The existence of adversarial examples poses concerns for the robustness of convolutional neural networks (CNN), for which a popular hypothesis is about the frequency bias phenomenon: CNNs rely more on high-frequency components (HFC) for classification than humans, which causes the brittleness of CNNs. However, most previous works manually select and roughly divide the image frequency spectrum and conduct qualitative analysis. In this work, we introduce Shapley value, a metric of cooperative game theory, into the frequency domain and propose to quantify the positive (negative) impact of every frequency component of data on CNNs. Based on the Shapley value, we quantify the impact in a fine-grained way and show intriguing instance disparity. Statistically, we investigate adversarial training(AT) and the adversarial attack in the frequency domain. The observations motivate us to perform an in-depth analysis and lead to multiple novel hypotheses about i) the cause of adversarial robustness of the AT model; ii) the fairness problem of AT between different classes in the same dataset; iii) the attack bias on different frequency components. Finally, we propose a Shapley-value guided data augmentation technique for improving the robustness. Experimental results on image classification benchmarks show its effectiveness. The code for this paper is at https://github.com/Ytchen981/CSA

Realizing embodied artificial intelligence is challenging due to the huge computation demands of large models (LMs). To support LMs while ensuring real-time inference, embodied edge intelligence (EEI) is a promising paradigm, which leverages an LM edge to provide computing powers in close proximity to embodied robots. Due to embodied data exchange, EEI requires higher spectral efficiency, enhanced communication security, and reduced inter-user interference. To meet these requirements, near-field communication (NFC), which leverages extremely large antenna arrays as its hardware foundation, is an ideal solution. Therefore, this paper advocates the integration of EEI and NFC, resulting in a near-field EEI (NEEI) paradigm. However, NEEI also introduces new challenges that cannot be adequately addressed by isolated EEI or NFC designs, creating research opportunities for joint optimization of both functionalities. To this end, we propose radio-friendly embodied planning for EEI-assisted NFC scenarios and view-guided beam-focusing for NFC-assisted EEI scenarios. We also elaborate how to realize resource-efficient NEEI through opportunistic collaborative navigation. Experimental results are provided to confirm the superiority of the proposed techniques compared with various benchmarks.

--Deep reinforcement learning can seamlessly transfer agile locomotion and navigation skills from the simulator to real world. However, bridging the sim-to-real gap with domain randomization or adversarial methods often demands expert physics knowledge to ensure policy robustness. Even so, cutting-edge simulators may fall short of capturing every real-world detail, and the reconstructed environment may introduce errors due to various perception uncertainties. T o address these challenges, we propose Neural Fidelity Calibration (NFC), a novel framework that employs conditional score-based diffusion models to calibrate simulator physical coefficients and residual fidelity domains online during robot execution. Specifically, the residual fidelity reflects the simulation model shift relative to the real-world dynamics and captures the uncertainty of the perceived environment, enabling us to sample realistic environments under the inferred distribution for policy fine-tuning. Our framework is informative and adaptive in three key ways: (a) we fine-tune the pretrained policy only under anomalous scenarios, (b) we build sequential NFC online with the pretrained NFC's proposal prior, reducing the diffusion model's training burden, and (c) when NFC uncertainty is high and may degrade policy improvement, we leverage optimistic exploration to enable "hallucinated" policy optimization. Our framework achieves superior simulator calibration precision compared to state-of-the-art methods across diverse robots with high-dimensional parametric spaces. We study the critical contribution of residual fidelity to policy improvement in simulation and real-world experiments. Notably, our approach demonstrates robust robot navigation under challenging real-world conditions, such as a broken wheel axle on snowy surfaces. Zero-shot sim-to-real reinforcement learning (RL) has empowered agile policy to various robots across soft [74], wheeled [83], aerial [18], and quadruped [45] embodiments. In the context of policy resilience against the real-world diversities, the proximal works in domain randomization (DR) [75] and adversarial training [19] emerge as powerful strategies by artificially introducing noise or attacks into the agent's states. Safety RL, which incorporates safety constraints into the optimization [10], remains tied to DR via exploration of diverse unsafe scenarios. Despite these advancements, expert real-world knowledge is often required to determine domain ranges [48], reconstruct environments [15], or design adversarial scenarios [66]. In theory, one could uniformly sample every domain parameter and environment variation, but this is usually impractical. Y u and L. Liu are with the Luddy School of Informatics, Computing, and Engineering at Indiana University, Bloomington, IN 47408, USA.

Imagine if AI decision-support tools not only complemented our ability to make accurate decisions, but also improved our skills, boosted collaboration, and elevated the joy we derive from our tasks. Despite the potential to optimize a broad spectrum of such human-centric objectives, the design of current AI tools remains focused on decision accuracy alone. We propose offline reinforcement learning (RL) as a general approach for modeling human-AI decision-making to optimize human-AI interaction for diverse objectives. RL can optimize such objectives by tailoring decision support, providing the right type of assistance to the right person at the right time. We instantiated our approach with two objectives: human-AI accuracy on the decision-making task and human learning about the task and learned decision support policies from previous human-AI interaction data. We compared the optimized policies against several baselines in AI-assisted decision-making. Across two experiments (N=316 and N=964), our results demonstrated that people interacting with policies optimized for accuracy achieve significantly better accuracy -- and even human-AI complementarity -- compared to those interacting with any other type of AI support. Our results further indicated that human learning was more difficult to optimize than accuracy, with participants who interacted with learning-optimized policies showing significant learning improvement only at times. Our research (1) demonstrates offline RL to be a promising approach to model human-AI decision-making, leading to policies that may optimize human-centric objectives and provide novel insights about the AI-assisted decision-making space, and (2) emphasizes the importance of considering human-centric objectives beyond decision accuracy in AI-assisted decision-making, opening up the novel research challenge of optimizing human-AI interaction for such objectives.

