Multiple-Question Multiple-Answer Text-VQA

We present Multiple-Question Multiple-Answer (MQMA), a novel approach to do text-VQA in encoder-decoder transformer models. The text-VQA task requires a model to answer a question by understanding multi-modal content: text (typically from OCR) and an associated image. To the best of our knowledge, almost all previous approaches for text-VQA process a single question and its associated content to predict a single answer. In order to answer multiple questions from the same image, each question and content are fed into the model multiple times. In contrast, our proposed MQMA approach takes multiple questions and content as input at the encoder and predicts multiple answers at the decoder in an auto-regressive manner at the same time. We make several novel architectural modifications to standard encoder-decoder transformers to support MQMA. We also propose a novel MQMA denoising pre-training task which is designed to teach the model to align and delineate multiple questions and content with associated answers. MQMA pre-trained model achieves state-of-the-art results on multiple text-VQA datasets, each with strong baselines. Specifically, on OCR-VQA (+2.5%), TextVQA (+1.4%), ST-VQA (+0.6%), DocVQA (+1.1%) absolute improvements over the previous state-of-the-art approaches

Study on the Performance of CO2 Two-stage Rotary Compressor in Freezing and Cold Storage Conditions

This paper describes a new type CO2 two-stage rotary compressor for cold storage and freezing of food. A two-stage compression form with an upper cylinder (first-stage) and a lower cylinder (second-stage), unique oil road structures and technical parameters have been used in the rotary compressor to increase the performance. The results indicating that the optimized CO2 two-stage rotary compressor has a significant performance advantage, which the coefficient of performance (COP) increases by 4.4% ~ 6.7%

Fundamentals or Population Dynamics and the Geographic Distribution of U.S. Biotechnology Enterprises, 1976-1989

Population ecology models are elegant in form and adequate in describing aggregate data, but poor in telling stories and predicting the location of growth. Fundamentals models emphasizing the variables central to resource mobilization, such as intellectual human capital, can predict where and when biotechnology enterprises emerge and agglomerate. Density dependence and previous founding dependence proxy many underlying processes; the legitimation and competition interpretation is more conjectural than empirically tenable. We argue and demonstrate for biotechnology that an alternative model based on the fundamentals related to resource reallocation and mobilization provides a stronger frame to explore industry formation. Fundamentals models outperform population ecology models in the estimations, while a combined model driven by fundamentals but incorporating weak population dynamics does best. In repeated dynamic simulations, the population ecology model predictions are essentially uncorrelated with the panel data on biotechnology entry by year and region while the combined model has correlation coefficients averaging above 0.8.

O-Band Subwavelength Grating Filters in a Monolithic Photonics Technology

The data communications industry has begun transitioning from electrical to optical interconnects in datacenters in order to overcome performance bottlenecks and meet consumer needs. To mitigate the costs associated with this change and achieve performance for 5G and beyond, it is crucial to explore advanced photonic devices that can enable high-bandwidth interconnects via wavelength-division multiplexing (WDM) in photonic integrated circuits. Subwavelength grating (SWG) filters have shown great promise for WDM applications. However, the small feature sizes necessary to implement these structures have prohibited them from penetrating into industrial applications. To explore the manufacturability and performance of SWG filters in an industrial setting, we fabricate and characterize O-band subwavelength grating filters using the monolithic photonics technology at GLOBALFOUNDRIES (GF). We demonstrate a low drop channel loss of -1.2 dB with a flat-top response, a high extinction ratio of -30 dB, a 3 dB channel width of 5 nm and single-source thermal tunability without shape distortion. This filter structure was designed using elements from the product design kit provided by GF and functions in a compact footprint of 0.002 mm2 with a minimum feature size of 150 nm.Comment: 4 pages, 3 figure

Exploring Universal Intrinsic Task Subspace via Prompt Tuning

Why can pre-trained language models (PLMs) learn universal representations and effectively adapt to broad NLP tasks differing a lot superficially? In this work, we empirically find evidence indicating that the adaptations of PLMs to various few-shot tasks can be reparameterized as optimizing only a few free parameters in a unified low-dimensional intrinsic task subspace, which may help us understand why PLMs could easily adapt to various NLP tasks with small-scale data. To find such a subspace and examine its universality, we propose an analysis pipeline called intrinsic prompt tuning (IPT). Specifically, we resort to the recent success of prompt tuning and decompose the soft prompts of multiple NLP tasks into the same low-dimensional nonlinear subspace, then we learn to adapt the PLM to unseen data or tasks by only tuning parameters in this subspace. In the experiments, we study diverse few-shot NLP tasks and surprisingly find that in a 250-dimensional subspace found with 100 tasks, by only tuning 250 free parameters, we can recover 97% and 83% of the full prompt tuning performance for 100 seen tasks (using different training data) and 20 unseen tasks, respectively, showing great generalization ability of the found intrinsic task subspace. Besides being an analysis tool, IPT could further bring practical benefits, such as improving the prompt tuning stability.Comment: Withdrawn from Findings of ACL 202

