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Systematic FAIRness Assessment of Open Voice Biomarker Datasets for Mental Health and Neurodegenerative Diseases
Ishaan Mahapatra, Nihar R. Mahapatra
https://arxiv.org/abs/2508.14089

Systematic FAIRness Assessment of Open Voice Biomarker Datasets for Mental Health and Neurodegenerative Diseases
Voice biomarkers--human-generated acoustic signals such as speech, coughing, and breathing--are promising tools for scalable, non-invasive detection and monitoring of mental health and neurodegenerative diseases. Yet, their clinical adoption remains constrained by inconsistent quality and limited usability of publicly available datasets. To address this gap, we present the first systematic FAIR (Findable, Accessible, Interoperable, Reusable) evaluation of 27 publicly available voice biomarker d…

Crosslisted article(s) found for eess.AS. https://arxiv.org/list/eess.AS/new
[1/1]:
- Systematic FAIRness Assessment of Open Voice Biomarker Datasets for Mental Health and Neurodegene...
Ishaan Mahapatra, Nihar R. Mahapatra

A GEANT4-Based Simulation of Directional Neutron Detectors Using Liquid Scintillators and Boron Carbide Moderators
J. -H. Chen, M. Mirzakhani, R. Mahapatra, S. Sahoo
https://arxiv.org/abs/2507.14438

A GEANT4-Based Simulation of Directional Neutron Detectors Using Liquid Scintillators and Boron Carbide Moderators
We present a simulation-based study of a compact directional neutron detector composed of liquid scintillator, Cesium Iodide, with boron carbide (B4C) moderation, and silicon photomultipliers (SiPMs). Using GEANT4, we explored multiple detector geometries and material configurations, finding neutron detection efficiencies ranging from approximately 10% to 30%. To evaluate directionality, spatial energy distributions were analyzed and used to train a machine learning classifier, which achieved 1…

Novel Quantum Spin Liquid states in the $S = {\frac{1}{2}}$ three-dimensional compound Y$_{3}$Cu$_{2}$Sb$_{3}$O$_{14}$
Saikat Nandi, Rounak Das, Sagar Mahapatra, J\"org Sichelschmidt, M. Hemmida, H. -A. Krug von Nidda, Marlis Schuller, N. B\"uttgen, John Wilkinson, M. P. Saravanan, Indra Dasgupta, A. V. Mahajan
https://arxiv.org/…

Novel Quantum Spin Liquid states in the $S = {\frac{1}{2}}$ three-dimensional compound Y$_{3}$Cu$_{2}$Sb$_{3}$O$_{14}$
The three-dimensional $S = 1/2$ system Y$_{3}$Cu$_{2}$Sb$_{3}$O$_{14}$ consists of two inequivalent Cu$^{2+}$ ions, each forming edge shared triangular lattices. Our magnetic susceptibility $χ(T)=M/H$, specific heat $C_p(T)$, $^{89}$Y nuclear magnetic resonance (NMR), muon spin relaxation ($μ$SR), and electron spin resonance (ESR) measurements on this system confirm the absence of any long-range magnetic ordering and the persistence of spin dynamics down to 0.077 K. From $^{89}$Y NMR we find …

@… ウチのは要望があってmivatter.comから輸入してます。
Fedibirdはとりわけ内容が細かいですが、Misskeyもライセンステキストがついているので、誰が作ってどんなライセンスになっているか調べられることが多いです。
気に入ったり気になったりしている絵文字は、是非、詳細情報を覗いてみてください！

WANify: Gauging and Balancing Runtime WAN Bandwidth for Geo-distributed Data Analytics
Anshuman Das Mohapatra, Kwangsung Oh
https://arxiv.org/abs/2508.12961 https://

WANify: Gauging and Balancing Runtime WAN Bandwidth for Geo-distributed Data Analytics
Accurate wide area network (WAN) bandwidth (BW) is essential for geo-distributed data analytics (GDA) systems to make optimal decisions such as data and task placement to improve performance. Existing GDA systems, however, measure WAN BW statically and independently between data centers (DCs), while data transfer occurs dynamically and simultaneously among DCs during workload execution. Also, they use a single connection WAN BW that cannot capture actual WAN capacities between distant DCs. Such…

Unravelling Pentaquarks with Born--Oppenheimer effective theory
Nora Brambilla, Abhishek Mohapatra, Antonio Vairo
https://arxiv.org/abs/2508.13050 https://…

Unravelling Pentaquarks with Born--Oppenheimer effective theory
The hidden-charm pentaquark states $P_{c\bar{c}}\left(4312\right)^+$, $P_{c\bar{c}}\left(4380\right)^+$, $P_{c\bar{c}}\left(4440\right)^+$, and $P_{c\bar{c}}\left(4457\right)^+$, all with isospin $I = 1/2$, were discovered by the LHCb collaboration in the decay process $Λ_b^0 \to J/ψp K^-$. Although their quantum numbers remain undetermined, these states have generated significant theoretical interest. We analyze their spectrum and decay patterns-including those of their spin partners-within …

Scaling Wideband Massive MIMO Radar via Beamspace Dimension Reduction
Oveys Delafrooz Noroozi, Jiyoon Han, Wei Tang, Zhengya Zhang, Upamanyu Madhow
https://arxiv.org/abs/2508.11790

Scaling Wideband Massive MIMO Radar via Beamspace Dimension Reduction
We present an architecture for scaling digital beamforming for wideband massive MIMO radar. Conventional spatial processing becomes computationally prohibitive as array size grows; for example, the computational complexity of MVDR beamforming scales as O(N^3) for an N-element array. In this paper, we show that energy concentration in beamspace provides the basis for drastic complexity reduction, with array scaling governed by the O(NlogN) complexity of the spatial FFT used for beamspace transfo…

Scale Up Analysis of Inductively Heated Metamaterial Reactors
Chenghao Wan, Conner Cremers, Ariana B. H\"ofelmann, Zhennan Ru, Calvin H. Lin, Kesha N. Tamakuwala, Dolly Mantle, Pinak Mohapatra, Juan Rivas-Davila, Matthew W. Kanan, Jonathan A. Fan
https://arxiv.org/abs/2509.13719

Scale Up Analysis of Inductively Heated Metamaterial Reactors
Inductively heated metamaterial reactors, which utilize an open cell lattice baffle structure as a heating susceptor for magnetic induction, are promising candidates for scaled electrified thermochemical reactor operation due to their ability to support volumetric heating profiles and enhanced heat transfer properties. In this work, we present a systematic scale up analysis of inductive metamaterial reactors where we utilize a combination of analytic modeling, numerical simulations, and experim…

MEGAN: Mixture of Experts for Robust Uncertainty Estimation in Endoscopy Videos
Damola Agbelese, Krishna Chaitanya, Pushpak Pati, Chaitanya Parmar, Pooya Mobadersany, Shreyas Fadnavis, Lindsey Surace, Shadi Yarandi, Louis R. Ghanem, Molly Lucas, Tommaso Mansi, Oana Gabriela Cula, Pablo F. Damasceno, Kristopher Standish
https://arxiv.org/ab…

MEGAN: Mixture of Experts for Robust Uncertainty Estimation in Endoscopy Videos
Reliable uncertainty quantification (UQ) is essential in medical AI. Evidential Deep Learning (EDL) offers a computationally efficient way to quantify model uncertainty alongside predictions, unlike traditional methods such as Monte Carlo (MC) Dropout and Deep Ensembles (DE). However, all these methods often rely on a single expert's annotations as ground truth for model training, overlooking the inter-rater variability in healthcare. To address this issue, we propose MEGAN, a Multi-Expert Gati…