Combining Auto-Encoder with LSTM for WiFi-Based Fingerprint Positioning

Author's accepted manuscript.Although indoor positioning has long been investigated by various means, its accuracy remains concern. Several recent studies have applied machine learning algorithms to explore wireless fidelity (WiFi)-based positioning. In this paper, we propose a novel deep learning model which concatenates an auto-encoder with a long short term memory (LSTM) network for the purpose of WiFi fingerprint positioning. We first employ an auto-encoder to extract representative latent codes of fingerprints. Such an extraction is proven to be more reliable than simply using a deep neural network to extract representative features since a latent code can be reverted back to its original input. Then, a sequence of latent codes are injected into an LSTM network to identify location. To assess the accuracy and effectiveness of our model, we perform extensive real-life experiments.acceptedVersio

Predicting the Severity and Prognosis of Trismus after Intensity-Modulated Radiation Therapy for Oral Cancer Patients by Magnetic Resonance Imaging

To develop magnetic resonance imaging (MRI) indicators to predict trismus outcome for post-operative oral cavity cancer patients who received adjuvant intensity-modulated radiation therapy (IMRT), 22 patients with oral cancer treated with IMRT were studied over a two-year period. Signal abnormality scores (SA scores) were computed from Likert-type ratings of the abnormalities of nine masticator structures and compared with the Mann-Whitney U-test and Kruskal–Wallis one-way ANOVA test between groups. Seventeen patients (77.3%) experienced different degrees of trismus during the two-year follow-up period. The SA score correlated with the trismus grade (r = 0.52, p<0.005). Patients having progressive trismus had higher mean doses of radiation to multiple structures, including the masticator and lateral pterygoid muscles, and the parotid gland (p<0.05). In addition, this group also had higher SA-masticator muscle dose product at 6 months and SA scores at 12 months (p<0.05). At the optimum cut-off points of 0.38 for the propensity score, the sensitivity was 100% and the specificity was 93% for predicting the prognosis of the trismus patients. The SA score, as determined using MRI, can reflect the radiation injury and correlate to trismus severity. Together with the radiation dose, it could serve as a useful biomarker to predict the outcome and guide the management of trismus following radiation therapy

Comparison of single-incision mini-slings (Ajust) and standard transobturator midurethral slings (Align) in the management of female stress urinary incontinence: A 1-year follow-up

AbstractObjectiveTo investigate the effectiveness and safety of a new single-incision mini-sling (SIMS)—Ajust—compared with the standard transobturator midurethral sling (SMUS)—Align—for the treatment of female stress urinary incontinence (SUI).Materials and MethodsA retrospective cohort study was conducted between January 1, 2010 and August 31, 2012. Women with SUI who underwent either SMUS-Align or SIMS-Ajust were recruited. The primary outcomes included operation time, estimated operative blood loss, postoperative pain, and complications. The secondary outcomes included subjective and objective success, defined as an International Consultation on Incontinence Questionnaire (ICIQ) score of 0 or improvement as felt by the patient and a long-term complication, such as dyspareunia and mesh erosion after 6 months and 12 months of follow-up.ResultsA total of 136 patients were enrolled, including 76 receiving SMUS-Align and 60 receiving SIMS-Ajust. Baseline characteristics of the patients in both groups were similar, without a statistically significant difference. Primary outcomes between both groups were similar, except that women treated with SIMS-Ajust had statistically significantly shorter operation time (p = 0.003), less intent to treat (p < 0.05), and earlier postoperative discharge (p = 0.001) than women treated with SMUS-Align. Secondary outcomes were similar without a significant difference between the two groups (93% vs. 88% success rate in each group).ConclusionOur results showed that SIMS-Ajust was not inferior to SMUS-Align with respect to success rate, and might have a slight advantage in early discharge. A long-term follow-up or prospective study is needed to confirm the above findings

Text mining techniques for patent analysis.

