, we focus on estimating the orientation of the pen in order to separate the gravitational component from the measured acceleration using a single tri-axial accelerometer. Under practical assumptions on the writing surface and motion continuity, we formulate tilt estimation as a constrained optimization problem. The proposed framework provides a robust and computationally efficient approach for gravity compensation, serving as a fundamental step toward accurate trajectory reconstruction. Experimental results demonstrate that the proposed approach provides stable and accurate tilt estimates, thereby improving the quality of motion signals for handwriting and gesture tracking applications.

Abstract

Ambient air pollution and environmental exposures
impose a measurable but often invisible burden on community health, particularly in rapidly urbanising regions where populations face simultaneous exposures from traffic emissions, construction activities, agrochemical residues, and degraded water quality. Despite this burden, low-cost integrated monitoring
frameworks that combine real-time IoT-based air quality sensing with individual-level health data in demographically matched cohorts remain scarce in Indian settings. This pilot study deploys a calibrated low-cost IoT-based air quality monitoring
framework alongside portable water quality instruments and structured self-reported health surveys to assess rural-urban environmental health disparities in Telangana, India. Urban PM2.5 (mean 78.7 µg/m3
, peak 684 µg/m3 ) substantially exceeded
rural levels (mean 50.2 µg/m3, peak 117 µg/m3
), with 51.3% of urban monitoring hours surpassing the 60 µg/m3 acute-concern threshold; both sites exceeded the CPCB National Ambient Air Quality Standards (NAAQS) PM2.5 limit of 40 µg/m3 . Water quality parameters were broadly similar across sites, isolating
air and noise pollution as the dominant differential exposures. Urban residents, particularly women and children, reported higher burdens of recurrent respiratory infections, antibiotic use, hair loss, headaches, and premenstrual symptoms. Together,
these findings demonstrate that a low-cost IoT-driven monitoring framework can identify patterns consistent with pollution-linked health outcomes in matched communities, offering a scalable model for environmental health surveillance across rural-urban
India.

Abstract

Although topological mapping and navigation have been studied extensively, the specific role and downstream effect of loop closures in purely topological representations has received relatively little attention. Importantly, loop closure over topological maps is distinct from loop closure over globally referenced trajectories and metric maps. Building on recent denser topologies grounded in pixel-level, relative 3D geometry, we propose Pixel Loop which introduces loop closures directly in pixel space. Unlike sparse image-level edges or pose-graph corrections in SLAM, our pixel-level closures act as dense topological shortcuts that alter planning connectivity and cost propagation rather than merely aligning coordinates. This dense connectivity enables stable any-point-to-any-point navigation and produces costmaps that align accurately with geometric shortest paths. In particular, we showcase the distinct advantage of applying loop closures to fine-grained pixel topologies rather than image-level topologies. Across extensive simulated experiments, Pixel Loop achieves over 35% absolute improvement in both Success Rate and SPL compared to image-relative baselines, with the largest gains in scenarios requiring shortcut exploitation. Results are further validated through real-world mobile robot deployments, demonstrating that dense pixel-level loop closures provide a practical and robust foundation for topological visual navigation.

Abstract

Experience from repeated analogous situations can bias subsequent decision-making, yet it remains unclear whether such effects arise from prior experience itself or the valence of that experience.
We investigated whether prior gain or loss modulates
subsequent uncertain decisions compared to a no-experience control. Participants completed a manipulated Iowa Gambling Task (M-IGT), inducing predetermined gain or loss, followed by the standard IGT. Prior experience group showed altered exploratory behavior in the early phase, with the loss group exhibiting early risk aversion. Behavioral differences across control and prior experience groups converged later in the task. The 𝑃𝑉𝐿 − Δ model revealed that prior experience initially reduced outcome sensitivity. In the later blocks, the gain group showed reduced learning rate and higher outcome sensitivity, indicating strategy stabilization. Prior loss was associated with higher
choice consistency across all blocks. These findings suggest that prior experience transiently biases early exploration, while valence shapes decision consistency and learning flexibility.

