DeepIOD: Towards A Context-Aware Indoor-Outdoor Detection Framework Using Smartphone Sensors.

Accurate indoor-outdoor detection (IOD) is essential for location-based services, context-aware computing, and mobile applications, as it enhances service relevance and precision. However, traditional IOD methods, which rely only on GPS data, often fail in indoor environments due to signal obstructions, while IMU data are unreliable on unseen data in real-time applications due to reduced generalizability. This study addresses this research gap by introducing the DeepIOD framework, which leverages IMU sensor data, GPS, and light information to accurately classify environments as indoor or outdoor. The framework preprocesses input data and employs multiple deep neural network models, combining outputs using an adaptive majority voting mechanism to ensure robust and reliable predictions. Experimental results evaluated on six unseen environments using a smartphone demonstrate that DeepIOD achieves significantly higher accuracy than methods using only IMU sensors. Our DeepIOD system achieves a remarkable accuracy rate of 98-99% with a transition time of less than 10 ms. This research concludes that DeepIOD offers a robust and reliable solution for indoor-outdoor classification with high generalizability, highlighting the importance of integrating diverse data sources to improve location-based services and other applications requiring precise environmental context awareness.

Controlling the Interlayer Dzyaloshinskii-Moriya Interaction by Electrical Currents.

The recently discovered interlayer Dzyaloshinskii-Moriya interaction (IL-DMI) in multilayers with perpendicular magnetic anisotropy favors canting of spins in the in-plane direction. It could thus stabilize intriguing spin textures such as Hopfions. A key requirement for nucleation is to control the IL-DMI. Therefore, we investigate the influence of an electric current on a synthetic antiferromagnet with growth-induced IL-DMI. The IL-DMI is quantified by using out-of-plane hysteresis loops of the anomalous Hall effect while applying a static in-plane magnetic field at varied azimuthal angles. We observe a shift in the azimuthal dependence with an increasing current, which we conclude to originate from the additional in-plane symmetry breaking introduced by the current flow. Fitting the angular dependence, we demonstrate the presence of an additive current-induced term that linearly increases the IL-DMI in the direction of current flow. This opens the possibility of easily manipulating 3D spin textures by currents.

An Adaptive User Tracking Algorithm Using Irregular Data Frames for Passive Fingerprint Positioning.

Wi-Fi fingerprinting is the most popular indoor positioning method today, representing received signal strength (RSS) values as vector-type fingerprints. Passive fingerprinting, unlike the active fingerprinting method, has the advantage of being able to track location without user participation by utilizing the signals that are naturally emitted from the user's smartphone. However, since signals are generated depending on the user's network usage patterns, there is a problem in that data are irregularly collected according to the patterns. Therefore, this paper proposes an adaptive algorithm that shows stable tracking performances for fingerprints generated at irregular time intervals. The accuracy and stability of the proposed tracking method were verified by experiments conducted in three scenarios. Through the proposed method, it is expected that the stability of indoor positioning and the quality of location-based services will improve.

Long-range chiral exchange interaction in synthetic antiferromagnets.

The exchange interaction governs static and dynamic magnetism. This fundamental interaction comes in two flavours-symmetric and antisymmetric. The symmetric interaction leads to ferro- and antiferromagnetism, and the antisymmetric interaction has attracted significant interest owing to its major role in promoting topologically non-trivial spin textures that promise fast, energy-efficient devices. So far, the antisymmetric exchange interaction has been found to be rather short ranged and limited to a single magnetic layer. Here we report a long-range antisymmetric interlayer exchange interaction in perpendicularly magnetized synthetic antiferromagnets with parallel and antiparallel magnetization alignments. Asymmetric hysteresis loops under an in-plane field reveal a unidirectional and chiral nature of this interaction, which results in canted magnetic structures. We explain our results by considering spin-orbit coupling combined with reduced symmetry in multilayers. Our discovery of a long-range chiral interaction provides an additional handle to engineer magnetic structures and could enable three-dimensional topological structures.

Author Correction: Long-range chiral exchange interaction in synthetic antiferromagnets.

