Abstract

Background Heggli et al. (2021) noted that circadian rhythms and individual traits influence diurnal patterns in music preference. Depressive individuals often experience more negative moods in the evening, known as the evening-worsening effect (Rusting & Larsen, 1998). As music is often used for emotion focused-coping during such mood states (Stewart et al., 2019), depressive individuals’ music consumption in the evening are unclear. Aims This study explores the evening-worsening effect in music listening, examining if depressive individuals prefer low-valence, low-arousal music in the evening. Methods A survey of 290 Indians (M=20.35yrs, SD=2.08, 211 males) collected Kessler Psychological Distress scores (K10) and Healthy-Unhealthy music scale (HUMS) scores along with Spotify listening histories over one year. Additional measures like trait empathy and life satisfaction were collected but not analysed. Participants were classified as No-Risk (K10<20, n=114, 82 males) or At-Risk (K10≥29, n=81, 52 males) of depression (Andrews & Slade, 2001). Spotify valence and energy features for top 100-250 songs over the past 4-8 weeks from the time they filled in the survey were analyzed alongside timestamps. K-Means clustering divided the day into early hours, morning, afternoon and evening, and group differences in hourly valence and energy averages were assessed using two-way ANOVA and t-tests. Results At-Risk had significantly higher Unhealthy scores than No-Risk (t=8.15, p<0.001). Significant differences in valence and energy were found between At-Risk and No-Risk only in the afternoon (valence: t=-9.47, energy: t=-6.53) and evening (valence: t=-2.98, energy: t=-11.56)(all p≤ 0.05). Two-way ANOVA showed significant main effects of time, group, and their interaction for valence (time: F(3, 40)=22.86; group: F(1, 40)=12.36; interaction: F(3, 40)=6.65) and energy (time: F(3, 40)=23.22; group: F(1, 40)=98.73; interaction: F(3, 40)=13.57)(all p<0.001), consistent across the top 100-250 songs and the past 4-8 weeks of listening histories. Conclusions This study confirms diurnal patterns in music preferences, with At-Risk individuals favouring low-valence and low-energy music, especially in the afternoon and evening. These results align with Rusting and Larsen’s (1998) evening-worsening effect, linking depressive tendencies to heightened negative moods in the evening. The findings suggest potential maladaptive listening behaviors, needing further research.

Abstract

Most research on the presence and activities of Muslim intellectuals in interwar Europe deals with men, with Muslim women strikingly underrepresented in such scholarship. This article addresses this gap by dwelling on the activities of Haidarābādī reformer and writer Ṣuġhrā Humāyūñ Mirzā (1884–1958) in Berlin in 1924 and, in the process, offering a glimpse into the role and work of women activists, educators, social workers, and intellectuals working in Germany at the time. Her own thought in this regard makes for an important and fascinating study. Furthermore, the speeches she was invited to deliver by different associations contain striking historical commentary and communicate her thoughts and experiences as a social reformer and educator in Europe. In examining this account, this paper also demonstrates how Ṣuġhrā Begam’s insatiable personal curiosity and relentless sense of duty critically challenge and undermine the enforced, unhistorical invisibility of Muslim women travellers in the hegemonic Eurocentric discourse of travel writing.

Abstract

We develop randomized quantum algorithms to simulate quantum collision models, also known as repeated interaction schemes, which provide a rich framework to model various open-system dynamics. The underlying technique involves composing time evolutions of the total (system, bath, and interaction) Hamiltonian and intermittent tracing out the environment degrees of freedom. This results in a unified framework where any near-term Hamiltonian simulation algorithm can be incorporated to implement an arbitrary number of such collisions on early fault-tolerant quantum computers: we do not assume access to specialized oracles such as block encodings and minimize the number of ancilla qubits needed. In particular, using the correspondence between Lindbladian evolution and completely positive trace-preserving maps arising out of memoryless collisions, we provide an end-to-end quantum algorithm for simulating Lindbladian dynamics. For a system of 𝑛-qubits, we exhaustively compare the circuit depth needed to estimate the expectation value of an observable with respect to the reduced state of the system after time 𝑡 while employing different near-term Hamiltonian simulation techniques, requiring at most 𝑛+2 qubits in all. We compare the cnot gate counts of the various approaches for estimating the transverse field magnetization of a 10-qubit XX-Heisenberg spin chain under amplitude damping. Finally, we also develop a framework to efficiently simulate an arbitrary number of memory-retaining collisions, i.e., where environments interact, leading to non-Markovian dynamics. Overall, our methods can leverage quantum collision models for both Markovian and non-Markovian dynamics on early fault-tolerant quantum computers, shedding light on the advantages and limitations of simulating open systems dynamics using this framework.