Abstract

Semantic banks, WordNets, and other sense inventories play a crucial role in the training
and evaluation of many NLP applications, including machine translation and semantic
understanding tasks. However, for low-resource languages such as Sanskrit, comprehensive and well-structured sense inventories are often unavailable. This not only poses a
significant challenge for traditional NLP pipelines but also for leveraging state-of-the-art
large language models (LLMs), which still require a foundational, machine usable sense
inventory to support tasks involving meaning disambiguation and semantic consistency.
Our work is motivated by this gap. We focus on creating a machine usable sense inventory
for Sanskrit by using the Monier-Williams dictionary as a primary lexical resource. Our
goal in building such an inventory, is to minimize ambiguity to the maximum possible
extent so that the resulting senses are clear and suitable for computational models. To
achieve this, we make use of clustering algorithms to group semantically similar senses
into synsets and to reorganize the existing dictionary structure in a way that it better
reflects semantic relationships such as polysemy and synonymy.

Abstract

How should we evaluate generation systems that combine autoregressive (AR) and diffusion decoding? Westudythis question through Speculative Refinement (SpecRef), a training-free hybrid method that warm-starts a masked diffusion language model from an AR draft using entropy-guided selective masking. Evaluating SpecRef across six benchmarks (HumanEval, MBPP, GSM8K, BBH, ARC-Challenge, HellaSwag) with three distinct evaluation protocols (execution-based pass@1, exact-match, loglikelihood scoring), we surface several findings relevant beyond our specific system: (1) code benchmarks conflate structural discovery with logical correctness: providing a syntactic scaffold lifts accuracy from near zero to over 20% without changing the model, indicating that much of the baseline failure is structural; (2) a refinement tension phenomenon where multistage correction degrades already-correct tokens, exposing benchmark saturation ceilings invisible to single-model evaluation; (3) loglikelihood and generative evaluation produce different model rankings for the same model pair, suggesting they measure different capabilities; (4) standard Python post-processing silently breaks code evaluation for non-AR generators. These observations apply to any multistage or non-autoregressive generation pipeline and point toward more diagnostic evaluation practices. Codes are here.

Abstract

Verbal humor involves reasoning through complex conversational contexts. Although LLMs have achieved strong performance on English humor datasets, their ability to interpret humor in Hindi remains unexplored. In this paper, we evaluate Hindi humor for which we extract dialogues from humorous video clips. We use a pipeline that transforms video content into detailed textual streams, including dialogue transcripts and scene descriptions, allowing reasoning over inputs exceeding 2,000 words. We test various LLMs, from efficient edge models (Qwen-2.5-7B, Qwen-3-7B, Gemma-3-27B) to Indic-focused models (Sarvam-M-24B) and large frontier models (Llama-3.1-70B, Gemini-2.0-Flash). Our findings show a concave performance pattern in long-context understanding, with reasoning quality peaking at moderate lengths (250-750 words) and declining at higher context lengths. We also show that standard metrics overstate pragmatic competence. While increasing model size generally improves performance, we also observe distinct failures in smaller LLMs due to instructional and linguistic issues, necessitating diversity metrics to capture hallucinations. Smaller, Hindi-focused models can compete with much larger generalist models. Importantly, our evaluation reveals that conversational humor is a challenge for even specialized models, making HinS a valuable benchmark for advancing research in Hindi Long-Context Humor Reasoning.

Abstract

Text-to-speech systems are improving fast, but measuring how natural they sound still requires expensive human listening tests; existing automatic methods struggle to generalise well across different datasets, so we present QMOS, a MOS prediction framework that extracts hierarchical speech quality features from a frozen Qwen2-Audio large audio language model and combines them with layer-weighted WavLM SSL representations through a learned cross-attention fusion, capturing both high-level semantic naturalness and low-level acoustic distortions in a unified model, and on two standard benchmarks, SOMOS and BVCC, QMOS achieves competitive system-level SRCC of 0.923 and 0.921 respectively while using no system-ID conditioning or listener embeddings, with cross-domain evaluation yielding a competitive SRCC of 0.702, suggesting the learned representations generalise across acoustic domains.