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Wearable sensor systems have demonstrated a great potential for real-time, objective monitoring of physiological health to support behavioral interventions. However, obtaining accurate labels in free-living environments remains difficult due to limited human supervision and the reliance on self-labeling by patients, making data collection and supervised learning particularly challenging. To address this issue, we introduce CUDLE (Cannabis Use Detection with Label Efficiency), a novel framework that leverages self-supervised learning with real-world wearable sensor data to tackle a pressing healthcare challenge: the automatic detection of cannabis consumption in free-living environments. CUDLE identifies cannabis consumption moments using sensor-derived data through a contrastive learning framework. It first learns robust representations via a self-supervised pretext task with data augmentation. These representations are then fine-tuned in a downstream task with a shallow classifier, enabling CUDLE to outperform traditional supervised methods, especially with limited labeled data. To evaluate our approach, we conducted a clinical study with 20 cannabis users, collecting over 500 hours of wearable sensor data alongside user-reported cannabis use moments through EMA (Ecological Momentary Assessment) methods. Our extensive analysis using the collected data shows that CUDLE achieves a higher accuracy of 73.4%, compared to 71.1% for the supervised approach, with the performance gap widening as the number of labels decreases. Notably, CUDLE not only surpasses the supervised model while using 75% less labels, but also reaches peak performance with far fewer subjects., Comment: 8 pages, 5 figures, 1 table

Sensor systems are constrained by design and finding top sensor channel(s) for a given computational task is an important but hard problem. We define an optimization framework and mathematically formulate the minimum-cost channel selection problem. We then propose two novel algorithms of varying scope and complexity to solve the optimization problem. Branch and bound channel selection finds a globally optimal channel subset and the greedy channel selection finds the best intermediate subset based on the value of a score function. Proposed channel selection algorithms are conditioned with performance as well as the cost of the channel subset. We evaluate both algorithms on two publicly available time series datasets of human activity recognition and mental task detection. Branch and bound channel selection achieved a cost saving of up to 94.8% and the greedy search reduced the cost by 89.6% while maintaining performance thresholds.

There is a pressing need for a transition from fossil fuel to renewable energy to meet the increasing energy demands and reduce greenhouse gas (GHG) emissions. The Himalayas possess substantial renewable energy potential that can be harnessed through hydropower projects due to its peculiar topographic characteristics and abundant water resources. However, the current exploitation rate is low owing to the predominance of run-of-river hydropower systems to support the power system. The utility-scale storage facility is crucial in the load scenario of an integrated power system to manage diurnal variation, peak demand, and penetration of intermittent energy sources. In this study, we first identify the potential of pumped storage hydropower across Nepal (a central Himalayan country) under multiple configurations by pairing lakes, hydropower projects, rivers, and available flat terrains. We then identify technically feasible pairs from those of potential locations. Infrastructural, environmental, operational, and other technical constraints govern the choice of feasible locations. We find the flat land-to-river configuration most promising than other configurations. Our results provide insight into the potential of pumped storage hydropower and are of practical importance in planning sustainable power systems in the Himalayas and beyond.

Stress detection and classification from wearable sensor data is an emerging area of research with significant implications for individuals' physical and mental health. In this work, we introduce a new dataset, ADARP, which contains physiological data and self-report outcomes collected in real-world ambulatory settings involving individuals diagnosed with alcohol use disorders. We describe the user study, present details of the dataset, establish the significant correlation between physiological data and self-reported outcomes, demonstrate stress classification, and make our dataset public to facilitate research.

In this paper classification of mental task-root Brain-Computer Interfaces (BCI) is being investigated, as those are a dominant area of investigations in BCI and are of utmost interest as these systems can be augmented life of people having severe disabilities. The BCI model's performance is primarily dependent on the size of the feature vector, which is obtained through multiple channels. In the case of mental task classification, the availability of training samples to features are minimal. Very often, feature selection is used to increase the ratio for the mental task classification by getting rid of irrelevant and superfluous features. This paper proposes an approach to select relevant and non-redundant spectral features for the mental task classification. This can be done by using four very known multivariate feature selection methods viz, Bhattacharya's Distance, Ratio of Scatter Matrices, Linear Regression and Minimum Redundancy & Maximum Relevance. This work also deals with a comparative analysis of multivariate and univariate feature selection for mental task classification. After applying the above-stated method, the findings demonstrate substantial improvements in the performance of the learning model for mental task classification. Moreover, the efficacy of the proposed approach is endorsed by carrying out a robust ranking algorithm and Friedman's statistical test for finding the best combinations and comparing different combinations of power spectral density and feature selection methods.

