Few-shot Learning with Multi-scale Self-supervision

Learning concepts from the limited number of datapoints is a challenging task usually addressed by the so-called one- or few-shot learning. Recently, an application of second-order pooling in few-shot learning demonstrated its superior performance due to the aggregation step handling varying image resolutions without the need of modifying CNNs to fit to specific image sizes, yet capturing highly descriptive co-occurrences. However, using a single resolution per image (even if the resolution varies across a dataset) is suboptimal as the importance of image contents varies across the coarse-to-fine levels depending on the object and its class label e. g., generic objects and scenes rely on their global appearance while fine-grained objects rely more on their localized texture patterns. Multi-scale representations are popular in image deblurring, super-resolution and image recognition but they have not been investigated in few-shot learning due to its relational nature complicating the use of standard techniques. In this paper, we propose a novel multi-scale relation network based on the properties of second-order pooling to estimate image relations in few-shot setting. To optimize the model, we leverage a scale selector to re-weight scale-wise representations based on their second-order features. Furthermore, we propose to a apply self-supervised scale prediction. Specifically, we leverage an extra discriminator to predict the scale labels and the scale discrepancy between pairs of images. Our model achieves state-of-the-art results on standard few-shot learning datasets

Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learning

The majority of existing few-shot learning methods describe image relations with binary labels. However, such binary relations are insufficient to teach the network complicated real-world relations, due to the lack of decision smoothness. Furthermore, current few-shot learning models capture only the similarity via relation labels, but they are not exposed to class concepts associated with objects, which is likely detrimental to the classification performance due to underutilization of the available class labels. To paraphrase, children learn the concept of tiger from a few of actual examples as well as from comparisons of tiger to other animals. Thus, we hypothesize that in fact both similarity and class concept learning must be occurring simultaneously. With these observations at hand, we study the fundamental problem of simplistic class modeling in current few-shot learning methods. We rethink the relations between class concepts, and propose a novel Absolute-relative Learning paradigm to fully take advantage of label information to refine the image representations and correct the relation understanding in both supervised and unsupervised scenarios. Our proposed paradigm improves the performance of several the state-of-the-art models on publicly available datasets.Comment: IEEE/CVF Conference on Computer Vision and Pattern Recognition 202

Hydrogen Clouds before Reionization: a Lognormal Model Approach

We study the baryonic gas clouds (the IGM) in the universe before the reionization with the lognormal model which is shown to be dynamcially legitimate in describing the fluctuation evolution in quasilinear as well as nonlinear regimes in recent years. The probability distribution function of the mass field in the LN model is long tailed and so plays an important role in rare events, such as the formation of the first generation of baryonic objects. We calculate density and velocity distributions of the IGM at very high spatial resolutions, and simulate the distributions at resolution of 0.15 kpc from z=7 to 15 in the LCDM cosmological model. We performed a statistics of the hydrogen clouds including column densities, clumping factors, sizes, masses, and spatial number density etc. One of our goals is to identify which hydrogen clouds are going to collapse. By inspecting the mass density profile and the velocity profile of clouds, we found that the velocity outflow significantly postpones the collapsing process in less massive clouds, in spite of their masses are larger than the Jeans mass. Consequently, only massive (> 10^5 M_sun) clouds can form objects at higher redshift, and less massive (10^4-10^5) collapsed objects are formed later. For example, although the mass fraction in clouds with sizes larger than the Jeans length is already larger than 1 at z=15, there is only a tiny fraction of mass (10^{-8}) in the clouds which are collapsed at that time. If all the ionizing photons, and the 10^{-2} metallicity observed at low redshift are produced by the first 1% mass of collapsed baryonic clouds, the majority of those first generation objects would not happen until z=10.Comment: Paper in AAStex, 12 figure

111In-Labeled Cystine-Knot Peptides Based on the Agouti-Related Protein for Targeting Tumor Angiogenesis

Agouti-related protein (AgRP) is a 4-kDa cystine-knot peptide of human origin with four disulfide bonds and four solvent-exposed loops. The cell adhesion receptor integrin αvβ3 is an important tumor angiogenesis factor that determines the invasiveness and metastatic ability of many malignant tumors. AgRP mutants have been engineered to bind to integrin αvβ3 with high affinity and specificity using directed evolution. Here, AgRP mutants 7C and 6E were radiolabeled with 111In and evaluated for in vivo targeting of tumor integrin αvβ3 receptors. AgRP peptides were conjugated to the metal chelator 1, 4, 7, 10-tetra-azacyclododecane- N, N′, N″, N‴-tetraacetic acid (DOTA) and radiolabeled with 111In. The stability of the radiopeptides 111In-DOTA-AgRP-7C and 111In-DOTA-AgRP-6E was tested in phosphate-buffered saline (PBS) and mouse serum, respectively. Cell uptake assays of the radiolabeled peptides were performed in U87MG cell lines. Biodistribution studies were performed to evaluate the in vivo performance of the two resulting probes using mice bearing integrin-expressing U87MG xenograft tumors. Both AgRP peptides were easily labeled with 111In in high yield and radiochemical purity (>99%). The two probes exhibited high stability in phosphate-buffered saline and mouse serum. Compared with 111In-DOTA-AgRP-6E, 111In-DOTA-AgRP-7C showed increased U87MG tumor uptake and longer tumor retention (5.74 ± 1.60 and 1.29 ± 0.02%ID/g at 0.5 and 24 h, resp.), which was consistent with measurements of cell uptake. Moreover, the tumor uptake of 111In-DOTA-AgRP-7C was specifically inhibited by coinjection with an excess of the integrin-binding peptidomimetic c(RGDyK). Thus, 111In-DOTA-AgRP-7C is a promising probe for targeting integrin αvβ3 positive tumors in living subjects

