Order-free Learning Alleviating Exposure Bias in Multi-label Classification

Multi-label classification (MLC) assigns multiple labels to each sample. Prior studies show that MLC can be transformed to a sequence prediction problem with a recurrent neural network (RNN) decoder to model the label dependency. However, training a RNN decoder requires a predefined order of labels, which is not directly available in the MLC specification. Besides, RNN thus trained tends to overfit the label combinations in the training set and have difficulty generating unseen label sequences. In this paper, we propose a new framework for MLC which does not rely on a predefined label order and thus alleviates exposure bias. The experimental results on three multi-label classification benchmark datasets show that our method outperforms competitive baselines by a large margin. We also find the proposed approach has a higher probability of generating label combinations not seen during training than the baseline models. The result shows that the proposed approach has better generalization capability

Sample based Explanations via Generalized Representers

We propose a general class of sample based explanations of machine learning models, which we term generalized representers. To measure the effect of a training sample on a model's test prediction, generalized representers use two components: a global sample importance that quantifies the importance of the training point to the model and is invariant to test samples, and a local sample importance that measures similarity between the training sample and the test point with a kernel. A key contribution of the paper is to show that generalized representers are the only class of sample based explanations satisfying a natural set of axiomatic properties. We discuss approaches to extract global importances given a kernel, and also natural choices of kernels given modern non-linear models. As we show, many popular existing sample based explanations could be cast as generalized representers with particular choices of kernels and approaches to extract global importances. Additionally, we conduct empirical comparisons of different generalized representers on two image and two text classification datasets.Comment: Accepted by Neurips 202

Reconstructing phylogeny from metabolic substrate-product relationships

<p> Abstract</p> <p>Background</p> <p>Many approaches utilize metabolic pathway information to reconstruct the phyletic tree of fully sequenced organisms, but how metabolic networks can add information to original genomic annotations has remained open.</p> <p>Methods</p> <p>We translated enzyme reactions assigned in 1075 organisms into substrate-product relationships to represent the metabolic information at a finer resolution than enzymes and compounds. Each organism was represented as a vector of substrate-product relationships and the phyletic tree was reconstructed by a simple hierarchical method. Obtained results were compared with several other approaches that use genome information and network properties.</p> <p>Results</p> <p>Phyletic trees without consideration of network properties can already extract organisms in anomalous environments. This efficient method can add insights to traditional genome-based phylogenetic reconstruction.</p> <p>Conclusions</p> <p>Structural relationship among metabolites can highlight parasitic or symbiont species such as spirochaete and clamydia. The method assists understanding of species-environment interaction when used in combination with traditional phylogenetic methods.</p

Seismic Performance and Application of Sandwiched Buckling-Restrained Braces and Dual-Core Self-Centering Braces

This paper first presents cyclic test results  and  the  application  of  the proposed sandwiched buckling-restrained brace (BRB).  The proposed BRB can be easily  disassembled  in the field.  This  provides  an opportunity for inspection of  the  core  after  a  large  earthquake.  The  mechanics  and  cyclic  behavior  of  a novel  steel  dual-core  self-centering  brace  (SCB)  are  then  proposed  and introduced, followed by  the  testing of  a  dual-core SCB  in order  to evaluate  its cyclic  performance.  Both  braces  achieve  an  excellent  target  lateral  drift performance  of  up to 2.5%,  thus  satisfying  the seismic requirement by  the  AISC Seismic Provisions 2010

Effect of frying process on fatty acid composition and iodine value of selected vegetable oils and their blends

The main objective of the present study was to investigate the effects of the frying media and storage time on the fatty acid composition (FAC) and iodine value (IV) of deep-fat fried potato chips. The frying experiment was conducted at 180ºC for five consecutive days. Six frying media were considered as the main treatments: refined, bleached, deodorized (RBD) palm olein (A), canola oil (C), RBD palm olein/sesame oil (AB, 1:1 w/w), RBD palm olein/canola oil (AC, 1:1, w/w), sesame oil/canola oil (BC, 1:1, w/w), and RBD palm olein/sesame oil/canola oil (ABC, 1:1:1, w/w/w). The initial degrees of unsaturation of the consumed oils, A, C, AB, AC, BC, and ABC, were 58.6, 94.0, 68.0, 72.2, 87.7, and 75.8 (g/100 g), respectively. The fatty acid analysis showed that there was a decrease in both the linolenic acid (C18:3) and linoleic acid (C18:2) contents, whereas the palmitic acid (C16:0) increased with a prolonged frying time. The chemical analysis showed that there was a significant (p < 0.05) difference in terms of the IV for each frying oil during the five consecutive days of frying (day 0 to 5). Oil C had the least stability in terms of deep-fat frying due to a high level of unsaturated fatty acids. Conversely, oil AC had the best stability due to the smallest reduction of the C18:2/C16:0 ratio and the IV

Cellular Automaton for Simulation of Oxide Layer Growth Influenced by Chromium Concentration of Structure Material

Chromium, an important alloying element, has been added in ferrous and nickel based alloy such as stainless steels and Inconel alloy to improve the corrosion resistance. High corrosion resistance of structural materials in extremely high working temperature is one crucial R&D objective of Gen IV nuclear power plants which propose to raise the thermal efficiency via high working temperature. A cellular automaton (CA) model based on the stochastic approach was proposed to simulate the process of oxidation and corrosion of structural material in flowing fluid. The relation of chromium concentration against oxide layer thickness during a specific period was found. The material containing a specific amount of chromium content shows the thinnest oxide layer on its surface, which shows the strongest ability of corrosion resistance The result of simulation is close to that of experiments, which demonstrates that the CA model will have potential to achieve the goal of this kind of study. Moreover, it not only brings the benefit to save considerably experimental time and resources but also helps researchers to find out the optimized chromium content for the best corrosion resistance

Lifespan-increasing drug nordihydroguaiaretic acid inhibits p300 and activates autophagy.

Aging is characterized by the progressive loss of physiological function in all organisms. Remarkably, the aging process can be modulated by environmental modifications, including diet and small molecules. The natural compound nordihydroguaiaretic acid (NDGA) robustly increases lifespan in flies and mice, but its mechanism of action remains unclear. Here, we report that NDGA is an inhibitor of the epigenetic regulator p300. We find that NDGA inhibits p300 acetyltransferase activity in vitro and suppresses acetylation of a key p300 target in histones (i.e., H3K27) in cells. We use the cellular thermal shift assay to uniquely demonstrate NDGA binding to p300 in cells. Finally, in agreement with recent findings indicating that p300 is a potent blocker of autophagy, we show that NDGA treatment induces autophagy. These findings identify p300 as a target of NDGA and provide mechanistic insight into its role in longevity