Stress detection and monitoring is an active area of research with important implications for the personal, professional, and social health of an individual. Current approaches for affective state classification use traditional machine learning algorithms with features computed from multiple sensor modalities. These methods are data-intensive and rely on hand-crafted features which impede the practical applicability of these sensor systems in daily lives. To overcome these shortcomings, we propose a novel Convolutional Neural Network (CNN) based stress detection and classification framework without any feature computation using data from only one sensor modality. Our method is competitive and outperforms current state-of-the-art techniques and achieves a classification accuracy of $92.85\%$ and an $f1$ score of $0.89$. Through our leave-one-subject-out analysis, we also show the importance of personalizing stress models., Comment: 4 pages, 4 figures, IEEE International Conference on Wearable and Implantable Body Sensor Networks

Machine learning is used for inference and decision making in wearable sensor systems. However, recent studies have found that machine learning algorithms are easily fooled by the addition of adversarial perturbations to their inputs. What is more interesting is that adversarial examples generated for one machine learning system is also effective against other systems. This property of adversarial examples is called transferability. In this work, we take the first stride in studying adversarial transferability in wearable sensor systems from the following perspectives: 1) transferability between machine learning systems, 2) transferability across subjects, 3) transferability across sensor body locations, and 4) transferability across datasets. We found strong untargeted transferability in most cases. Targeted attacks were less successful with success scores from $0\%$ to $80\%$. The transferability of adversarial examples depends on many factors such as the inclusion of data from all subjects, sensor body position, number of samples in the dataset, type of learning algorithm, and the distribution of source and target system dataset. The transferability of adversarial examples decreases sharply when the data distribution of the source and target system becomes more distinct. We also provide guidelines for the community for designing robust sensor systems., Comment: 31 pages

Single crystals of Frank-Kasper compounds RTM$_2$Al$_{20}$ (R = Eu, Gd and La; TM = V and Ti) were grown by self-flux method and their physical properties were investigated through magnetization ($M$), magnetic susceptibility ($\chi$), specific heat ($C_P$) and electrical resistivity ($\rho$) measurements. Powder x-ray diffraction studies and structural analysis showed that these compounds crystallize in the cubic crystal structure with the space group $Fd\overline{3}m$. The magnetic susceptibility for the compounds EuTi$_2$Al$_{20}$ and GdTi$_2$Al$_{20}$ showed a sudden jump below the N{\'e}el temperature $T_N$ indicative of plausible double magnetic transition. Specific heat ($C_P$) and electrical resistivity ($\rho$) measurements also confirm the first-order magnetic transition (FOMT) and possible double magnetic transitions. Temperature variation of heat capacity showed a sharp phase transition and huge $C_P$ value for the (Eu/Gd)Ti$_2$Al$_{20}$ compounds Full width at half-maximum (FWHM) $<$ 0.2 K) which is reminiscent of a first-order phase transition and a unique attribute among RTM$_2$Al$_{20}$ compounds. We observed clear anomaly between heating and cooling cycle in temperature-time relaxation curve for the compounds GdTi$_2$Al$_{20}$ (2.38 $K$) and EuTi$_2$Al$_{20}$ (3.2 $K$) which is indicating a thermal arrest due to the latent heat. The temperature variation of $S_{mag}$ for GdTi$_2$Al$_{20}$ saturates to a value $0.95R\ln8$ while the other magnetic systems exhibited still lower entropy saturation values in the high temperature limit. Resistivity measurements showed that all the samples behave as normal Fermi liquid type compounds and $\rho(T)$ due to electron-phonon scattering follows Bloch-Gr$\ddot{\textrm u}$neisen-Mott relation in the paramagnetic region., Comment: 13 figures, 3 Tables

