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Pushpendra Singh

Publications and source records attributed to Pushpendra Singh.

At least 19 recordsLinked to original sources
arXiv2026

Spatial Feature-wise Linear Modulation (SpFiLM) for Contrast Agent-Aware Brain Parcellation

Most automated brain parcellation tools are developed and validated on T1-weighted (T1w) MRI. Yet, some clinical workflows for which parcellation is relevant only use contrast-enhanced T1w (T1ce) MRI, on which T1w-trained models are less accurate. We present a unified network that parcellates both pre- and post-contrast agent T1w MRI reliably, trained on a combination of the two with conditioning that spatially modulates its response differently for each. Feature-wise Linear Modulation (FiLM) is a known approach for input-based modulation in networks. It applies a per-channel scale and shift uniformly across the input. However, the appearance change between pre- and post-contrast varies locally across the brain, making FiLM suboptimal for our use case. In this work, we introduce Spatial FiLM (SpFiLM), a conditioning layer whose modulation varies spatially, assembling a voxel-wise scale and shift from image-derived spatial patterns. Using a cohort of 134 patients with paired T1w and T1ce MRI parcellated into 106 classes, the addition of SpFiLM layers in a UNet increased the mean Dice on the test set of 25 patients from 80.2% to 84.1%, a 4.9% relative improvement. Adding SpFiLM layers led to the best performance on both pre- and post-contrast MRI, even when controlling for network parameter counts.

eess.IV↗
arXiv2026

Scales, Reflections, and Conversations: A Multi-Modal Approach to Emotion Annotation

Mental health concerns are increasing worldwide, highlighting the need for interventions that support everyday emotional well being. Prior work has demonstrated the potential of wearable and mobile technologies to deliver data driven interventions. However, developing effective data-driven systems requires access to emotion data that captures individuals' emotional variability and change in everyday contexts. Existing approaches to data collection largely rely on frequent, prescheduled prompts and predefined scales or questionnaires. These methods often fail to account for participants' availability, agency, or the complexity of their emotional experiences, resulting in shallow, context poor data. In this paper, we present a feasibility study of a participant centric, multimodal emotion-annotation application designed around users' emotional intensity and availability. Our findings show how multimodal emotion logging can shape participants' experiences and data logging behaviors, and demonstrate its potential to support the collection of richer, more nuanced emotion data.

cs.HC↗
arXiv2026

A note on idempotents in quandle rings

It was conjectured that the nonzero idempotents of the integral quandle ring of a finite latin quandle are trivial. We prove this holds for latin dihedral quandles. Furthermore, we discuss counterexamples against this conjecture.

math.RA↗
arXiv2026

On Codes in (Generalized) Symmetric Groups

In this article, we show that, for the symmetric group $S_n$, the Young subgroup $S_{\lambda}$, with $l(\lambda)\geq 3$, is not a code with respect to a conjugacy class of $S_n$. This provides a partial answer to \cite[Problem 4.1]{FL26}. We then provide a characterization of conjugacy classes $X_i$, such that subgroups $S_{(n-2,1,1)}$, $S_{(n-3,2,1)}$, and $Y_k$ for $k\leq 3$ are codes for $X_1 \cup X_2$. Furthermore, we describe some codes in the finite Coxeter groups of type $B_n$, $C_n$, $D_n$, and in the generalized symmetric group $C_m \wr S_n$.

math.GR↗
arXiv2026

FEEL: Quantifying Heterogeneity in Physiological Signals for Generalizable Emotion Recognition

