China has for the first time sent a TB001 combat and reconnaissance drone on a solo mission through Okinawa Prefecture's Miyako Strait, traveling from the East China Sea into the Pacific near Taiwan, according to the Japanese Defense Ministry. The flight of the drone, which has a maximum range of 6,000 kilometers and can carry missiles and precision guided bombs, occurred Monday from the morning through the afternoon. The Defense Ministry said it scrambled fighter jets to monitor the drone, which did not violate the country's territorial airspace. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

Prior work has demonstrated that data augmentation is useful for improving dialogue state tracking. However, there are many types of user utterances, while the prior method only considered the simplest one for augmentation, raising the concern about poor generalization capability. In order to better cover diverse dialogue acts and control the generation quality, this paper proposes controllable user dialogue act augmentation (CUDA-DST) to augment user utterances with diverse behaviors. With the augmented data, different state trackers gain improvement and show better robustness, achieving the state-of-the-art performance on MultiWOZ 2.1

If you have a habit of perusing satellite imagery of the world's oceans--and who doesn't, really?--you might get lucky and spot long, thin clouds, like white slashes across the sea. That's a peculiar phenomenon known as a ship track. As cargo ships chug along, flinging sulfur into the atmosphere, they actually trace their routes for satellites to see. That's because those pollutants rise into low-level clouds and plump them up by acting as nuclei that attract water vapor, which also brightens the clouds. Counterintuitively, these pollution-derived tracks actually have a cooling effect on the climate, since brighter clouds bounce more of the sun's energy back into space.

As machine learning has made its way into more and more areas of our lives, concerns about algorithmic bias have escalated. Machine learning models, which today facilitate decisions about everything from hiring and lending to medical diagnosis and criminal sentencing, may appear to be data-driven and impartial, at least to naïve users--but the typically opaque models are only as good the data they are trained on, and only as ethical as the value judgments embedded in the algorithms. The burgeoning field of algorithmic fairness, part of the much broader field of responsible computing, is aiming to remedy the situation. For several years now, along with philosophers, legal scholars, and experts in other fields, computer scientists have been tackling the issue. As Stanford University computer science professor Omer Reingold likes to put it, "We are part of the problem, and we should be part of the solution."

Automated detection of anomalous trajectories is an important problem with considerable applications in intelligent transportation systems. Many existing studies have focused on distinguishing anomalous trajectories from normal trajectories, ignoring the large differences between anomalous trajectories. A recent study has made great progress in identifying abnormal trajectory patterns and proposed a two-stage algorithm for anomalous trajectory detection and classification (ATDC). This algorithm has excellent performance but suffers from a few limitations, such as high time complexity and poor interpretation. Here, we present a careful theoretical and empirical analysis of the ATDC algorithm, showing that the calculation of anomaly scores in both stages can be simplified, and that the second stage of the algorithm is much more important than the first stage. Hence, we develop a FastATDC algorithm that introduces a random sampling strategy in both stages. Experimental results show that FastATDC is 10 to 20 times faster than ATDC on real datasets. Moreover, FastATDC outperforms the baseline algorithms and is comparable to the ATDC algorithm.

"It is a far, far better thing that I do, than I have ever done," Sydney Carton thinks on his way to the guillotine. That far better thing is dying tragically, for many reasons: to save an innocent man, to fulfill his own redemption, and--of course--to make us cry at the end of A Tale of Two Cities. The death scene is one of the sharpest tools in a writer's toolbox, as likely to wound the writer themself as the reader--for if a well-written death scene can be thrilling, terrifying, or filled with despair, so can a poorly written one be bathetic, stupid, and eye-rolling. But let's not talk about those. Let's talk about the good ones, the deathless death scenes. We've assembled the 50 greatest fictional deaths of all time--the most moving, most funny, most shocking, most influential scenes from books, movies, TV, theater, video games, and more. Spoilers abound: It's a list that spans nearly 2,500 years of human culture, from Athens to A24, and is so competitive that even poor Sydney Carton and his famous last words couldn't make it. We've also talked to many of the creators behind the scenes on our list to ask them how they wrote them, why they killed off characters we loved, what makes a great death scene, and what final moments from fiction have stuck with them all their lives. We've made this list during a pandemic, as real-life death has stalked us all, more tangible than ever. After all, one of the many things art can do is to help us navigate the pitfalls of life, and there's no deeper pitfall than the final one. Here are the scenes that have shown us all what the big goodbye might actually be like, when it comes. Imagine Imagine the horror in Athens' Theatre of Dionysus at the premiere of Medea, as the audience heard the desperate cries of Medea's two sons while she ruthlessly stabbed them to death.

