Background effects on reconstructed WIMP couplings

In this talk, I presented effects of small, but non-negligible unrejected background events on the determinations of WIMP couplings/cross sections.Comment: 4 pages, 5 eps figures, to appear in the proceedings of the 12th International Conference on Topics in Astroparticle and Underground Physics (TAUP 2011), September 5-9, 2011, Munich, German

Determining Ratios of WIMP-Nucleon Cross Sections from Direct Dark Matter Detection Data

Weakly Interacting Massive Particles (WIMPs) are one of the leading candidates for Dark Matter. So far the usual procedure for constraining the WIMP-nucleon cross sections in direct Dark Matter detection experiments have been to fit the predicted event rate based on some model(s) of the Galactic halo and of WIMPs to experimental data. One has to assume whether the spin-independent (SI) or the spin-dependent (SD) WIMP-nucleus interaction dominates, and results of such data analyses are also expressed as functions of the as yet unknown WIMP mass. In this article, I introduce methods for extracting information on the WIMP-nucleon cross sections by considering a general combination of the SI and SD interactions. Neither prior knowledge about the local density and the velocity distribution of halo WIMPs nor about their mass is needed. Assuming that an exponential-like shape of the recoil spectrum is confirmed from experimental data, the required information are only the measured recoil energies (in low energy ranges) and the number of events in the first energy bin from two or more experiments.Comment: 33 pages, 20 eps figures; v2: typos fixed, references added and updated, revised version for publicatio

Analyzing direct dark matter detection data with unrejected background events by the AMIDAS website

In this talk I have presented the data analysis results of extracting properties of halo WIMPs: the mass and the (ratios between the) spin-independent and spin-dependent couplings/cross sections on nucleons by the AMIDAS website by taking into account possible unrejected background events in the analyzed data sets. Although non-standard astronomical setup has been used to generate pseudodata sets for our analyses, it has been found that, without prior information/assumption about the local density and velocity distribution of halo Dark Matter, these WIMP properties have been reconstructed with ~ 2% to <~ 30% deviations from the input values.Comment: 9 pages, 10 eps figures, 1 table, to appear in the proceedings of the Seventh International Workshop on the Dark Side of the Universe (DSU 2011), September 26-30, 2011, Beijing, Chin

Agreement of Anterior Segment Parameters Obtained From Swept-Source Fourier-Domain and Time-Domain Anterior Segment Optical Coherence Tomography.

PurposeTo assess the interdevice agreement between swept-source Fourier-domain and time-domain anterior segment optical coherence tomography (AS-OCT).MethodsFifty-three eyes from 41 subjects underwent CASIA2 and Visante OCT imaging. One hundred eighty-degree axis images were measured with the built-in two-dimensional analysis software for the swept-source Fourier-domain AS-OCT (CASIA2) and a customized program for the time-domain AS-OCT (Visante OCT). In both devices, we examined the angle opening distance (AOD), trabecular iris space area (TISA), angle recess area (ARA), anterior chamber depth (ACD), anterior chamber width (ACW), and lens vault (LV). Bland-Altman plots and intraclass correlation (ICC) were performed. Orthogonal linear regression assessed any proportional bias.ResultsICC showed strong correlation for LV (0.925) and ACD (0.992) and moderate agreement for ACW (0.801). ICC suggested good agreement for all angle parameters (0.771-0.878) except temporal AOD500 (0.743) and ARA750 (nasal 0.481; temporal 0.481). There was a proportional bias in nasal ARA750 (slope 2.44, 95% confidence interval [CI]: 1.95-3.18), temporal ARA750 (slope 2.57, 95% CI: 2.04-3.40), and nasal TISA500 (slope 1.30, 95% CI: 1.12-1.54). Bland-Altman plots demonstrated in all measured parameters a minimal mean difference between the two devices (-0.089 to 0.063); however, evidence of constant bias was found in nasal AOD250, nasal AOD500, nasal AOD750, nasal ARA750, temporal AOD500, temporal AOD750, temporal ARA750, and ACD. Among the parameters with constant biases, CASIA2 tends to give the larger numbers.ConclusionsBoth devices had generally good agreement. However, there were proportional and constant biases in most angle parameters. Thus, it is not recommended that values be used interchangeably

SemHint-MD: Learning from Noisy Semantic Labels for Self-Supervised Monocular Depth Estimation

Without ground truth supervision, self-supervised depth estimation can be trapped in a local minimum due to the gradient-locality issue of the photometric loss. In this paper, we present a framework to enhance depth by leveraging semantic segmentation to guide the network to jump out of the local minimum. Prior works have proposed to share encoders between these two tasks or explicitly align them based on priors like the consistency between edges in the depth and segmentation maps. Yet, these methods usually require ground truth or high-quality pseudo labels, which may not be easily accessible in real-world applications. In contrast, we investigate self-supervised depth estimation along with a segmentation branch that is supervised with noisy labels provided by models pre-trained with limited data. We extend parameter sharing from the encoder to the decoder and study the influence of different numbers of shared decoder parameters on model performance. Also, we propose to use cross-task information to refine current depth and segmentation predictions to generate pseudo-depth and semantic labels for training. The advantages of the proposed method are demonstrated through extensive experiments on the KITTI benchmark and a downstream task for endoscopic tissue deformation tracking

Latanoprost with high precision, piezo-print microdose delivery for IOP lowering: clinical results of the PG21 study of 0.4 µg daily microdose.