Emotion recognition from physiological signals has substantial potential for applications in mental health and emotion-aware systems. However, the lack of standardized, large-scale evaluations across heterogeneous datasets limits progress and model generalization. We introduce FEEL, the first large-scale benchmarking study of emotion recognition using electrodermal activity (EDA) and photoplethysmography (PPG) signals across 19 publicly available datasets. We evaluate 16 architectures spanning traditional machine learning, deep learning, and self-supervised pretraining approaches, structured into four representative modeling paradigms. Our study includes both within-dataset and cross-dataset evaluations, analyzing generalization across variations in experimental settings, device types, and labeling strategies. Our results showed that fine-tuned contrastive signal-language pretraining (CLSP) models (71/114) achieve the highest F1 across arousal and valence classification tasks, while simpler models like Random Forests, LDA, and MLP remain competitive (36/114). Models leveraging handcrafted features (107/114) consistently outperform those trained on raw signal segments, underscoring the value of domain knowledge in low-resource, noisy settings. Further cross-dataset analyses reveal that models trained on real-life setting data generalize well to lab (F1 = 0.79) and constraint-based settings (F1 = 0.78). Similarly, models trained on expert-annotated data transfer effectively to stimulus-labeled (F1 = 0.72) and self-reported datasets (F1 = 0.76). Moreover, models trained on lab-based devices also demonstrated high transferability to both custom wearable devices (F1 = 0.81) and the Empatica E4 (F1 = 0.73), underscoring the influence of heterogeneity. More information about FEEL can be found on our website https://alchemy18.github.io/FEEL_Benchmark/.

cs.HC↗
arXiv2025

JEEVHITAA -- An HCAI Ecosystem to Support Collective Care

Current mobile health platforms are predominantly individual-centric and lack the support for coordinated, auditable multi-actor workflows. However, in many settings worldwide, health decisions are enacted through multi-actor coordination rather than individual users. We present JEEVHITAA, a cross-platform mobile system enabling role-aware sharing and verifiable information flows within permissioned care circles. JEEVHITAA ingests platform and device data, builds layered profiles from sensors and tiered onboarding, and enforces fine-grained, time-bounded access control across care graphs. Data stays secure both within the application and the cloud. Integrated retrieval-augmented Large Language Models produce structured, role-targeted summaries and action plans, offer evidence-grounded verification with provenance and confidence scores, and support advanced insights on reports. We describe the architecture, connector abstractions, and security primitives, and report robustness evaluations using synthetic, ontology-driven data and findings from a feasibility study with real-life care circles across 9-14 weeks. We outline plans for larger multi-site evaluations focusing on operational alignment, longitudinal trust & literacy impact, and relational friction & efforts to sink into the daily infrastructure.

cs.HC↗
arXiv2025

Leveraging Familiarity with Television to Enrich Older Adults' Engagement and Wellbeing: A Feasibility Study Using Video Probes

The shift away from multigenerational families to nuclear families in India has created a growing need to support older adults living independently. While technology can help address this gap, older adults' limited exposure to newer technology restricts the adoption of such solutions. However, they remain comfortable with long-standing technologies like television (TV). This study explores their daily technology usage and challenges, aiming to determine whether TV can be leveraged to improve their quality of life. We examined how TV systems could be enhanced to assist older adults with tasks such as staying connected, receiving health alerts, and ensuring security. Using a participatory design approach, we developed video probes using the prototype of the TV-based application and interviewed 27 older adults to assess its acceptance and usability. Our findings demonstrate older adults' strong interest in a TV-based solution and a preference for familiar technology to support security, independence, and wellbeing.

cs.HC↗
arXiv2025

Unpacking "Personal" Health Informatics for Proactive Collective Care

Care is primarily a collective phenomenon, with a practice that involves sharing health and wellbeing information within a trusted "care circle" of family members and companions for sensemaking, interpretation, decision-making, and follow-through. However, current digital health tools and information systems are designed for individuals and primarily intended for Personal Health Informatics (PHI). This mismatch between collective practice and individualistic design creates new challenges for the proactive use of such systems in care settings and limits adoption, sustained engagement, and meaningful use. To examine how people practice collective care and how (if) they perceive, adopt, and integrate PHI systems for proactive care, we conducted a sequential mixed-methods study. Through an initial survey (n=87) and semi-structured interviews (n=22), we found that their practices involve collectively understanding, analyzing, and sensemaking health information. However, we also found that their use of existing systems to support such practices is constrained by factors at personal, relational, technological, and structural levels that evolve over time. To explore redesigning PHI toward "Collective Health Informatics", we conducted stakeholder-specific interviews (n=12), a follow-up survey (n=116), and co-design workshops (n=6) to understand the dynamics required for collective settings while retaining agency. Using a design probe evaluation (n=38), we refine a design vision for coordinated, trustworthy action across such care relationships. Our findings motivate CC-Proact, an operational map that translates ecological influences into three design levers: Agency, Elicitation, and Engagement. Using this map, our work empirically examines collective care practices and offers ten design recommendations for building responsible systems that proactively support collective care.