Ten years ago, the authors posited that being a data scientist was the “sexiest job of the 21st century.” A decade later, does the claim stand up? The job has grown in popularity and is generally well-paid, and the field is projected to experience more growth than almost any other by 2029. But the job has changed, in both large and small ways. It’s become better institutionalized, the scope of the job has been redefined, the technology it relies on has made huge strides, and the importance of non-technical expertise, such as ethics and change management, has grown. How it operates in companies — and how executives need to think about managing data science efforts — has changed, too, as businesses now need to create and oversee diverse data science teams rather than searching for data scientist unicorns. Finally, companies need to think about what comes next, and how they can begin to think about democratizing data science.

Due to the ever-growing demands for electronic chips in different sectors the semiconductor companies have been mandated to offshore their manufacturing processes. This unwanted matter has made security and trustworthiness of their fabricated chips concerning and caused creation of hardware attacks. In this condition, different entities in the semiconductor supply chain can act maliciously and execute an attack on the design computing layers, from devices to systems. Our attack is a hardware Trojan that is inserted during mask generation/fabrication in an untrusted foundry. The Trojan leaves a footprint in the fabricated through addition, deletion, or change of design cells. In order to tackle this problem, we propose Explainable Vision System for Hardware Testing and Assurance (EVHA) in this work that can detect the smallest possible change to a design in a low-cost, accurate, and fast manner. The inputs to this system are Scanning Electron Microscopy (SEM) images acquired from the Integrated Circuits (ICs) under examination. The system output is determination of IC status in terms of having any defect and/or hardware Trojan through addition, deletion, or change in the design cells at the cell-level. This article provides an overview on the design, development, implementation, and analysis of our defense system.

As opaque predictive models increasingly impact many areas of modern life, interest in quantifying the importance of a given input variable for making a specific prediction has grown. Recently, there has been a proliferation of model-agnostic methods to measure variable importance (VI) that analyze the difference in predictive power between a full model trained on all variables and a reduced model that excludes the variable(s) of interest. A bottleneck common to these methods is the estimation of the reduced model for each variable (or subset of variables), which is an expensive process that often does not come with theoretical guarantees. In this work, we propose a fast and flexible method for approximating the reduced model with important inferential guarantees. We replace the need for fully retraining a wide neural network by a linearization initialized at the full model parameters. By adding a ridge-like penalty to make the problem convex, we prove that when the ridge penalty parameter is sufficiently large, our method estimates the variable importance measure with an error rate of $O(\frac{1}{\sqrt{n}})$ where $n$ is the number of training samples. We also show that our estimator is asymptotically normal, enabling us to provide confidence bounds for the VI estimates. We demonstrate through simulations that our method is fast and accurate under several data-generating regimes, and we demonstrate its real-world applicability on a seasonal climate forecasting example.

As you may have noticed, I'm pretty obsessed with covering the best A.I. startups. Check out my posts on Prospectus. On this Newsletter I've taken special care to talk about A.I. being used in war and national security and will continue to do so. Recently, I was alarmed about a startup that wants to use Drones equipped with Tasers to help monitor for school shootings. Curiously most of his ethics board resigned in protest.