Abstract Patent documents contain important research results. However, they are lengthy and rich in technical terminology such that it takes a lot of human efforts for analyses. Automatic tools for assisting patent engineers or decision makers in patent analysis are in great demand. This paper describes a series of text mining techniques that conforms to the analytical process used by patent analysts. These techniques include text segmentation, summary extraction, feature selection, term association, cluster generation, topic identification, and information mapping. The issues of efficiency and effectiveness are considered in the design of these techniques. Some important features of the proposed methodology include a rigorous approach to verify the usefulness of segment extracts as the document surrogates, a corpus-and dictionary-free algorithm for keyphrase extraction, an efficient co-word analysis method that can be applied to large volume of patents, and an automatic procedure to create generic cluster titles for ease of result interpretation. Evaluation of these techniques was conducted. The results confirm that the machine-generated summaries do preserve more important content words than some other sections for classification. To demonstrate the feasibility, the proposed methodology was applied to a realworld patent set for domain analysis and mapping, which shows that our approach is more effective than existing classification systems. The attempt in this paper to automate the whole process not only helps create final patent maps for topic analyses, but also facilitates or improves other patent analysis tasks such as patent classification, organization, knowledge sharing, and prior art searches

EmoVIT: Revolutionizing Emotion Insights with Visual Instruction Tuning

Visual Instruction Tuning represents a novel learning paradigm involving the fine-tuning of pre-trained language models using task-specific instructions. This paradigm shows promising zero-shot results in various natural language processing tasks but is still unexplored in vision emotion understanding. In this work, we focus on enhancing the model's proficiency in understanding and adhering to instructions related to emotional contexts. Initially, we identify key visual clues critical to visual emotion recognition. Subsequently, we introduce a novel GPT-assisted pipeline for generating emotion visual instruction data, effectively addressing the scarcity of annotated instruction data in this domain. Expanding on the groundwork established by InstructBLIP, our proposed EmoVIT architecture incorporates emotion-specific instruction data, leveraging the powerful capabilities of Large Language Models to enhance performance. Through extensive experiments, our model showcases its proficiency in emotion classification, adeptness in affective reasoning, and competence in comprehending humor. The comparative analysis provides a robust benchmark for Emotion Visual Instruction Tuning in the era of LLMs, providing valuable insights and opening avenues for future exploration in this domain. Our code is available at \url{https://github.com/aimmemotion/EmoVIT}.Comment: Accepted by CVPR 202

The Potential Utility of Curcumin in the Treatment of HER-2-Overexpressed Breast Cancer: An In Vitro and In Vivo Comparison Study with Herceptin

HER-2 is an important oncoprotein overexpressed in about 15–25% of breast cancers. We hypothesized that the ability of curcumin to downregulate HER-2 oncoprotein and inhibit the signal transduction pathway of PI3K/Akt, MAPK, and NF-κB activation may be important in the treatment of HER-2-overexpressed breast cancer. To examine the effect of curcumin on breast cancer cells, MCF-7, MDA-MB-231, MCF-10A, BT-474, and SK-BR-3-hr (a herceptin resistant strain from SK-BR-3) cells were used for in vitro analysis. The in vivo effect of curcumin on HER-2-overexpressed breast cancer was investigated with the HER-2-overexpressed BT-474 xenograft model. Cell growth, cell cycle change, the antimobility effect, signal transduction, and xenograft volume analysis between groups treated with herceptin and/or curcumin were tested. Curcumin decreased the cell growth of various breast cancer cell lines (MCF-7, MDA-MB-231, MCF-10A, BT-474, and SK-BR-3-hr). In Western blot analysis, the phosphorylation of Akt, MAPK, and expression of NF-κB were reduced in BT-474 cells, but not in SK-BR-3-hr cells, after treatment with herceptin. When treated with curcumin, the HER-2 oncoprotein, phosphorylation of Akt, MAPK and expression of NF-κB were decreased in both BT-474 and SK-BR-3-hr cells. In the BT-474 xenograft model, though not as much as herceptin, curcumin did effectively decrease the tumor size. The combination of curcumin with herceptin was not better than herceptin alone; however, the combination of taxol and curcumin had an antitumor effect comparable with taxol and herceptin. The results suggested that curcumin has potential as a treatment for HER-2-overexpressed breast cancer

