How Re-sampling Helps for Long-Tail Learning?

Long-tail learning has received significant attention in recent years due to the challenge it poses with extremely imbalanced datasets. In these datasets, only a few classes (known as the head classes) have an adequate number of training samples, while the rest of the classes (known as the tail classes) are infrequent in the training data. Re-sampling is a classical and widely used approach for addressing class imbalance issues. Unfortunately, recent studies claim that re-sampling brings negligible performance improvements in modern long-tail learning tasks. This paper aims to investigate this phenomenon systematically. Our research shows that re-sampling can considerably improve generalization when the training images do not contain semantically irrelevant contexts. In other scenarios, however, it can learn unexpected spurious correlations between irrelevant contexts and target labels. We design experiments on two homogeneous datasets, one containing irrelevant context and the other not, to confirm our findings. To prevent the learning of spurious correlations, we propose a new context shift augmentation module that generates diverse training images for the tail class by maintaining a context bank extracted from the head-class images. Experiments demonstrate that our proposed module can boost the generalization and outperform other approaches, including class-balanced re-sampling, decoupled classifier re-training, and data augmentation methods. The source code is available at https://www.lamda.nju.edu.cn/code_CSA.ashx.Comment: Accepted by NeurIPS 202

An industry case of large-scale demand forecasting of hierarchical components

Demand forecasting of hierarchical components is essential in manufacturing. However, its discussion in the machine-learning literature has been limited, and judgemental forecasts remain pervasive in the industry. Demand planners require easy-to-understand tools capable of delivering state-of-the-art results. This work presents an industry case of demand forecasting at one of the largest manufacturers of electronics in the world. It seeks to support practitioners with five contributions: (1) A benchmark of fourteen demand forecast methods applied to a relevant data set, (2) A data transformation technique yielding comparable results with state of the art, (3) An alternative to ARIMA based on matrix factorization, (4) A model selection technique based on topological data analysis for time series and (5) A novel data set. Organizations seeking to up-skill existing personnel and increase forecast accuracy will find value in this work

A tau fragment links depressive-like behaviors and cognitive declines in Alzheimer’s disease mouse models through attenuating mitochondrial function

IntroductionAlzheimer’s disease (AD) is the most prevalent neurodegenerative disease characterized by extracellular senile plaques including amyloid-β peptides and intracellular neurofibrillary tangles consisting of abnormal Tau. Depression is one of the most common neuropsychiatric symptoms in AD, and clinical evidence demonstrates that depressive symptoms accelerate the cognitive deficit of AD patients. However, the underlying molecular mechanisms of depressive symptoms present in the process of AD remain unclear.MethodsDepressive-like behaviors and cognitive decline in hTau mice were induced by chronic restraint stress (CRS). Computational prediction and molecular experiments supported that an asparagine endopeptidase (AEP)-derived Tau fragment, Tau N368 interacts with peroxisome proliferator-activated receptor delta (PPAR-δ). Further behavioral studies investigated the role of Tau N368-PPAR-δ interaction in depressive-like behaviors and cognitive declines of AD models exposed to CRS.ResultsWe found that mitochondrial dysfunction was positively associated with depressive-like behaviors and cognitive deficits in hTau mice. Chronic stress increased Tau N368 and promoted the interaction of Tau N368 with PPAR-δ, repressing PPAR-δ–mediated transactivation in the hippocampus of mice. Then we predicted and identified the binding sites of PPAR-δ. Finally, inhibition of AEP, clearance of Tau N368 and pharmacological activation of PPAR-δ effectively alleviated CRS-induced depressive-like behaviors and cognitive decline in mice.ConclusionThese results demonstrate that Tau N368 in the hippocampus impairs mitochondrial function by suppressing PPAR-δ, facilitating the occurrence of depressive-like behaviors and cognitive decline. Therefore, our findings may provide new mechanistic insight in the pathophysiology of depression-like phenotype in mouse models of Alzheimer’s disease

Identification of conserved and novel microRNAs that are responsive to heat stress in Brassica rapa