Epigenetic memory in induced pluripotent stem cells.

Somatic cell nuclear transfer and transcription-factor-based reprogramming revert adult cells to an embryonic state, and yield pluripotent stem cells that can generate all tissues. Through different mechanisms and kinetics, these two reprogramming methods reset genomic methylation, an epigenetic modification of DNA that influences gene expression, leading us to hypothesize that the resulting pluripotent stem cells might have different properties. Here we observe that low-passage induced pluripotent stem cells (iPSCs) derived by factor-based reprogramming of adult murine tissues harbour residual DNA methylation signatures characteristic of their somatic tissue of origin, which favours their differentiation along lineages related to the donor cell, while restricting alternative cell fates. Such an 'epigenetic memory' of the donor tissue could be reset by differentiation and serial reprogramming, or by treatment of iPSCs with chromatin-modifying drugs. In contrast, the differentiation and methylation of nuclear-transfer-derived pluripotent stem cells were more similar to classical embryonic stem cells than were iPSCs. Our data indicate that nuclear transfer is more effective at establishing the ground state of pluripotency than factor-based reprogramming, which can leave an epigenetic memory of the tissue of origin that may influence efforts at directed differentiation for applications in disease modelling or treatment

A narrow line Seyfert 1--blazar composite nucleus in 2MASX J0324+3410

We report the identification of 2MASX J032441.19+341045.9 (hereafter 2MASX J0324+3410) with an appealing object which shows the dual properties of both a narrow line Seyfert 1 galaxy (NLS1) and a blazar. Its optical spectrum, which has a H\beta line width about 1600 km s^-1 (FWHM), an [OIII] to H\beta line ratio ~0.12, and strong FeII emission, clearly fulfills the conventional definition of NLS1s. On the other hand, 2MASX J0324+3410 also exhibits some behavior which is characteristic of blazars, including a flat radio spectrum above 1 GHz, a compact core plus a one-sided jet structure on mas-scale at 8.4 GHz, highly variable fluxes in the radio, optical, and X-ray bands, and a possible detection of TeV gamma-ray emission. On its optical image, obtained with the HST WFPC2, the active nucleus is displaced from the center of the host galaxy, which exhibits an apparent one-armed spiral structure extended to 16 kpc. The remarkable hybrid behavior of this object presents a challenge to current models of NLS1 galaxies and $\gamma$-ray blazars.Comment: 12 pages, 2 figures. Accepted to ApJ

Treatment outcomes of fixed-dose combination versus separate tablet regimens in pulmonary tuberculosis patients with or without diabetes in Qatar

Background: Tuberculosis is considered the second most common cause of death due to infectious agent. The currently preferred regimen for treatment of pulmonary tuberculosis (PTB) is isoniazid, rifampin, pyrazinamide, and ethambutol, which has been used either as separate tablets (ST) or as fixed-dose combination (FDC). To date, no studies have compared both regimens in Qatar. We aim to evaluate the safety and effectiveness of FDC and ST regimen for treating PTB, in addition to comparing safety and efficacy of FDC and ST regimens in patients with diabetes treated for TB. Methods: A retrospective observational study was conducted in two general hospitals in Qatar. Patients diagnosed with PTB received anti-tuberculosis medications (either as FDC or ST) administered by the nurse. Sputum smears were tested weekly. We assessed the time to negative sputum smear and incidence of adverse events among FDC and ST groups. Results: The study included 148 patients. FDC was used in 90 patients (61%). Effectiveness was not different between FDC and ST regimens as shown by mean time to sputum conversion (29.9 ± 18.3 vs. 35.6 ± 23 days, p = 0.12). Similarly, there was no difference in the incidence of adverse events, except for visual one that was higher in ST group. Among the 33 diabetic patients, 19 received the FDC and had faster sputum conversion compared to those who received ST (31 ± 12 vs. 49.4 ± 30.9 days, p = 0.05). Overall, diabetic patients needed longer time for sputum conversion and had more hepatotoxic and gastric adverse events compared to non-diabetics. Conclusion: ST group had higher visual side effects compared to FDC. FDC may be more effective in diabetic patients; however, further studies are required to confirm such finding.PublishedN/