S-OHEM: Stratified Online Hard Example Mining for Object Detection

One of the major challenges in object detection is to propose detectors with highly accurate localization of objects. The online sampling of high-loss region proposals (hard examples) uses the multitask loss with equal weight settings across all loss types (e.g, classification and localization, rigid and non-rigid categories) and ignores the influence of different loss distributions throughout the training process, which we find essential to the training efficacy. In this paper, we present the Stratified Online Hard Example Mining (S-OHEM) algorithm for training higher efficiency and accuracy detectors. S-OHEM exploits OHEM with stratified sampling, a widely-adopted sampling technique, to choose the training examples according to this influence during hard example mining, and thus enhance the performance of object detectors. We show through systematic experiments that S-OHEM yields an average precision (AP) improvement of 0.5% on rigid categories of PASCAL VOC 2007 for both the IoU threshold of 0.6 and 0.7. For KITTI 2012, both results of the same metric are 1.6%. Regarding the mean average precision (mAP), a relative increase of 0.3% and 0.5% (1% and 0.5%) is observed for VOC07 (KITTI12) using the same set of IoU threshold. Also, S-OHEM is easy to integrate with existing region-based detectors and is capable of acting with post-recognition level regressors.Comment: 9 pages, 3 figures, accepted by CCCV 201

TandemNet: Distilling Knowledge from Medical Images Using Diagnostic Reports as Optional Semantic References

In this paper, we introduce the semantic knowledge of medical images from their diagnostic reports to provide an inspirational network training and an interpretable prediction mechanism with our proposed novel multimodal neural network, namely TandemNet. Inside TandemNet, a language model is used to represent report text, which cooperates with the image model in a tandem scheme. We propose a novel dual-attention model that facilitates high-level interactions between visual and semantic information and effectively distills useful features for prediction. In the testing stage, TandemNet can make accurate image prediction with an optional report text input. It also interprets its prediction by producing attention on the image and text informative feature pieces, and further generating diagnostic report paragraphs. Based on a pathological bladder cancer images and their diagnostic reports (BCIDR) dataset, sufficient experiments demonstrate that our method effectively learns and integrates knowledge from multimodalities and obtains significantly improved performance than comparing baselines.Comment: MICCAI2017 Ora

Finite Symmetry of Leptonic Mass Matrices

We search for possible symmetries present in the leptonic mixing data from SU(3) subgroups of order up to 511. Theoretical results based on symmetry are compared with global fits of experimental data in a chi-squared analysis, yielding the following results. There is no longer a group that can produce all the mixing data without a free parameter, but a number of them can accommodate the first or the second column of the mixing matrix. The only group that fits the third column is $\Delta(150)$. It predicts $\sin^22\theta_{13}=0.11$ and $\sin^22\theta_{23}=0.94$, in good agreement with experimental results.Comment: Version to appear in Physical Review

Giant Colloidal Diffusivity on Corrugated Optical Vortices

A single colloidal sphere circulating around a periodically modulated optical vortex trap can enter a dynamical state in which it intermittently alternates between freely running around the ring-like optical vortex and becoming trapped in local potential energy minima. Velocity fluctuations in this randomly switching state still are characterized by a linear Einstein-like diffusion law, but with an effective diffusion coefficient that is enhanced by more than two orders of magnitude.Comment: 4 pages, 4 figure

SU(3) Predictions of B→PPB\to PP Decays in the Standard Model

With SU(3) symmetry one only needs 13 hadronic parameters to describe $B\to PP$ decays in the Standard Model. When annihilation contributions are neglected, only 7 hadronic parameters are needed. These parameters can be determined from existing experimental data and some unmeasured branching ratios and CP asymmetries of the type $B\to PP$ can be predicted. In this talk we present SU(3) predictions of branching ratios and CP asymmetries for $B\to PP$ decays in the Standard Model.Comment: 4 pages, no figure. Talk present at the 5th International Conference on Hyperons, Charm and Beauty Hadrons, Vancouver, June 200

Filamentary superconductivity across the phase diagram of Ba(Fe,Co)2_2As2_2

We show magnetotransport results on Ba(Fe$_{1-x}$Co$_x$)$_2$As$_2$ ($0.0 \leq x \leq 0.13$) single crystals. We identify the low temperature resistance step at 23 K in the parent compound with the onset of filamentary superconductivity (FLSC), which is suppressed by an applied magnetic field in a similar manner to the suppression of bulk superconductivity (SC) in doped samples. FLSC is found to persist across the phase diagram until the long range antiferromagnetic order is completely suppressed. A significant suppression of FLSC occurs for $0.02<x<0.04$, the doping concentration where bulk SC emerges. Based on these results and the recent report of an electronic anisotropy maximum for 0.02 $\leq x \leq$ 0.04 [Science 329, 824 (2010)], we speculate that, besides spin fluctuations, orbital fluctuations may also play an important role in the emergence of SC in iron-based superconductors.Comment: 5 pages, 3 figure

Machine-learning the Sato-Tate conjecture

We apply some of the latest techniques from machine-learning to the arithmetic of hyperelliptic curves. More precisely we show that, with impressive accuracy and confidence (between 99 and 100 percent precision), and in very short time (matter of seconds on an ordinary laptop), a Bayesian classifier can distinguish between Sato–Tate groups given a small number of Euler factors for the L-function. Our observations are in keeping with the Sato-Tate conjecture for curves of low genus. For elliptic curves, this amounts to distinguishing generic curves (with Sato–Tate group SU(2)) from those with complex multiplication. In genus 2, a principal component analysis is observed to separate the generic Sato–Tate group USp(4) from the non-generic groups. Furthermore in this case, for which there are many more non-generic possibilities than in the case of elliptic curves, we demonstrate an accurate characterisation of several Sato–Tate groups with the same identity component. Throughout, our observations are verified using known results from the literature and the data available in the LMFDB. The results in this paper suggest that a machine can be trained to learn the Sato–Tate distributions and may be able to classify curves efficiently

Machine learning invariants of arithmetic curves

We show that standard machine learning algorithms may be trained to predict certain invariants of low genus arithmetic curves. Using datasets of size around 105, we demonstrate the utility of machine learning in classification problems pertaining to the BSD invariants of an elliptic curve (including its rank and torsion subgroup), and the analogous invariants of a genus 2 curve. Our results show that a trained machine can efficiently classify curves according to these invariants with high accuracies (>0.97). For problems such as distinguishing between torsion orders, and the recognition of integral points, the accuracies can reach 0.998