The robust field-insensitive heavy fermion features in Sm$_3$Ru$_4$Ge$_{13}$ and the magnetic phase transition at $T_N \approx$ 5~K are studied using magnetization $M(T)$, specific heat $C_p(T)$, resistivity $\rho(T)$ and thermal conductivity $\kappa_T(T)$. The average crystal structure of Sm$_3$Ru$_4$Ge$_{13}$ conforms to the cubic space group $Pm\bar{3}n$ however, signatures of subtle structural distortions are obtained from the x ray data. The magnetic susceptibility, $\chi(T)$, follows a modified Curie-Weiss law indicating the presence of crystal fields of Sm$^{3+}$ and the significance of van Vleck terms. No sign of ferromagnetism is observed in $M(H)$ of Sm$_3$Ru$_4$Ge$_{13}$ which yields only 0.025~$\mu_\mathrm{B}$/f.u.-Sm at 2~K, 7~T. The Sommerfeld coefficient, $\gamma \approx$ 220~mJ/mol-Sm K$^2$, estimated from the analysis of low temperature specific heat suggests the formation of heavy quasi particles at low temperature. Though a ln$T$ dependence of $\rho(T)$ is observed till 60~K, the resistivity behavior is accounted for by assuming a two-band model for activated behavior of charge carriers. The field scans of resistivity, $\rho(H)$, below $T_N$ display significant nonlinearity while those above the $T_N$ are more metal-like. Low values of thermal conductivity, $\kappa_T(T)$, are observed in Sm$_3$Ru$_4$Ge$_{13}$ however, displaying an anomaly at $T_N$ which signifies magnetoelastic coupling. A fairly high value of Seebeck coefficient, $S \approx$ 40~$\mu$V/K is observed at 300~K. We identify Sm$_3$Ru$_4$Ge$_{13}$ as a low charge carrier density system with unusual field-insensitive heavy fermion features very similar to the filled skutterudites., Comment: 18 pages, 7 figures, Accepted to J. Alloys and Comp., 2016

The compound PrFe$_2$Al$_8$ possesses a three-dimensional network structure resulting from the packing of Al polyhedra centered at the transition metal element Fe and the rare earth Pr. Along the $c$-axis, Fe and Pr form {\em chains} which are separated from each other by the Al-network. In this paper, the magnetism and crystalline electric field effects in PrFe$_2$Al$_8$ are investigated through the analysis of magnetization and specific heat data. A magnetic phase transition in the Pr lattice is identified at $T^{Pr}_{N}\approx$ 4~K in dc magnetization and ac susceptibility data. At 2~K, the magnetization isotherm presents a ferromagnetic saturation, however, failing to reach full spin-only ferromagnetic moment of Pr$^{3+}$. Metamagnetic step-like low-field features are present in the magnetization curve at 2~K which is shown to shift upon field-cooling the material. Arrott plots centered around $T^{Pr}_{N}$ display "S"-like features suggestive of an inhomogeneous magnetic state. The magnetic entropy, $S_m$, estimated from specific heat outputs a value of $R$ ln(2) at $T_{N2}$ suggesting a doublet state for Pr$^{3+}$. The magnetic specific heat is modeled by using a 9-level Schottky equation pertinent to the Pr$^{3+}$ ion with $J$ = 4. Given the crystalline electric field situation of Pr$^{3+}$, the inference of a doublet state from specific heat and consequent long-range magnetic order is an unexpected result., Comment: 7 Pages and 8 Figures