Abstract

Stable aerial manipulation during dynamic tasks such as object catching, perching, or contact with rigid surfaces necessarily requires compliant behavior, which is often achieved via impedance control. Successful manipulation depends on how effectively the impedance control can tackle the unavoidable coupling forces between the aerial vehicle and the manipulator. However, the existing impedance controllers for aerial manipulator either ignore these coupling forces (in partitioned system compliance methods) or require their precise knowledge (in complete system compliance methods). Unfortunately, such forces are very difficult to model, if at all possible. To solve this long-standing control challenge, we introduce an impedance controller for aerial manipulator which does not rely on a priori knowledge of the system dynamics and of the coupling forces. The impedance control design can address unknown coupling forces, along with system parametric uncertainties, via suitably designed adaptive laws. The closed-loop system stability is proved analytically and experimental results with a payload-catching scenario demonstrate significant improvements in overall stability and tracking over the state-of-the-art impedance controllers using either partitioned or complete system compliance.

Abstract

Do LLMs genuinely incorporate external definitions, or do they primarily rely on their parametric knowledge? To address these questions, we conduct controlleD experiments across multiple explanation benchmark datasets (general and domain-specific) and label definition conditions, including expert-curated, LLM- generated, perturbed, and swapped definitions. Our results reveal that while explicit label definitions can enhance accuracy and explainability, their integration into an LLM’s task-solvinG processes is neither guaranteed nor consistent, suggesting reliance on internalized representations in many cases. Models often default to their internal representations, particularly in general tasks, whereas domain-specific tasks benefit more from explicit definitions. These findings underscore the need for a deeper understanding of how LLMs process external knowledge alongside their pre-existing capabilities.

Abstract

Knowledge Graph Foundation Models (KGFMs) have shown promise in enabling zero-shot reasoning over unseen graphs by learning transferable patterns. However, most existing KGFMs rely solely on graph structure, overlooking the rich semantic signals encoded in textual attributes. We introduce SEMMA, a dual-module KGFM that systematically integrates transferable textual semantics alongside structure. SEMMA leverages Large Language Models (LLMs) to enrich relation identifiers, generating semantic embeddings that subsequently form a textual relation graph, which is fused with the structural component. Across 54 diverse KGs, SEMMA outperforms purely structural baselines like ULTRA in fully inductive link prediction. Crucially, we show that in more challenging generalization settings, where the test-time relation vocabulary is entirely unseen, structural methods collapse while SEMMA is 2x more effective. Our findings demonstrate that textual semantics are critical for generalization in settings where structure alone fails, highlighting the need for foundation models that unify structural and linguistic signals in knowledge reasoning.

Abstract

This paper presents an 18.87–24.19 GHz dual-core, dual-mode voltage-controlled oscillator (VCO) using a multi-tap (MT) inductor-based 2-port resonator with capacitive coupling via a mode-switching block. The 2-port resonator introduces a gate-to-drain phase shift, suppressing flicker phase noise (PN), while the high-quality factor of the tank minimizes thermal PN. The mode-switching block enables dual resonance modes—high-frequency band (HB) and low-frequency band (LB), achieving a wide frequency tuning range (FTR) and low PN in both the 1/f^2 and 1/f^3 regions. The proposed VCO is implemented in 65 nm CMOS, and post-layout results show a PN of –68.7 dBc/Hz at a 10 kHz offset and –118.2 dBc/Hz at a 1 MHz offset from an 18.87 GHz carrier frequency, with a power consumption of 25.65 mW and an area of 0.154 mm^2. The peak figure of merit (FoM) is 189.6 dBc/Hz, and the maximum 1/f^3 noise corner frequency is 347 kHz across the FTR.