The species Brassica rapa includes various vegetable crops. Production of these vegetable crops is usually impaired by heat stress. Some microRNAs (miRNAs) in Arabidopsis have been considered to mediate gene silencing in plant response to abiotic stress. However, it remains unknown whether or what miRNAs play a role in heat resistance of B. rapa. To identify genomewide conserved and novel miRNAs that are responsive to heat stress in B. rapa, we defined temperature thresholds of non-heading Chinese cabbage (B. rapa ssp. chinensis) and constructed small RNA libraries from the seedlings that had been exposed to high temperature (46 °C) for 1 h. By deep sequencing and data analysis, we selected a series of conserved and novel miRNAs that responded to heat stress. In total, Chinese cabbage shares at least 35 conserved miRNA families with Arabidopsis thaliana. Among them, five miRNA families were responsive to heat stress. Northern hybridization and real-time PCR showed that the conserved miRNAs bra-miR398a and bra-miR398b were heat-inhibitive and guided heat response of their target gene, BracCSD1; and bra-miR156h and bra-miR156g were heat-induced and its putative target BracSPL2 was down-regulated. According to the criteria of miRNA and miRNA* that form a duplex, 21 novel miRNAs belonging to 19 miRNA families were predicted. Of these, four were identified to be heat-responsive by Northern blotting and/or expression analysis of the putative targets. The two novel miRNAs bra-miR1885b.3 and bra-miR5718 negatively regulated their putative target genes. 5′-Rapid amplification of cDNA ends PCR indicated that three novel miRNAs cleaved the transcripts of their target genes where their precursors may have evolved from. These results broaden our perspective on the important role of miRNA in plant responses to heat

Index modulated OFDM with interleaved grouping for V2X communications

The rapid penetration of intelligent transportation systems (ITS) into the conventional transportation infrastructure urgently calls for high spectral efficiency high reliability communication technology for vehicle-to-vehicle and vehicle-to-infrastructure (V2X) applications. Since orthogonal frequency division multiplexing (OFDM) is widely considered as a promising candidate for such applications, in this paper we propose a novel variation of OFDM for improved spectral efficiency as well as enhanced reliability in V2X channels with correlated frequency-selective fading and inevitable Doppler effects. Our proposed scheme is built upon a recently emerging technique termed as index modulated (IM-) OFDM. However, different from the existing localized subcarrier grouping, we propose interleaved subcarrier grouping. We then carry out analytical and simulated comparisons to demonstrate the merits of this new scheme in terms of both the bit error rate (BER) performance and the maximum achievable rate (MAR) of the overall system, in V2X channels.EICPCI-S(ISTP)[email protected]; [email protected]; [email protected]; [email protected]

QTL mapping of leafy heads by genome resequencing in the RIL population of Brassica rapa.

Leaf heads of cabbage (Brassica oleracea), Chinese cabbage (B. rapa), and lettuce (Lactuca sativa) are important vegetables that supply mineral nutrients, crude fiber and vitamins in the human diet. Head size, head shape, head weight, and heading time contribute to yield and quality. In an attempt to investigate genetic basis of leafy head in Chinese cabbage (B. rapa), we took advantage of recent technical advances of genome resequencing to perform quantitative trait locus (QTL) mapping using 150 recombinant inbred lines (RILs) derived from the cross between heading and non-heading Chinese cabbage. The resequenced genomes of the parents uncovered more than 1 million SNPs. Genotyping of RILs using the high-quality SNPs assisted by Hidden Markov Model (HMM) generated a recombination map. The raw genetic map revealed some physical assembly error and missing fragments in the reference genome that reduced the quality of SNP genotyping. By deletion of the genetic markers in which recombination rates higher than 20%, we have obtained a high-quality genetic map with 2209 markers and detected 18 QTLs for 6 head traits, from which 3 candidate genes were selected. These QTLs provide the foundation for study of genetic basis of leafy heads and the other complex traits

Time History Method of Three-Dimensional Dynamic Stability Analysis for High Earth-Rockfill Dam and Its Application

Accurately grasping the stability characteristics of high earth-rockfill dam slopes is the key to the seismic safety evaluation of dams. In this research, the development and application of the common methods for slope stability analysis are reviewed firstly. Then, a three-dimensional dynamic time history stability analysis method is presented, and corresponding software is developed based on the sliding surface finite element stress method combined with the three-dimensional finite element dynamic response. This method makes the three-dimensional dynamic stability analysis efficient, and the effectiveness of this software is verified. Finally, the two-dimensional (2D) and three-dimensional (3D) dynamic stability analyses of a high concrete face dam are carried out, and the stability of the dam’s downstream slope under seismic load is studied. The results indicate that there are many differences between the results of the traditional 2D and 3D stability analyses. The time history of the safety factor, local safety behavior, overall shape and spatial position of the potential sliding body, and even the sliding process of failure can be captured with 3D stability analysis