Neural field methods have seen great progress in various long-standing tasks in computer vision and computer graphics, including novel view synthesis and geometry reconstruction. As existing neural field methods try to predict some coordinate-based continuous target values, such as RGB for Neural Radiance Field (NeRF), all of these methods are regression models and are optimized by some regression loss. However, are regression models really better than classification models for neural field methods? In this work, we try to visit this very fundamental but overlooked question for neural fields from a machine learning perspective. We successfully propose a novel Neural Field Classifier (NFC) framework which formulates existing neural field methods as classification tasks rather than regression tasks. The proposed NFC can easily transform arbitrary Neural Field Regressor (NFR) into its classification variant via employing a novel Target Encoding module and optimizing a classification loss. By encoding a continuous regression target into a high-dimensional discrete encoding, we naturally formulate a multi-label classification task. Extensive experiments demonstrate the impressive effectiveness of NFC at the nearly free extra computational costs. Moreover, NFC also shows robustness to sparse inputs, corrupted images, and dynamic scenes. Background Neural field methods emerge as promising methods for parameterizing a field, represented by a scalar, vector, or tensor, that has a target value for each point in space and time.

The existence of adversarial examples poses concerns for the robustness of convolutional neural networks (CNN), for which a popular hypothesis is about the frequency bias phenomenon: CNNs rely more on high-frequency components (HFC) for classification than humans, which causes the brittleness of CNNs. However, most previous works manually select and roughly divide the image frequency spectrum and conduct qualitative analysis. In this work, we introduce Shapley value, a metric of cooperative game theory, into the frequency domain and propose to quantify the positive (negative) impact of every frequency component of data on CNNs. Based on the Shapley value, we quantify the impact in a fine-grained way and show intriguing instance disparity. Statistically, we investigate adversarial training(AT) and the adversarial attack in the frequency domain. The observations motivate us to perform an in-depth analysis and lead to multiple novel hypotheses about i) the cause of adversarial robustness of the AT model; ii) the fairness problem of AT between different classes in the same dataset; iii) the attack bias on different frequency components. Finally, we propose a Shapley-value guided data augmentation technique for improving the robustness. Experimental results on image classification benchmarks show its effectiveness. The code for this paper is at https://github.com/Ytchen981/CSA

Xiaomi has also launched a new tablet, the Pad 5, and an NFC version of the Mi Smart Band 6 today, in addition to its new Xiaomi 11 smartphones. The Pad 5 was designed with productivity in mind, specifically as a tool meant for people working or studying from home. It comes with the company's Smart Pen, which can be used to take notes or to quickly take screenshots with its function keys. The tablet has an 8-megapixel front camera that supports 1080p video for meetings and classes. It also has built-in capability to scan documents for later use or for sharing using its 13-megapixel rear camera.

What does the future smell like? That depends on who you ask. PUIG's perfumiers, who produce scents for Paco Rabanne, believe that the future smells sexy, confident and energetic. That's how they're choosing to market Phantom, the fashion house's latest fragrance-cum-piece of retro-futurist art. Phantom comes in a robot-shaped bottle that, when you tap your phone on the NFC tag embedded into its head, welcomes you into its own digital world.

The label of Black Red Ale beer, which incorporates a large "talking" skull, fully fits into the smart packaging trend by using advanced AR facial recognition and dynamic scenarios dependent on users' emotions. NRC is a set of communication protocols that enables two electronic devices, one of which is often a portable smartphone, to establish communication and AR is the interactive experience of a real-world environment, which has been "augmented" digitally. Click to EnlargeAs the customer scans the smart label with a mobile app, the skull presented on the label engages in an interactive dialogue with the consumer. The facial recognition feature detects if the customer is happy or sad and customizes the next part of the dialogue to accommodate a flowing conversation. Furthermore, variable AR scenarios are also launched depending on answers provided to questions asked by the skull.

If you've ever struggled to pair your phone with a Bluetooth speaker or set up a wireless printer, you know that it's often easier to connect to a server halfway around the world than to a gadget across the room. That's a problem as we increasingly use our phones to pay for stuff, unlock doors, and control everything from televisions to thermostats. No one wants to wait for coffee because the cash register can't detect their phone, or shiver in the cold because their watch is trying to connect to their neighbor's door lock instead of their own. Multiple wireless technologies have emerged in recent years to tackle this problem, including Bluetooth, LoRa, and NFC. These technologies are all based on radio frequencies. But a growing number of businesses, from Ticketmaster to Google to nuclear-power plants, are turning to a simpler solution: sound.