In the version of this Article originally published, the sentence 'D.-S.H. wrote the paper with K.L., J.H. and M.K.' in the author contributions was incorrect; it should have read 'D.-S.H. wrote the paper with K.L., J.H., M.-H.J. and M.K.' This has been corrected in the online versions of the Article.

Electric-Field Control of Spin-Orbit Torques in Perpendicularly Magnetized W/CoFeB/MgO Films.

Controlling magnetism by electric fields offers a highly attractive perspective for designing future generations of energy-efficient information technologies. Here, we demonstrate that the magnitude of current-induced spin-orbit torques in thin perpendicularly magnetized CoFeB films can be tuned and even increased by electric-field generated piezoelectric strain. Using theoretical calculations, we uncover that the subtle interplay of spin-orbit coupling, crystal symmetry, and orbital polarization is at the core of the observed strain dependence of spin-orbit torques. Our results open a path to integrating two energy efficient spin manipulation approaches, the electric-field-induced strain and the current-induced magnetization switching, thereby enabling novel device concepts.

Fusion of the SLAM with Wi-Fi-Based Positioning Methods for Mobile Robot-Based Learning Data Collection, Localization, and Tracking in Indoor Spaces.

The ability to estimate the current locations of mobile robots that move in a limited workspace and perform tasks is fundamental in robotic services. However, even if the robot is given a map of the workspace, it is not easy to quickly and accurately determine its own location by relying only on dead reckoning. In this paper, a new signal fluctuation matrix and a tracking algorithm that combines the extended Viterbi algorithm and odometer information are proposed to improve the accuracy of robot location tracking. In addition, to collect high-quality learning data, we introduce a fusion method called simultaneous localization and mapping and Wi-Fi fingerprinting techniques. The results of the experiments conducted in an office environment confirm that the proposed methods provide accurate and efficient tracking results. We hope that the proposed methods will also be applied to different fields, such as the Internet of Things, to support real-life activities.

AMID: Accurate Magnetic Indoor Localization Using Deep Learning.

Geomagnetic-based indoor positioning has drawn a great attention from academia and industry due to its advantage of being operable without infrastructure support and its reliable signal characteristics. However, it must overcome the problems of ambiguity that originate with the nature of geomagnetic data. Most studies manage this problem by incorporating particle filters along with inertial sensors. However, they cannot yield reliable positioning results because the inertial sensors in smartphones cannot precisely predict the movement of users. There have been attempts to recognize the magnetic sequence pattern, but these attempts are proven only in a one-dimensional space, because magnetic intensity fluctuates severely with even a slight change of locations. This paper proposes accurate magnetic indoor localization using deep learning (AMID), an indoor positioning system that recognizes magnetic sequence patterns using a deep neural network. Features are extracted from magnetic sequences, and then the deep neural network is used for classifying the sequences by patterns that are generated by nearby magnetic landmarks. Locations are estimated by detecting the landmarks. AMID manifested the proposed features and deep learning as an outstanding classifier, revealing the potential of accurate magnetic positioning with smartphone sensors alone. The landmark detection accuracy was over 80% in a two-dimensional environment.

Asymmetric Hysteresis for Probing Dzyaloshinskii-Moriya Interaction.

The interfacial Dzyaloshinskii-Moriya interaction (DMI) is intimately related to the prospect of superior domain-wall dynamics and the formation of magnetic skyrmions. Although some experimental efforts have been recently proposed to quantify these interactions and the underlying physics, it is still far from trivial to address the interfacial DMI. Inspired by the reported tilt of the magnetization of the side edge of a thin film structure, we here present a quasi-static, straightforward measurement tool. By using laterally asymmetric triangular-shaped microstructures, it is demonstrated that interfacial DMI combined with an in-plane magnetic field yields a unique and significant shift in magnetic hysteresis. By systematic variation of the shape of the triangular objects combined with a droplet model for domain nucleation, a robust value for the strength and sign of interfacial DMI is obtained. This method gives immediate and quantitative access to DMI, enabling a much faster exploration of new DMI systems for future nanotechnology.

