Robust Estimation and Inference for Expected Shortfall Regression with Many Regressors

Expected Shortfall (ES), also known as superquantile or Conditional Value-at-Risk, has been recognized as an important measure in risk analysis and stochastic optimization, and is also finding applications beyond these areas. In finance, it refers to the conditional expected return of an asset given that the return is below some quantile of its distribution. In this paper, we consider a recently proposed joint regression framework that simultaneously models the quantile and the ES of a response variable given a set of covariates, for which the state-of-the-art approach is based on minimizing a joint loss function that is non-differentiable and non-convex. This inevitably raises numerical challenges and limits its applicability for analyzing large-scale data. Motivated by the idea of using Neyman-orthogonal scores to reduce sensitivity with respect to nuisance parameters, we propose a statistically robust (to highly skewed and heavy-tailed data) and computationally efficient two-step procedure for fitting joint quantile and ES regression models. With increasing covariate dimensions, we establish explicit non-asymptotic bounds on estimation and Gaussian approximation errors, which lay the foundation for statistical inference. Finally, we demonstrate through numerical experiments and two data applications that our approach well balances robustness, statistical, and numerical efficiencies for expected shortfall regression

Macroautophagy in T Lymphocyte Development and Function

Macroautophagy (referred to as autophagy) is a fundamental intracellular process characterized by the sequestration of cytoplasmic compartments through double-membrane vesicles, termed autophagosomes. Recent studies have established important roles of autophagy in regulating T lymphocyte development and function. Resting T lymphocytes have basal levels of autophagy that is upregulated by T cell receptor stimulation. Several specific knockout or transgenic models have been developed during the past few years, and it has been revealed that autophagy plays an essential role in regulating thymocyte selection, peripheral T cell survival, and proliferation. The regulation of T cell development and function by autophagy is mediated through its role in regulating self-antigen presentation, intracellular organelle homeostasis, and energy production. Here we will review the current findings concerning how autophagy regulates T cell function, as well as compare different models in studying autophagy in T lymphocytes

Segatron: Segment-Aware Transformer for Language Modeling and Understanding

Transformers are powerful for sequence modeling. Nearly all state-of-the-art language models and pre-trained language models are based on the Transformer architecture. However, it distinguishes sequential tokens only with the token position index. We hypothesize that better contextual representations can be generated from the Transformer with richer positional information. To verify this, we propose a segment-aware Transformer (Segatron), by replacing the original token position encoding with a combined position encoding of paragraph, sentence, and token. We first introduce the segment-aware mechanism to Transformer-XL, which is a popular Transformer-based language model with memory extension and relative position encoding. We find that our method can further improve the Transformer-XL base model and large model, achieving 17.1 perplexity on the WikiText-103 dataset. We further investigate the pre-training masked language modeling task with Segatron. Experimental results show that BERT pre-trained with Segatron (SegaBERT) can outperform BERT with vanilla Transformer on various NLP tasks, and outperforms RoBERTa on zero-shot sentence representation learning.Comment: Accepted by AAAI 202

Policy Shaping and Generalized Update Equations for Semantic Parsing from Denotations

Semantic parsing from denotations faces two key challenges in model training: (1) given only the denotations (e.g., answers), search for good candidate semantic parses, and (2) choose the best model update algorithm. We propose effective and general solutions to each of them. Using policy shaping, we bias the search procedure towards semantic parses that are more compatible to the text, which provide better supervision signals for training. In addition, we propose an update equation that generalizes three different families of learning algorithms, which enables fast model exploration. When experimented on a recently proposed sequential question answering dataset, our framework leads to a new state-of-the-art model that outperforms previous work by 5.0% absolute on exact match accuracy.Comment: Accepted at EMNLP 201

Ringtone Regardless of P-Early-Media Tag

A user device placing a mobile originating call on a network using Real-time Transport Protocol (RTP) ignores P-Early-Media tags and session description protocol (SDP) in Session Initiation Protocol (SIP) packets received from the network after receiving a 180 Ringing alert packet indicating that the receiving device is ringing to minimize a delay in playing a ringback tone. The user device plays a local ringtone until the user device receives audio RTP packets containing Early Media. If the user device receives audio RTP packets containing Early Media before the call is connected, the user device plays the Early Media as a custom ringtone