Learning to Estimate Driver Drowsiness from Car Acceleration Sensors using Weakly Labeled Data

This paper addresses the learning task of estimating driver drowsiness from the signals of car acceleration sensors. Since even drivers themselves cannot perceive their own drowsiness in a timely manner unless they use burdensome invasive sensors, obtaining labeled training data for each timestamp is not a realistic goal. To deal with this difficulty, we formulate the task as a weakly supervised learning. We only need to add labels for each complete trip, not for every timestamp independently. By assuming that some aspects of driver drowsiness increase over time due to tiredness, we formulate an algorithm that can learn from such weakly labeled data. We derive a scalable stochastic optimization method as a way of implementing the algorithm. Numerical experiments on real driving datasets demonstrate the advantages of our algorithm against baseline methods.Comment: Accepted by ICASSP202

Posterior Mean Super-Resolution with a Compound Gaussian Markov Random Field Prior

This manuscript proposes a posterior mean (PM) super-resolution (SR) method with a compound Gaussian Markov random field (MRF) prior. SR is a technique to estimate a spatially high-resolution image from observed multiple low-resolution images. A compound Gaussian MRF model provides a preferable prior for natural images that preserves edges. PM is the optimal estimator for the objective function of peak signal-to-noise ratio (PSNR). This estimator is numerically determined by using variational Bayes (VB). We then solve the conjugate prior problem on VB and the exponential-order calculation cost problem of a compound Gaussian MRF prior with simple Taylor approximations. In experiments, the proposed method roughly overcomes existing methods.Comment: 5 pages, 20 figures, 1 tables, accepted to ICASSP2012 (corrected 2012/3/23

Regression with Sensor Data Containing Incomplete Observations

This paper addresses a regression problem in which output label values are the results of sensing the magnitude of a phenomenon. A low value of such labels can mean either that the actual magnitude of the phenomenon was low or that the sensor made an incomplete observation. This leads to a bias toward lower values in labels and its resultant learning because labels may have lower values due to incomplete observations, even if the actual magnitude of the phenomenon was high. Moreover, because an incomplete observation does not provide any tags indicating incompleteness, we cannot eliminate or impute them. To address this issue, we propose a learning algorithm that explicitly models incomplete observations corrupted with an asymmetric noise that always has a negative value. We show that our algorithm is unbiased as if it were learned from uncorrupted data that does not involve incomplete observations. We demonstrate the advantages of our algorithm through numerical experiments

確率的隠れ構造モデリングを用いたBayes最適な推定に関する研究

早大学位記番号:新7539早稲田大

Pancreatectomy in patients with LC

Background : Several reports have shown a high mortality rate in patients with liver cirrhosis (LC) who undergo pancreaticoduodenectomy, however, there are few reports on its long-term prognosis. Methods : Twelve patients with LC who had undergone pancreatic resection were enrolled. To compare clinicopathological variables, 159 non-LC patients who had undergone resection for pancreatic cancer were enrolled. Results : Pancreaticoduodenectomy (PD) was performed in 5 LC patients and distal pancreatectomy (DP) was performed in 7 LC patients. Patients in the LC group had more co-morbidities, lower platelet counts and higher Fib4 index than the non-LC group. The postoperative complication rate was higher in the LC group (83.3% vs 47.8%). While the postoperative hospital stay and 30-day mortality rate were not different, the 90-day mortality rate was higher in the LC group (25.0% vs 2.5% ; p < 0.01). Comparison by operative procedure showed no significant differences of postoperative outcomes in DP cases. However, in PD cases, postoperative complications were more frequent (100% vs 42.3%) and 90-day mortality was higher (40.0% vs 2.9% ; p < 0.01) in the LC group. Conclusions : PD resulted in higher postoperative morbidity and mortality rates in patients with LC compared with non-LC patients. DP could be tolerated in the LC patients

Molecular basis of sugar recognition by collectin-K1 and the effects of mutations associated with 3MC syndrome

Background Collectin-K1 (CL-K1, or CL-11) is a multifunctional Ca2+-dependent lectin with roles in innate immunity, apoptosis and embryogenesis. It binds to carbohydrates on pathogens to activate the lectin pathway of complement and together with its associated serine protease MASP-3 serves as a guidance cue for neural crest development. High serum levels are associated with disseminated intravascular coagulation, where spontaneous clotting can lead to multiple organ failure. Autosomal mutations in the CL-K1 or MASP-3 genes cause a developmental disorder called 3MC (Carnevale, Mingarelli, Malpuech and Michels) syndrome, characterised by facial, genital, renal and limb abnormalities. One of these mutations (Gly204Ser in the CL-K1 gene) is associated with undetectable levels of protein in the serum of affected individuals. Results In this study, we show that CL-K1 primarily targets a subset of high-mannose oligosaccharides present on both self- and non-self structures, and provide the structural basis for its ligand specificity. We also demonstrate that three disease-associated mutations prevent secretion of CL-K1 from mammalian cells, accounting for the protein deficiency observed in patients. Interestingly, none of the mutations prevent folding nor oligomerization of recombinant fragments containing the mutations in vitro. Instead, they prevent Ca2+ binding by the carbohydrate-recognition domains of CL-K1. We propose that failure to bind Ca2+ during biosynthesis leads to structural defects that prevent secretion of CL-K1, thus providing a molecular explanation of the genetic disorder. Conclusions We have established the sugar specificity of CL-K1 and demonstrated that it targets high-mannose oligosaccharides on self- and non-self structures via an extended binding site which recognises the terminal two mannose residues of the carbohydrate ligand. We have also shown that mutations associated with a rare developmental disorder called 3MC syndrome prevent the secretion of CL-K1, probably as a result of structural defects caused by disruption of Ca2+ binding during biosynthesis

Results of the search for inspiraling compact star binaries from TAMA300's observation in 2000-2004

We analyze the data of TAMA300 detector to search for gravitational waves from inspiraling compact star binaries with masses of the component stars in the range 1-3Msolar. In this analysis, 2705 hours of data, taken during the years 2000-2004, are used for the event search. We combine the results of different observation runs, and obtained a single upper limit on the rate of the coalescence of compact binaries in our Galaxy of 20 per year at a 90% confidence level. In this upper limit, the effect of various systematic errors such like the uncertainty of the background estimation and the calibration of the detector's sensitivity are included.Comment: 8 pages, 4 Postscript figures, uses revtex4.sty The author list was correcte

Observation results by the TAMA300 detector on gravitational wave bursts from stellar-core collapses

We present data-analysis schemes and results of observations with the TAMA300 gravitational-wave detector, targeting burst signals from stellar-core collapse events. In analyses for burst gravitational waves, the detection and fake-reduction schemes are different from well-investigated ones for a chirp-wave analysis, because precise waveform templates are not available. We used an excess-power filter for the extraction of gravitational-wave candidates, and developed two methods for the reduction of fake events caused by non-stationary noises of the detector. These analysis schemes were applied to real data from the TAMA300 interferometric gravitational wave detector. As a result, fake events were reduced by a factor of about 1000 in the best cases. The resultant event candidates were interpreted from an astronomical viewpoint. We set an upper limit of 2.2x10^3 events/sec on the burst gravitational-wave event rate in our Galaxy with a confidence level of 90%. This work sets a milestone and prospects on the search for burst gravitational waves, by establishing an analysis scheme for the observation data from an interferometric gravitational wave detector