Low-dose metformin reprograms the tumor immune microenvironment in human esophageal cancer:Results of a phase II clinical trial

PURPOSE: The tumor immune microenvironment (TIME) has an important impact on response to cancer immunotherapy using immune checkpoint inhibitors. Specifically, an "infiltrated-excluded"/"cold" TIME is predictive of poor response. The antidiabetic agent metformin may influence anti-cancer immunity in esophageal squamous cell carcinoma (ESCC). EXPERIMENTAL DESIGN: We analyzed matched pre- and post-treatment ESCC specimens in a phase II clinical trial of low-dose metformin treatment (250 mg/day) to evaluate direct anti-ESCC activity and TIME-reprogramming. Follow-up correlative studies using a carcinogen-induced ESCC mouse model were performed with short-term (1 week) or long-term (12 weeks) low-dose metformin (50 mg/kg/day) treatment. RESULTS: In the clinical trial, low-dose metformin did not affect proliferation or apoptosis in ESCC tumors as assayed by Ki67 and cleaved caspase-3 immunostaining. However, metformin reprogrammed the TIME towards "infiltrated-inflamed" and increased the numbers of infiltrated CD8+ cytotoxic T-lymphocyte and CD20+ B-lymphocyte. Further, an increase in tumor-suppressive (CD11c+) and a decrease in tumor-promoting (CD163+) macrophages were observed. Metformin augmented macrophage-mediated phagocytosis of ESCC cells in vitro. In ESCC mouse model, short-term metformin treatment reprogrammed the TIME in a similar fashion to humans, whereas long-term treatment further shifted the TIME towards an active state (e.g., reduction in CD4+ FoxP3+ Tregs) and inhibited ESCC growth. In both humans and mice, metformin triggered AMPK activation and STAT3 inactivation, and altered the production of effector cytokines (i.e. TNF-α, IFN-γ, IL-10) in the immune cells. CONCLUSIONS: Low-dose metformin reprograms the TIME to an activated status and may be a suitable immune response modifier for further investigation in ESCC patients

CokeBERT: Contextual Knowledge Selection and Embedding towards Enhanced Pre-Trained Language Models

Several recent efforts have been devoted to enhancing pre-trained language models (PLMs) by utilizing extra heterogeneous knowledge in knowledge graphs (KGs) and achieved consistent improvements on various knowledge-driven NLP tasks. However, most of these knowledge-enhanced PLMs embed static sub-graphs of KGs ("knowledge context"), regardless of that the knowledge required by PLMs may change dynamically according to specific text ("textual context"). In this paper, we propose a novel framework named Coke to dynamically select contextual knowledge and embed knowledge context according to textual context for PLMs, which can avoid the effect of redundant and ambiguous knowledge in KGs that cannot match the input text. Our experimental results show that Coke outperforms various baselines on typical knowledge-driven NLP tasks, indicating the effectiveness of utilizing dynamic knowledge context for language understanding. Besides the performance improvements, the dynamically selected knowledge in Coke can describe the semantics of text-related knowledge in a more interpretable form than the conventional PLMs. Our source code and datasets will be available to provide more details for Coke

Large-scale on-chip integration of gate-voltage addressable hybrid superconductor-semiconductor quantum wells field effect nano-switch arrays

Stable, reproducible, scalable, addressable, and controllable hybrid superconductor-semiconductor (S-Sm) junctions and switches are key circuit elements and building blocks of gate-based quantum processors. The electrostatic field effect produced by the split gate voltages facilitates the realisation of nano-switches that can control the conductance or current in the hybrid S-Sm circuits based on 2D semiconducting electron systems. Here, we experimentally demonstrate a novel realisation of large-scale scalable, and gate voltage controllable hybrid field effect quantum chips. Each chip contains arrays of split gate field effect hybrid junctions, that work as conductance switches, and are made from In0.75Ga0.25As quantum wells integrated with Nb superconducting electronic circuits. Each hybrid junction in the chip can be controlled and addressed through its corresponding source-drain and two global split gate contact pads that allow switching between their (super)conducting and insulating states. We fabricate a total of 18 quantum chips with 144 field effect hybrid Nb- In0.75Ga0.25As 2DEG-Nb quantum wires and investigate the electrical response, switching voltage (on/off) statistics, quantum yield, and reproducibility of several devices at cryogenic temperatures. The proposed integrated quantum device architecture allows control of individual junctions in a large array on a chip useful for the development of emerging cryogenic nanoelectronics circuits and systems for their potential applications in fault-tolerant quantum technologies