A near air mishap on Bengaluru skies that put the lives of 430 people in danger shows safety risk norms require constant refresh

National Bureau of Economic Research

National Bureau of Economic Research

National Bureau of Economic Research

National Bureau of Economic Research

IEEE / Institute of Electrical and Electronics Engineers Incorporated

Sastri’s sonorous voice carried manodharmam aspects of raga, neraval and swara kalpana well.

Underlying positives in sub-7 pc Q1 growth

BL Mumbai Bureau Knight Frank India reports 9 per cent rise in home sales and 27 per cent increase in office leasing in 2024

Natco Pharma said that the company has shown continued strong growth during the second quarter driven by exports formulation business and stable domestic pharma business

India’s services PMI rebounds to 58.5 in October, driven by strong output, new business, and job creation, signaling economic growth

Total tractor sales in the domestic market grew by 23%per cent to 144,675 units in October 2024

India has made significant strides in reducing its carbon intensity, with a 33 per cent reduction between 2005 and 2019, as per a UN report

Revenue rose to ₹ 1,491.23 crore, compared to ₹ 1,161.89 crore in the previous year

Ford Endeavour’s sturdiness and ability to be driven in rough driving conditions was apparent during a recent test drive

This year’s Geothermal Energy Association (GEA)’s Geothermal Energy Expo and Geothermal Resources Council’s Annual Meeting incorporated a wide range of meetings on international development. U.S. interest in exporting geothermal goods and services is ever-increasingly met with outside interest in U.S. knowledge and materials, particularly from developing countries.

Estonia’s economy is performing well, and public finances are in excellent shape, yet growth is softening and spending pressures from infrastructure needs and an ageing population are mounting. Efforts should now focus on improving income equality and well-being, greening growth and accelerating the country’s digital transformation, according to a new OECD report.

The recent droughts in Brazil’s Rio de Janeiro and São Paulo states have exposed the need to shift from crisis management to effective risk governance of the country’s water resources, according to a new OECD report.

The recent droughts in Brazil’s Rio de Janeiro and São Paulo states have exposed the need to shift from crisis management to effective risk governance of the country’s water resources, according to a new OECD report.

Water-supply planning that considers the preferences of multiple stakeholders under uncertain and variable future conditions are more robust than planning decisions based on historical conditions, a recent study has stated. Using the Thames river basin in the UK as an example, the researchers present a new computer-modelling approach to assess which combinations of water-management measures best secure future water supply under a wide range of possible future conditions.

The Innovative Technology Platform Company Welcomes Three New Hires

I originally gave this post the title, “Stigler: The Changing History of Robustness,” but then I was afraid nobody would read it. In the current environment of Move Fast and Break Things, not so many people care about robustness. Also, the widespread use of robustness checks to paper over brittle conclusions has given robustness a […]

We give an efficient algorithm for robustly clustering of a mixture of two arbitrary Gaussians, a central open problem in the theory of computationally efficient robust estimation, assuming only that the the means of the component Gaussians are well-separated or their covariances are well-separated. Our algorithm and analysis extend naturally to robustly clustering mixtures of well-separated strongly logconcave distributions. The mean separation required is close to the smallest possible to guarantee that most of the measure of each component can be separated by some hyperplane (for covariances, it is the same condition in the second degree polynomial kernel). We also show that for Gaussian mixtures, separation in total variation distance suffices to achieve robust clustering. Our main tools are a new identifiability criterion based on isotropic position and the Fisher discriminant, and a corresponding Sum-of-Squares convex programming relaxation, of fixed degree.

Showing items that do not match search query intent degrades customer experience in e-commerce. These mismatches result from counterfactual biases of the ranking algorithms toward noisy behavioral signals such as clicks and purchases in the search logs. Mitigating the problem requires a large labeled dataset, which is expensive and time-consuming to obtain. In this paper, we develop a deep, end-to-end model that learns to effectively classify mismatches and to generate hard mismatched examples to improve the classifier. We train the model end-to-end by introducing a latent variable into the cross-entropy loss that alternates between using the real and generated samples. This not only makes the classifier more robust but also boosts the overall ranking performance. Our model achieves a relative gain compared to baselines by over 26% in F-score, and over 17% in Area Under PR curve. On live search traffic, our model gains significant improvement in multiple countries.