cs.HC↗
arXiv2025

AnnoSense: A Framework for Physiological Emotion Data Collection in Everyday Settings for AI

Emotional and mental well-being are vital components of quality of life, and with the rise of smart devices like smartphones, wearables, and artificial intelligence (AI), new opportunities for monitoring emotions in everyday settings have emerged. However, for AI algorithms to be effective, they require high-quality data and accurate annotations. As the focus shifts towards collecting emotion data in real-world environments to capture more authentic emotional experiences, the process of gathering emotion annotations has become increasingly complex. This work explores the challenges of everyday emotion data collection from the perspectives of key stakeholders. We collected 75 survey responses, performed 32 interviews with the public, and 3 focus group discussions (FGDs) with 12 mental health professionals. The insights gained from a total of 119 stakeholders informed the development of our framework, AnnoSense, designed to support everyday emotion data collection for AI. This framework was then evaluated by 25 emotion AI experts for its clarity, usefulness, and adaptability. Lastly, we discuss the potential next steps and implications of AnnoSense for future research in emotion AI, highlighting its potential to enhance the collection and analysis of emotion data in real-world contexts.

cs.HC↗
arXiv2025

Translating Emotions to Annotations -- A Participant Perspective of Physiological Emotion Data Collection

Physiological signals hold immense potential for ubiquitous emotion monitoring, presenting numerous applications in emotion recognition. However, harnessing this potential is hindered by significant challenges, particularly in the collection of annotations that align with physiological changes since the process hinges heavily on human participants. In this work, we set out to study human participant perspectives in the emotion data collection procedure. We conducted a lab-based emotion data collection study with 37 participants using 360 degree virtual reality video stimulus followed by semi-structured interviews with the study participants. Our findings presented that intrinsic factors like participant perception, experiment design nuances, and experiment setup suitability impact their emotional response and annotation within lab settings. Drawing from our findings and prior research, we propose recommendations for incorporating participant context into annotations and emphasizing participant-centric experiment designs. Furthermore, we explore current emotion data collection practices followed by AI practitioners and offer insights for future contributions leveraging physiological emotion data.

cs.HC↗
arXiv2025

Classification of simple quandles of small order

In this article, we define quasiprimitive quandles and describe them with the help of quasiprimitive permutation groups. As a consequence, we enumerate finite non-affine simple quandles up to order $4096$.

math.GR↗
arXiv2024

Can we say a cat is a cat? Understanding the challenges in annotating physiological signal-based emotion data

Artificial Intelligence (AI) algorithms, trained on emotion data extracted from physiological signals, provide a promising approach to monitoring emotions, affect, and mental well-being. However, the field encounters challenges because there is a lack of effective methods for collecting high-quality data in everyday settings that genuinely reflect changes in emotion or affect. This paper presents a position discussion on the current technique of annotating physiological signal-based emotion data. Our discourse underscores the importance of adopting a nuanced understanding of annotation processes, paving the way for a more insightful exploration of the intricate relationship between physiological signals and human emotions.

cs.HC↗
arXiv2024

Tensor product and quandle rings of connected quandles of prime order

Let $\mathbb{C}$ be field of complex numbers and $X$ be a connected quandle of prime order. We study the regular representation of $X$ by describing the quandle ring $\mathbb{C}[X]$ as direct sum of right simple ideals. We provide description of tensor product of connected quandles of prime order. We further discuss multiplicity freeness of quandle ring decomposition for connected quandles of order $\leq 47$ and prove that $\mathbb{C}[X]$ decomposes multiplicity free for affine connected quandle $X$.