Spin-orbit-splitting-driven nonlinear Hall effect in NbIrTe4.

The Berry curvature dipole (BCD) serves as a one of the fundamental contributors to emergence of the nonlinear Hall effect (NLHE). Despite intense interest due to its potential for new technologies reaching beyond the quantum efficiency limit, the interplay between BCD and NLHE has been barely understood yet in the absence of a systematic study on the electronic band structure. Here, we report NLHE realized in NbIrTe4 that persists above room temperature coupled with a sign change in the Hall conductivity at 150 K. First-principles calculations combined with angle-resolved photoemission spectroscopy (ARPES) measurements show that BCD tuned by the partial occupancy of spin-orbit split bands via temperature is responsible for the temperature-dependent NLHE. Our findings highlight the correlation between BCD and the electronic band structure, providing a viable route to create and engineer the non-trivial Hall effect by tuning the geometric properties of quasiparticles in transition-metal chalcogen compounds.

Piezoelectric characteristics of PVA/DL-alanine polycrystals in d33 mode.

In this study, polyvinyl alcohol (PVA)-mixed DL-alanine (PVA/DL-alanine) polycrystals are fabricated, and their piezoelectric characteristics in the d33 mode are investigated. The d33 piezoelectric coefficients of the PVA/DL-alanine polycrystals are found to increase with an increase in the weight ratio of DL-alanine, and the PVA/DL-alanine polycrystal composed of PVA and DL-alanine in a weight ratio of 1:3 exhibits a d33 of ∼5 pC/N. The piezoelectric characteristics of the PVA/DL-alanine polycrystals are discussed in terms of the crystal structure by employing scanning electron microscopy and X-ray diffraction analyses. To confirm the piezoelectric performance of the polycrystals, the piezoelectric voltages of a piezoelectric device composed of a single layer of ZnO thin film and heterostructured devices consisting of a layer of PVA/DL-alanine polycrystal and a ZnO thin film layer are measured and compared. This study presents PVA/DL-alanine polycrystals as a potential piezoelectric material for bio-friendly piezoelectric-device applications.

Protein complex prediction based on simultaneous protein interaction network.

MOTIVATION: The increase in the amount of available protein-protein interaction (PPI) data enables us to develop computational methods for protein complex predictions. A protein complex is a group of proteins that interact with each other at the same time and place. The protein complex generally corresponds to a cluster in PPI network (PPIN). However, clusters correspond not only to protein complexes but also to sets of proteins that interact dynamically with each other. As a result, conventional graph-theoretic clustering methods that disregard interaction dynamics show high false positive rates in protein complex predictions. RESULTS: In this article, a method of refining PPIN is proposed that uses the structural interface data of protein pairs for protein complex predictions. A simultaneous protein interaction network (SPIN) is introduced to specify mutually exclusive interactions (MEIs) as indicated from the overlapping interfaces and to exclude competition from MEIs that arise during the detection of protein complexes. After constructing SPINs, naive clustering algorithms are applied to the SPINs for protein complex predictions. The evaluation results show that the proposed method outperforms the simple PPIN-based method in terms of removing false positive proteins in the formation of complexes. This shows that excluding competition between MEIs can be effective for improving prediction accuracy in general computational approaches involving protein interactions. AVAILABILITY: http://code.google.com/p/simultaneous-pin/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Gastric Cancer and the Daily Intake of the Major Dish Groups Contributing to Sodium Intake: A Case-Control Study in Korea.

Studies on the association between gastric cancer (GC) and the intake of soup-based dish groups (noodles and dumplings, soups, and stews), which are sodium-contributing foods, in Korea are insufficient, and the results of studies on the intake of pickled vegetables such as kimchi are inconsistent. This study aimed to determine the association between the incidence of GC and the daily intake of high-sodium dish groups (noodles and dumplings, soups, stews, and pickled vegetables) and whether these associations differ depending on behavioral risk factors for GC. In this case-control study, subjects aged 20-79 years were recruited from two hospitals between December 2002 and September 2006. A total of 440 cases and 485 controls were recruited, of which 307 pairs were matched and included for the analysis. In our results, a higher intake of noodles and dumplings was associated with a significantly increased incidence of GC. In the participants who consumed past or current alcohol, a higher intake of noodles and dumplings was associated with a significantly increased incidence of GC. Our results suggest that efforts to reduce the daily sodium intake from noodles and dumplings are needed to prevent and reduce the incidence of GC.