Graph convolution network (GCN) have achieved state-of-the-art performance in the task of node prediction in the graph structure. However, with the gradual various of graph attack methods, there are lack of research on the robustness of GCN. At this paper, we will design a robust GCN method for node prediction tasks. Considering the graph structure contains two types of information: node information and connection information, and attackers usually modify the connection information to complete the interference with the prediction results of the node, we first proposed a method to hide the connection information in the generator, named Anonymized GCN (AN-GCN). By hiding the connection information in the graph structure in the generator through adversarial training, the accurate node prediction can be completed only by the node number rather than its specific position in the graph. Specifically, we first demonstrated the key to determine the embedding of a specific node: the row corresponding to the node of the eigenmatrix of the Laplace matrix, by target it as the output of the generator, we designed a method to hide the node number in the noise. Take the corresponding noise as input, we will obtain the connection structure of the node instead of directly obtaining. Then the encoder and decoder are spliced both in discriminator, so that after adversarial training, the generator and discriminator can cooperate to complete the encoding and decoding of the graph, then complete the node prediction. Finally, All node positions can generated by noise at the same time, that is to say, the generator will hides all the connection information of the graph structure. The evaluation shows that we only need to obtain the initial features and node numbers of the nodes to complete the node prediction, and the accuracy did not decrease, but increased by 0.0293.

Convolutional Neural Networks (CNNs) are generally prone to noise interruptions, i.e., small image noise can cause drastic changes in the output. To suppress the noise effect to the final predication, we enhance CNNs by replacing max-pooling, strided-convolution, and average-pooling with Discrete Wavelet Transform (DWT). We present general DWT and Inverse DWT (IDWT) layers applicable to various wavelets like Haar, Daubechies, and Cohen, etc., and design wavelet integrated CNNs (WaveCNets) using these layers for image classification. In WaveCNets, feature maps are decomposed into the low-frequency and high-frequency components during the down-sampling. The low-frequency component stores main information including the basic object structures, which is transmitted into the subsequent layers to extract robust high-level features. The high-frequency components, containing most of the data noise, are dropped during inference to improve the noise-robustness of the WaveCNets. Our experimental results on ImageNet and ImageNet-C (the noisy version of ImageNet) show that WaveCNets, the wavelet integrated versions of VGG, ResNets, and DenseNet, achieve higher accuracy and better noise-robustness than their vanilla versions.

Cognitive radio is a promising technology to improve spectral efficiency. However, the secure performance of a secondary network achieved by using physical layer security techniques is limited by its transmit power and channel fading. In order to tackle this issue, a cognitive unmanned aerial vehicle (UAV) communication network is studied by exploiting the high flexibility of a UAV and the possibility of establishing line-of-sight links. The average secrecy rate of the secondary network is maximized by robustly optimizing the UAV's trajectory and transmit power. Our problem formulation takes into account two practical inaccurate location estimation cases, namely, the worst case and the outage-constrained case. In order to solve those challenging non-convex problems, an iterative algorithm based on $mathcal{S}$-Procedure is proposed for the worst case while an iterative algorithm based on Bernstein-type inequalities is proposed for the outage-constrained case. The proposed algorithms can obtain effective suboptimal solutions of the corresponding problems. Our simulation results demonstrate that the algorithm under the outage-constrained case can achieve a higher average secrecy rate with a low computational complexity compared to that of the algorithm under the worst case. Moreover, the proposed schemes can improve the secure communication performance significantly compared to other benchmark schemes.

A hysteresis device assembly comprises a first spacer plate, a second spacer plate, a stack plate, and a hub. The hub engages the second spacer plate. The hysteresis device assembly further comprises a tabbed washer comprising a tab. The tab engages the first spacer plate and the stack plate.

A first method includes receiving a first reflected radar signal from a target in a first field of view and receiving a second reflected radar signal from a target in a second field of view offset from the first field of view by a predetermined distance; transforming the first and second reflected radar signals to obtain first and second sets of frequency coefficients, from which a frequency-dependent phase difference is obtained; and calculating a time-delay from the slope of the frequency dependence. A second method includes obtaining summed difference values between the first and second radar responses, where each of the summed difference values corresponds to different time shifts between the first and second radar response, and deriving from the summed difference values a time-delay associated with the target's motion from the first field of view to the second field of view. A third method combines the time-delays or associated speeds obtained from independent estimators.

A nanoscale plate structure includes base plates and rib plates with nanoscale thickness and macroscopic lateral dimensions. The base plate resides in the first plane, the ribs can reside out-of-plane and form at least one strengthening rib, and additional base plates can reside in planes parallel to the first plane. The strengthening rib can be patterned such that there is no straight line path extending through a lateral dimension of the plate structure that does not intersect the at least one base plate and the at least one strengthening rib. The plates and ribs used in the structure have a thickness between about 1 nm and about 100 nm. The plate structures can be fabricated using a conformal deposition method including atomic layer deposition.