Spin freezing in the $A$-site spinel FeAl$_2$O$_4$ which is a spin liquid candidate is studied using remnant magnetization and nonlinear magnetic susceptibility and isofield cooling and heating protocols. The remnant magnetization behavior of FeAl$_2$O$_4$ differs significantly from that of a canonical spin glass which is also supported by analysis of the nonlinear magnetic susceptibility term $\chi_3 (T)$. Through the power-law analysis of $\chi_3 (T)$, a spin-freezing temperature, $T_g$ = 11.4$\pm$0.9~K and critical exponent, $\gamma$ = 1.48$\pm$0.59 are obtained. Cole-Cole analysis of magnetic susceptibility shows the presence of broad spin relaxation times in FeAl$_2$O$_4$, however, the irreversible dc susceptibility plot discourages an interpretation based on conventional spin glass features. The magnetization measured using the cooling-and-heating-in-unequal-fields protocol brings more insight to the magnetic nature of this frustrated magnet and reveals unconventional glassy behaviour. Combining our results, we arrive at the conclusion that the present sample of FeAl$_2$O$_4$ consists of a majority spin liquid phase with "glassy" regions embedded., Comment: 5 pages, 6 figs, 2-column, Accepted to Phys. Rev. B

We generalize Ramanujan method of approximating the smallest root of an equation which is found in Ramanujan Note books, Part-I. We provide simple analytical proof to study convergence of this method. Moreover, we study iterative approach of this method on approximating a root with arbitrary order of convergence., Comment: Comments are invited

Currently, criminals profile (CP) is obtained from investigators or forensic psychologists interpretation, linking crime scene characteristics and an offenders behavior to his or her characteristics and psychological profile. This paper seeks an efficient and systematic discovery of nonobvious and valuable patterns between variables from a large database of solved cases via a probabilistic network (PN) modeling approach. The PN structure can be used to extract behavioral patterns and to gain insight into what factors influence these behaviors. Thus, when a new case is being investigated and the profile variables are unknown because the offender has yet to be identified, the observed crime scene variables are used to infer the unknown variables based on their connections in the structure and the corresponding numerical (probabilistic) weights. The objective is to produce a more systematic and empirical approach to profiling, and to use the resulting PN model as a decision tool., Comment: IEEE format, International Journal of Computer Science and Information Security, IJCSIS January 2010, ISSN 1947 5500, http://sites.google.com/site/ijcsis/

We study few properties of square-free integers in certain equations. Using this property, we derive some infinite products in powers of square free numbers. Also, we present a method, to convert power series and trigonometric series to infinte products. Infinite products of few elementary trigonometric functions and factorials for large numbers are shown as examples., Comment: 11 pages, comments are invited

We show some definite integrals connecting to infinite series, studied in Ramanujan's paper, titled "On question 330 of Professor Sanjana". We present few recursive methods to evaluate these definite integrals in various cases and we generalize this, to evaluate simliar kind of integrals through infinite series., Comment: 12 pages, Paper published

The development of high-degree interpolation polynomials which use the values of the function and its subsequent derivatives is reformulated. Also, we present a variant of new formula in barycentric form., Comment: This paper has been withdrawn by the author due to a crucial sign error in main formula

Through introducing a new iterative formula for divided differnce using Neville's and Aitken's algorithms,we study new iterative methods for interpolation,numerical differentiation and numerical integration formulas with arbitrary order of accuracy for evanly or unevanly spaced data. Basic computer algorithms for new methods are given, Comment: 11 pages, comments are invited

We present a new formula for divided difference and few new schemes of divided difference tables in this paper. Through this, we derive new interpolation, numerical differentiation and numerical integration formulas with arbitrary order of accuracy for evanly and unevanly spaced data. First, we study the new interpolation formula which generalizes both Newton's and Lagrange's interpolation formula with the new divided difference table for unevanly spaced points and using this; we derive other interpolation formulas, in terms of differences for evanly spaced data. Second, we study two new different methods of numerical differentiation for both evanly and unevanly spaced points without differentiating the interpolating polynomials or the use of operators. Third, we derive new numerical integration formulas using new differentiation formulas and Taylor formula for both evanly and unevanly spaced data. Basic computer algorithms for few new formulas are given. In Comparison to former polynomial interpolation, numerical differentiation and numerical integration formuals, these new formulas have some featured advantages for approximating functional values, numerical derivatives of higher order and approximate integral values for evanly and unevanly spaced data., Comment: 34 pages