Differentially Abundant Bacterial Taxa Associated with Prognostic Variables of Crohn's Disease: Results from the IMPACT Study.

Limited studies have examined the intestinal microbiota composition in relation to Crohn's disease (CD) prognosis. We analyzed the differences in microbial communities and relevant metabolic pathways associated with prognostic variables in patients with CD. We applied 16S rRNA gene sequencing to analyze a cohort of 1110 CD and healthy control (HC) fecal samples. We categorized patients with CD into good (CD-G), intermediate (CD-I) and poor (CD-P) prognosis groups, according to the history of using biologics and intestinal resection. Microbiota α-diversity decreased more in CD-P than CD-G and CD-I. Microbiota ß-diversity in CD-P differed from that in CD-G and CD-I. Thirteen genera and 10 species showed differential abundance between CD-G and CD-P groups. Escherichia coli (p = 0.001) and species Producta (p = 0.01) and genera Lactobacillus (p = 0.003) and Coprococcus (p = 0.01) consistently showed differences between CD-G and CD-P groups after adjusting for confounding variables. Functional profiling suggested that the microbial catabolic pathways and pathways related to enterobacterial common antigen and lipopolysaccharide biosynthesis were better represented in the CD-P group than in the CD-G group, and E. coli were the top contributors to these pathways. CD prognosis is associated with altered microbiota composition and decreased diversity, and E. coli might be causally involved in CD progression, and may have adapted to live in inflammatory environments.

Protein complex prediction based on mutually exclusive interactions in protein interaction network.

The increasing amount of available Protein-Protein Interaction (PPI) data enables scalable methods for the protein complex prediction. A protein complex is a group of two or more proteins formed by interactions that are stable over time, and it generally corresponds to a dense sub-graph in PPI Network (PPIN). However, dense sub-graphs correspond not only to stable protein complexes but also to sets of proteins including dynamic interactions. As a result, conventional simple PPIN based graph-theoretic clustering methods have high false positive rates in protein complex prediction. In this paper, we propose an approach to predict protein complexes based on the integration of PPI data and mutually exclusive interaction information drawn from structural interface data of protein domains. The extraction of Simultaneous Protein Interaction Cluster (SPIC) is the essence of our approach, which excludes interaction conflicts in network clusters by achieving mutually exclusion among interactions. The concept of SPIC was applied to conventional graph-theoretic clustering algorithms, MCODE and LCMA, to evaluate the density of clusters for protein complex prediction. The comparison with original graph-theoretic clustering algorithms verified the effectiveness of our approach; SPIC based methods refined false positives of original methods to be true positive complexes, without any loss of true positive predictions yielded by original methods.

Comparison of postpolypectomy bleeding between epinephrine and saline submucosal injection for large colon polyps by conventional polypectomy: a prospective randomized, multicenter study.

AIM: To evaluate and compare the clinical outcomes of prophylactic submucosal saline-epinephrine injection and saline injection alone for large colon polyps by conventional polypectomy. METHODS: A prospective study was conducted from July 2003 to July 2004 at 11 tertiary endoscopic centers. Large colon polyps (> 10 mm in diameter) were randomized to undergo endoscopic polypectomy with submucosal saline-epinephrine injection (epinephrine group) or normal saline injection (saline group). Endoscopic polypectomy was performed by the conventional snare method, and early (< 12 h) and late bleeding complications (12 h-30 d) were observed. RESULTS: A total of 561 polyps in 486 patients were resected by endoscopic polypectomy. Overall, bleeding complications occurred in 7.6% (37/486) of the patients, including 4.9% (12/244) in the epinephrine group, and 10.3% (25/242) in the saline group. Early and late postpolypectomy bleeding (PPB) occurred in 6.6% (32/486) and 1% (5/486) of the patients, respectively, including 4.5% (11/244), 0.4% (1/244) in the epinephrine group, and 8.7% (21/242), 1.7% (4/242) in the saline group. No significant differences in the rates of overall, early and late PPB were observed between the 2 groups. Multivariate stepwise logistic regression analysis revealed that large size (> 2 cm) and neoplastic polyps were independently and significantly associated with the presence of PPB. CONCLUSION: The prophylactic submucosal injection of diluted epinephrine does not appear to provide an additional advantage over the saline injection alone for the prevention of PPB.

