Recent advances in vision-language pre-training have pushed the state-of-the-art on various vision-language tasks, making machines more capable of multi-modal writing (image-to-text generation) and painting (text-to-image generation). However, few studies investigate if these two essential capabilities can be learned together and boost each other, making a versatile and powerful multi-modal foundation model. In this work, we disclose the potential of symmetric generative vision-language pre-training in learning to write and paint concurrently, and propose a new unified modal model, named DaVinci, trained with prefix language modeling and prefix image modeling, a simple generative self-supervised objective on image-text pairs. Thanks to the proposed prefix multi-modal modeling framework, DaVinci is simple to train, scalable to huge data, adaptable to both writing and painting tasks, and also strong on other vision, text, and multi-modal understanding tasks. DaVinci achieves competitive performance on a wide range of 27 generation/understanding tasks and demonstrates the superiority of combining vision/language generative pre-training. Furthermore, we carefully benchmark the performance of different vision-language pre-training objectives on different scales of pre-training datasets on a heterogeneous and broad distribution coverage. Our results demonstrate the potential of exploiting self-supervision in both language and vision inputs, and establish new, stronger baselines for future comparisons at different data scales. The code and pre-trained models are available at https://github.com/shizhediao/DaVinci.

Unless you have been living under a stone, you will have heard about the new software ChatGPT, which can write your emails and project reports, and your children's essays (still not allowed, by the way!). Maybe you're excited by the possibilities it offers and its aura of'the future is here'. Could a robot that cleans your house and acts as your PA, or even your friend, be next? Before you get too carried away, here are some of the reasons why ChatGPT may not be the'Next Big Thing', and why we should handle it with care – if at all. Let's first look at what ChatGPT actually is.

Less than two weeks after the tragic earthquake that has killed more than 40,000 people in Turkey and Syria, another shake has rocked New Zealand. Wednesday's'widely felt' tremor, around magnitude 6, jolted both New Zealand's islands, although thankfully there's been no immediate reports of damage or injury. Earthquakes are happening all the time, from the ones too small to even be noticed to the devastating high magnitude quakes that lead to thousands of fatalities. But its occurrence so soon after the disaster in Turkey and Syria begs the question - could they be linked? Here, MailOnline takes a closer look at today's event and whether it's related to the catastrophic tremor in the Middle East last week.

Get the Spectroscopy and Chemometrics News Weekly in real time on Twitter @ CalibModel and follow us. "Component Prediction of Antai Pills Based on One-Dimensional Convolutional Neural Network and Near-Infrared Spectroscopy" LINK "Moisture content monitoring in withering leaves during black tea processing based on electronic eye and near infrared spectroscopy" LINK "Hyperspectral technique combined with stacking and blending ensemble learning method for detection of cadmium content in oilseed rape leaves" LINK "Capacitance spectroscopy enables realtime monitoring of early cell death in mammalian cell culture" LINK "Detection of bruised loquats based on reflectance, absorbance and Kubelka-Munk spectra" LINK "Longitudinal alterations of pulmonary [… formula…] O2 on-kinetics during moderate-intensity exercise in competitive youth cyclists are related to alterations in the …" LINK

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The tanks and trench warfare in Ukraine may seem old-school, but US experts say the conflict has provided strategic insights into future possible conflicts from the Middle East to Taiwan. They range from the mundane -- the need for bigger weapons stockpiles -- to the high-tech, with Ukraine a proving ground for artificial intelligence and robotic warfare. Ukraine has been a test for "sensor fusion," triangulating diverse sources of information to create a fuller picture of the battlefield, said Stephen Biddle, a defense expert at Columbia University. US firm Palantir has provided Kyiv with artificial intelligence-powered tools that sort through gigabytes of data to help commanders understand the war in real time: Russian troop movements, positions and targets. Drone warfare came of age in Ukraine, but now both sides are roughly matched in capabilities, and armies around the world are catching up.

The rapid adoption of facial recognition (FR) technology by both government and commercial entities in recent years has raised concerns about civil liberties and privacy. In response, a broad suite of so-called "anti-facial recognition" (AFR) tools has been developed to help users avoid unwanted facial recognition. The set of AFR tools proposed in the last few years is wide-ranging and rapidly evolving, necessitating a step back to consider the broader design space of AFR systems and long-term challenges. This paper aims to fill that gap and provides the first comprehensive analysis of the AFR research landscape. Using the operational stages of FR systems as a starting point, we create a systematic framework for analyzing the benefits and tradeoffs of different AFR approaches. We then consider both technical and social challenges facing AFR tools and propose directions for future research in this field.

Distantly supervised named entity recognition (DS-NER) efficiently reduces labor costs but meanwhile intrinsically suffers from the label noise due to the strong assumption of distant supervision. Typically, the wrongly labeled instances comprise numbers of incomplete and inaccurate annotation noise, while most prior denoising works are only concerned with one kind of noise and fail to fully explore useful information in the whole training set. To address this issue, we propose a robust learning paradigm named Self-Collaborative Denoising Learning (SCDL), which jointly trains two teacher-student networks in a mutually-beneficial manner to iteratively perform noisy label refinery. Each network is designed to exploit reliable labels via self denoising, and two networks communicate with each other to explore unreliable annotations by collaborative denoising. Extensive experimental results on five real-world datasets demonstrate that SCDL is superior to state-of-the-art DS-NER denoising methods.

Observational studies of causal effects require adjustment for confounding factors. In the tabular setting, where these factors are well-defined, separate random variables, the effect of confounding is well understood. However, in public policy, ecology, and in medicine, decisions are often made in non-tabular settings, informed by patterns or objects detected in images (e.g., maps, satellite or tomography imagery). Using such imagery for causal inference presents an opportunity because objects in the image may be related to the treatment and outcome of interest. In these cases, we rely on the images to adjust for confounding but observed data do not directly label the existence of the important objects. Motivated by real-world applications, we formalize this challenge, how it can be handled, and what conditions are sufficient to identify and estimate causal effects. We analyze finite-sample performance using simulation experiments, estimating effects using a propensity adjustment algorithm that employs a machine learning model to estimate the image confounding. Our experiments also examine sensitivity to misspecification of the image pattern mechanism. Finally, we use our methodology to estimate the effects of policy interventions on poverty in African communities from satellite imagery.

Graph convolutional networks (GCNs) are currently the most promising paradigm for dealing with graph-structure data, while recent studies have also shown that GCNs are vulnerable to adversarial attacks. Thus developing GCN models that are robust to such attacks become a hot research topic. However, the structural purification learning-based or robustness constraints-based defense GCN methods are usually designed for specific data or attacks, and introduce additional objective that is not for classification. Extra training overhead is also required in their design. To address these challenges, we conduct in-depth explorations on mid-frequency signals on graphs and propose a simple yet effective Mid-pass filter GCN (Mid-GCN). Theoretical analyses guarantee the robustness of signals through the mid-pass filter, and we also shed light on the properties of different frequency signals under adversarial attacks. Extensive experiments on six benchmark graph data further verify the effectiveness of our designed Mid-GCN in node classification accuracy compared to state-of-the-art GCNs under various adversarial attack strategies.