Search for direct production of charginos and neutralinos in events with three leptons and missing transverse momentum in √s = 7 TeV pp collisions with the ATLAS detector

A search for the direct production of charginos and neutralinos in final states with three electrons or muons and missing transverse momentum is presented. The analysis is based on 4.7 fb−1 of proton–proton collision data delivered by the Large Hadron Collider and recorded with the ATLAS detector. Observations are consistent with Standard Model expectations in three signal regions that are either depleted or enriched in Z-boson decays. Upper limits at 95% confidence level are set in R-parity conserving phenomenological minimal supersymmetric models and in simplified models, significantly extending previous results

Measurement of the top quark-pair production cross section with ATLAS in pp collisions at \sqrt{s}=7\TeV

A measurement of the production cross-section for top quark pairs(\ttbar) in $pp$ collisions at \sqrt{s}=7 \TeV is presented using data recorded with the ATLAS detector at the Large Hadron Collider. Events are selected in two different topologies: single lepton (electron $e$ or muon $\mu$) with large missing transverse energy and at least four jets, and dilepton ($ee$, $\mu\mu$ or $e\mu$) with large missing transverse energy and at least two jets. In a data sample of 2.9 pb-1, 37 candidate events are observed in the single-lepton topology and 9 events in the dilepton topology. The corresponding expected backgrounds from non-\ttbar Standard Model processes are estimated using data-driven methods and determined to be $12.2 \pm 3.9$ events and $2.5 \pm 0.6$ events, respectively. The kinematic properties of the selected events are consistent with SM \ttbar production. The inclusive top quark pair production cross-section is measured to be \sigmattbar=145 \pm 31 ^{+42}_{-27} pb where the first uncertainty is statistical and the second systematic. The measurement agrees with perturbative QCD calculations.Comment: 30 pages plus author list (50 pages total), 9 figures, 11 tables, CERN-PH number and final journal adde

Inclusive search for same-sign dilepton signatures in pp collisions at root s=7 TeV with the ATLAS detector

An inclusive search is presented for new physics in events with two isolated leptons (e or mu) having the same electric charge. The data are selected from events collected from p p collisions at root s = 7 TeV by the ATLAS detector and correspond to an integrated luminosity of 34 pb(-1). The spectra in dilepton invariant mass, missing transverse momentum and jet multiplicity are presented and compared to Standard Model predictions. In this event sample, no evidence is found for contributions beyond those of the Standard Model. Limits are set on the cross-section in a fiducial region for new sources of same-sign high-mass dilepton events in the ee, e mu and mu mu channels. Four models predicting same-sign dilepton signals are constrained: two descriptions of Majorana neutrinos, a cascade topology similar to supersymmetry or universal extra dimensions, and fourth generation d-type quarks. Assuming a new physics scale of 1 TeV, Majorana neutrinos produced by an effective operator V with masses below 460 GeV are excluded at 95% confidence level. A lower limit of 290 GeV is set at 95% confidence level on the mass of fourth generation d-type quarks

Deploying Image Deblurring across Mobile Devices: A Perspective of Quality and Latency

Recently, image enhancement and restoration have become important applications on mobile devices, such as super-resolution and image deblurring. However, most state-of-the-art networks present extremely high computational complexity. This makes them difficult to be deployed on mobile devices with acceptable latency. Moreover, when deploying to different mobile devices, there is a large latency variation due to the difference and limitation of deep learning accelerators on mobile devices. In this paper, we conduct a search of portable network architectures for better quality-latency trade-off across mobile devices. We further present the effectiveness of widely used network optimizations for image deblurring task. This paper provides comprehensive experiments and comparisons to uncover the in-depth analysis for both latency and image quality. Through all the above works, we demonstrate the successful deployment of image deblurring application on mobile devices with the acceleration of deep learning accelerators. To the best of our knowledge, this is the first paper that addresses all the deployment issues of image deblurring task across mobile devices. This paper provides practical deployment-guidelines, and is adopted by the championship-winning team in NTIRE 2020 Image Deblurring Challenge on Smartphone Track.Comment: CVPR 2020 Workshop on New Trends in Image Restoration and Enhancement (NTIRE