Abstract

A conventional push–push frequency doubler (FD) is simple to implement but suffers from limited total efficiency ($eta_{text{total}}$) at millimeter-wave (mm-Wave) frequencies due to negative feedback introduced by the gate–drain capacitance ($C_{mathrm{gd}}$). To overcome this limitation, a 42.5-58 GHz mm-Wave FD is proposed, utilizing cross-coupled gate-to-source feedback. In the proposed topology, the push-push NMOS frequency doubler employs cross-coupled gate-to-source feedback, which generates a second-harmonic voltage at the gate that is in phase with the drain’s second-harmonic current. This feedback effectively mitigates the inherent feedback caused by $C_{mathrm{gd}}$, and doubles the gate-to-source voltage of the fundamental harmonic, $V_{gs}[f_{0}]$. As a result, the amplitude of the drain $2f_{0}$ current generated by the nonlinear second harmonic transconductance $g_{m2,nonlinear}$ of the NMOS increases, leading to enhanced drain efficiency ($eta_{mathrm{D}}$) and $eta_{text{total}}$. The proposed FD is designed in a 65 nm CMOS process. Post-layout simulations demonstrate a peak $eta_{mathrm{D}}$ of 28.39%, $eta_{text{total}}$ of 13.7%, and a peak $2f_0$ output power of 6.27 dBm at 47.5 GHz, with a 12 dBm local oscillator (LO) input and 14.92 mW DC power consumption. The doubler achieves a 3 dB bandwidth of 42.5–58 GHz $(30.85%)$ and an efficiency bandwidth 43.75–54.66 GHz $(20.4%)$ where $eta_{text{total}} geq 10%$.

Abstract

Respiratory diseases such as asthma, bronchitis, and pneumonia remain a leading cause of mortality worldwide, significantly affecting both individual health and socioeconomic conditions. Early and accurate diagnosis is essential to mitigate these effects, yet traditional diagnostic techniques such as auscultation are subjective and rely on the expertise of trained physicians. To address these challenges and improve diagnostic accessibility, this work proposes a fully portable and low-cost system for the automated detection and classification of respiratory diseases. Lung sounds are captured using a microphone interfaced with a microcontroller. These recordings are then transmitted to an Edge hardware platform, where a Convolutional Neural Network (CNN) model processes Mel spectrogram representations of the audio for classification. The system leverages the publicly available HF Lung dataset for training and evaluation. The proposed system achieved a classification accuracy of 80.5%, with a sensitivity of 95.65% and a specificity of 98.80%. This portable approach demonstrates the potential for real-time, AI-powered respiratory diagnostics in remote or resource-limited settings, bridging the gap between clinical expertise and everyday health monitoring.

Abstract

Frequency Modulated Continuous Wave (FMCW) radars, known for their millimeter-level accuracy, are increasingly used in healthcare for non-contact monitoring of vital signs like Heart Rate (HR) and Breath Rate (BR). HR and BR are estimated using frequency domain analysis of the extracted respiration and cardiac activity from Radar Data. The focus of our work is to extract the waveforms of chest vibrations due to respiration and cardiac activity, for which higher time resolution is required. This work utilizes a signal processing technique known as Zero Frequency Filtering (ZFF) on the FMCW Radar Data. ZFF is known to have the best time resolution. The processing pipeline is implemented on the PYNQ-ZU platform to ensure increased portability. Experimental results show that the proposed system achieves an accuracy of over 95.26% for HR estimation and over 98.47% for BR estimation, with a latency of less than 2 ms.