PreSPI: a domain combination based prediction system for protein-protein interaction.

With the accumulation of protein and its related data on the Internet, many domain-based computational techniques to predict protein interactions have been developed. However, most techniques still have many limitations when used in real fields. They usually suffer from low accuracy in prediction and do not provide any interaction possibility ranking method for multiple protein pairs. In this paper, we propose a probabilistic framework to predict the interaction probability of proteins and develop an interaction possibility ranking method for multiple protein pairs. Using the ranking method, one can discern the protein pairs that are more likely to interact with each other in multiple protein pairs. The validity of the prediction model was evaluated using an interacting set of protein pairs in yeast and an artificially generated non-interacting set of protein pairs. When 80% of the set of interacting protein pairs in the DIP (Database of Interacting Proteins) was used as a learning set of interacting protein pairs, high sensitivity (77%) and specificity (95%) were achieved for the test groups containing common domains with the learning set of proteins within our framework. The stability of the prediction model was also evident when tested over DIP CORE, HMS-PCI and TAP data. In the validation of the ranking method, we reveal that some correlations exist between the interacting probability and the accuracy of the prediction.

Inter-species validation for domain combination based protein-protein interaction prediction method.

Domain Combination based Protein-Protein Interaction Prediction (DCPPIP) method is revealed to show outstanding prediction accuracy in Yeast proteins. However, it is not yet apparent whether the method is still valid and can achieve comparable prediction accuracy for the proteins in other species. In this paper, we report the validation results of applying the DCPPIP method for Fly and Human proteins. We also report the results of inter-species validation, in which protein interaction and domain data of other species are used as learning set. 10,351 interacting protein pairs are used for the validation for Fly, 2,345 protein pairs for Human. 80% of the data are used as learning sets and 20% are reserved as test sets. High prediction accuracies (Fly: sensitivity approximately 77%, specificity approximately 92%, Human: sensitivity approximately 96%, specificity approximately 95%) are achieved in both Fly and Human cases. Interactions of proteins in Human, Mouse, H. pylori, E. coli, and C. elegans are predicted and validated using the protein interaction and domain data in Yeast, Fly, and the combination of Yeast and Fly respectively. Again, good prediction accuracy is achieved when the test protein pair has common domains with the proteins in a learning set of proteins. A notion of Domain Overlapping Rate (DOR) among species is newly developed in this paper and the correlation between DOR and prediction accuracy is examined. According to out test results, there exists fairly obvious correlation between DOR and prediction accuracy.

Thickness dependence of the interfacial Dzyaloshinskii-Moriya interaction in inversion symmetry broken systems.

In magnetic multilayer systems, a large spin-orbit coupling at the interface between heavy metals and ferromagnets can lead to intriguing phenomena such as the perpendicular magnetic anisotropy, the spin Hall effect, the Rashba effect, and especially the interfacial Dzyaloshinskii-Moriya (IDM) interaction. This interfacial nature of the IDM interaction has been recently revisited because of its scientific and technological potential. Here we demonstrate an experimental technique to straightforwardly observe the IDM interaction, namely Brillouin light scattering. The non-reciprocal spin wave dispersions, systematically measured by Brillouin light scattering, allow not only the determination of the IDM energy densities beyond the regime of perpendicular magnetization but also the revelation of the inverse proportionality with the thickness of the magnetic layer, which is a clear signature of the interfacial nature. Altogether, our experimental and theoretical approaches involving double time Green's function methods open up possibilities for exploring magnetic hybrid structures for engineering the IDM interaction.