Background:Topical high-precision piezo-print delivery of microdoses of latanoprost achieved significant IOP reduction consistent with the eyedropper effect but with a 75% reduced exposure to drugs and preservatives. Prostaglandin analogs are a mainstay glaucoma therapy. However, conventional eyedroppers deliver 30-50 µL drops that greatly exceed the physiologic 7-µL ocular tear film capacity. Eyedropper overdosing floods the eye with excess drug compounds and preservatives, resulting in ocular surface toxicity, periorbitopathy, and other well-characterized ocular side effects. Piezoelectric high-precision microdosing provides targeted delivery that can reduce exposure to both drug and preservatives compared to conventional eyedropper delivery, with the potential to deliver similar biologic effect. Methods:Both eyes (N=60) of 30 healthy volunteers received single 8-µL microdoses of 0.005% latanoprost (0.4 µg; µRx-latanoprost) on the morning of Days 1 and 2 using a high-precision, piezo-print horizontal delivery system. Diurnal IOP was measured before and 2 days after microdosing. Main efficacy outcomes were diurnal IOP change after µRx-latanoprost microdosing and accurate microdosing success rates, and the primary safety outcome was adverse event (AE) incidence. Results:µRx-latanoprost reduced baseline IOP by 26% and 30% at 1 and 2 days postadministration, respectively. Successful topical dosing was achieved in 100% of technician-assisted deliveries. All patients successfully self-administered microdoses after receiving training. Microdose administration was well tolerated and did not result in any AEs. Conclusion:Microdosing of 0.4 µg of µRx-latanoprost achieved significant IOP reduction. Lower ocular exposure with topical prostaglandin analog microdosing can enable new therapeutic opportunities for optimizing glaucoma treatment. Microdosing may also be beneficial in reducing ocular side effects associated with excessive drug product and preservatives often used to treat chronic ocular diseases such as glaucoma

Multi-Domain Adversarial Feature Generalization for Person Re-Identification

With the assistance of sophisticated training methods applied to single labeled datasets, the performance of fully-supervised person re-identification (Person Re-ID) has been improved significantly in recent years. However, these models trained on a single dataset usually suffer from considerable performance degradation when applied to videos of a different camera network. To make Person Re-ID systems more practical and scalable, several cross-dataset domain adaptation methods have been proposed, which achieve high performance without the labeled data from the target domain. However, these approaches still require the unlabeled data of the target domain during the training process, making them impractical. A practical Person Re-ID system pre-trained on other datasets should start running immediately after deployment on a new site without having to wait until sufficient images or videos are collected and the pre-trained model is tuned. To serve this purpose, in this paper, we reformulate person re-identification as a multi-dataset domain generalization problem. We propose a multi-dataset feature generalization network (MMFA-AAE), which is capable of learning a universal domain-invariant feature representation from multiple labeled datasets and generalizing it to `unseen' camera systems. The network is based on an adversarial auto-encoder to learn a generalized domain-invariant latent feature representation with the Maximum Mean Discrepancy (MMD) measure to align the distributions across multiple domains. Extensive experiments demonstrate the effectiveness of the proposed method. Our MMFA-AAE approach not only outperforms most of the domain generalization Person Re-ID methods, but also surpasses many state-of-the-art supervised methods and unsupervised domain adaptation methods by a large margin.Comment: TIP (Accept with Mandatory Minor Revisions

Effects of Residue Background Events in Direct Dark Matter Detection Experiments on the Determination of the WIMP Mass

In the earlier work on the development of a model-independent data analysis method for determining the mass of Weakly Interacting Massive Particles (WIMPs) by using measured recoil energies from direct Dark Matter detection experiments directly, it was assumed that the analyzed data sets are background-free, i.e., all events are WIMP signals. In this article, as a more realistic study, we take into account a fraction of possible residue background events, which pass all discrimination criteria and then mix with other real WIMP-induced events in our data sets. Our simulations show that, for the determination of the WIMP mass, the maximal acceptable fraction of residue background events in the analyzed data sets of O(50) total events is ~20%, for background windows of the entire experimental possible energy ranges, or in low energy ranges; while, for background windows in relatively higher energy ranges, this maximal acceptable fraction of residue background events can not be larger than ~10%. For a WIMP mass of 100 GeV with 20% background events in the windows of the entire experimental possible energy ranges, the reconstructed WIMP mass and the 1-sigma statistical uncertainty are ~97 GeV^{+61%}_{-35%} (~94 GeV^{+55%}_{-33%} for background-free data sets).Comment: 27 pages, 22 eps figures; v2: revised version for publication, references added and update