math.GR↗
arXiv2023

Decomposition of quandle rings of Takasaki quandles

Let $K = \mathbb{R}$ or $\mathbb{C}$ and $T_{n}$ denote the Takasaki quandle of order $n$. In this article we provide decomposition of quandle ring $K[T_n]$ as direct sum of right simple ideals. This decomposition is equivalent to decomposition of regular representation \cite{EM18} of Takasaki quandles.

math.GR↗
arXiv2022

Decomposition of quandle rings of dihedral quandles

Let $K = \mathbb R$ or $\mathbb C$ and $\mathcal R_n$ be the dihedral quandle of order $n$: In this article, we give decomposition of the quandle ring $K[\mathcal R_n]$ into indecomposable right $K[\mathcal R_n]$-modules for all even $n \in \mathbb N$. It follows that the decomposition of $K[\mathcal R_n]$ given in [EFT19, Prop. 4.18(2)] is valid only in the case when $n$ is not divisible by $4.$

math.GR↗
arXiv2021

The Generalized Fourier Transform: A Unified Framework for the Fourier, Laplace, Mellin and $Z$ Transforms

This paper introduces Generalized Fourier transform (GFT) that is an extension or the generalization of the Fourier transform (FT). The Unilateral Laplace transform (LT) is observed to be the special case of GFT. GFT, as proposed in this work, contributes significantly to the scholarly literature. There are many salient contribution of this work. Firstly, GFT is applicable to a much larger class of signals, some of which cannot be analyzed with FT and LT. For example, we have shown the applicability of GFT on the polynomially decaying functions and super exponentials. Secondly, we demonstrate the efficacy of GFT in solving the initial value problems (IVPs). Thirdly, the generalization presented for FT is extended for other integral transforms with examples shown for wavelet transform and cosine transform. Likewise, generalized Gamma function is also presented. One interesting application of GFT is the computation of generalized moments, for the otherwise non-finite moments, of any random variable such as the Cauchy random variable. Fourthly, we introduce Fourier scale transform (FST) that utilizes GFT with the topological isomorphism of an exponential map. Lastly, we propose Generalized Discrete-Time Fourier transform (GDTFT). The DTFT and unilateral $z$-transform are shown to be the special cases of the proposed GDTFT. The properties of GFT and GDTFT have also been discussed.

eess.SP↗
arXiv2018

Studies on Generalized Fourier Representations and Phase Transforms

Fourier representation (FR) is an indispensable mathematical formulation for modeling and analysis of physical phenomenon, engineering systems and signals in numerous applications. In this study, we present the generalized Fourier representation (GFR) that is completely based on the FR of a signal, and introduce the phase transform (PT) which is a special case of the GFR and a true generalization of the Hilbert transform. We derive the PT kernel to obtain any constant phase shift, discuss the various properties of the PT, and demonstrate that (i) a constant phase shift in a signal corresponds to variable time-delays in all harmonics, (ii) to obtain a constant time-delay in a signal, one need to provide variable phase shift in all harmonics, (iii) a constant phase shift is same as the constant time-delay only for single frequency sinusoid. The time derivative and time integral, including fractional order, of a signal can be obtained using the GFR. We propose to use discrete cosine transform (DCT) based implementation to avoid end artifacts due to discontinuities present in both end of the signal. We introduce fractional delay of a discrete time signal using the FR, and present the fast Fourier transform (FFT) implementation of all the above proposed representations. Using the analytic wavelet transform (AWT), we propose wavelet phase transform (WPT) to obtain a desired phase-shift in a signal under-analysis, and propose the two representations of wavelet quadrature transform (WQT) which is special case of the WPT where phase-shift is $\pi/2$ radians.

eess.SP↗