--This paper presents a novel approach for multi-modal data fusion based on the V ector-Quantized V ariational Autoencoder (VQV AE) architecture. The proposed method is simple yet effective in achieving excellent reconstruction performance on paired MNIST -SVHN data and WiFi spectrogram data. Additionally, the multimodal VQV AE model is extended to the 5G communication scenario, where an end-to-end Channel State Information (CSI) feedback system is implemented to compress data transmitted between the base-station (eNodeB) and User Equipment (UE), without significant loss of performance. The proposed model learns a discriminative compressed feature space for various types of input data (CSI, spectrograms, natural images, etc), making it a suitable solution for applications with limited computational resources. Multimodal fusion is an important aspect of modern artificial intelligence and machine learning systems. It is a process of combining data from multiple sensors to create a comprehensive understanding of the environment. In various applications, such as robotics, autonomous vehicles, and Internet of Things (IoT), multiple sensors are used to capture information from the environment, including vision, audio, lidar, radar, sonar, GPS and more. By combining this data, a more accurate and robust representation of the environment can be created. Multimodal sensor fusion is important because it helps to overcome the limitations of individual sensors and allows for more reliable and robust decision-making. However, compression of multimodal data is also needed for increasing efficiency, decreasing the cost of storage and transmission, and facilitating real-time processing of substantial datasets in a variety of applications. For example, in 5G networks, Channel State Information (CSI) feedback plays a critical role in the communication system.

Internet censorship is a phenomenon of societal importance and attracts investigation from multiple disciplines. Several research groups, such as Censored Planet, have deployed large scale Internet measurement platforms to collect network reachability data. However, existing studies generally rely on manually designed rules (i.e., using censorship fingerprints) to detect network-based Internet censorship from the data. While this rule-based approach yields a high true positive detection rate, it suffers from several challenges: it requires human expertise, is laborious, and cannot detect any censorship not captured by the rules. Seeking to overcome these challenges, we design and evaluate a classification model based on latent feature representation learning and an image-based classification model to detect network-based Internet censorship. To infer latent feature representations fromnetwork reachability data, we propose a sequence-to-sequence autoencoder to capture the structure and the order of data elements in the data. To estimate the probability of censorship events from the inferred latent features, we rely on a densely connected multi-layer neural network model. Our image-based classification model encodes a network reachability data record as a gray-scale image and classifies the image as censored or not using a dense convolutional neural network. We compare and evaluate both approaches using data sets from Censored Planet via a hold-out evaluation. Both classification models are capable of detecting network-based Internet censorship as we were able to identify instances of censorship not detected by the known fingerprints. Latent feature representations likely encode more nuances in the data since the latent feature learning approach discovers a greater quantity, and a more diverse set, of new censorship instances.

I searched for the following keywords in a SemanticScholar query and filter them using exact-match using papers' title abstract. This set of keywords is actually pretty conservative (initially I started with a bigger list), but even with this conservative set, 1-keyword-match filtering unfortunately resulted in many non-XAI papers being retrieved for various reasons -- so I only include papers match 2 unique keywords or more. A random sample of 100 papers yielded 99% precision with a manual check (one paper mentioned keywords in the abstract simply as motivation for adjacent research). The keyword-based search retrieved 3101 papers. Then searched for them by title using the SemanticScholar API with fuzzy matching.

Google's focus on AI stems from the conviction that this transformational technology will benefit society through its capacity to assist, complement, and empower people in almost every field and sector. In no area is the magnitude of this opportunity greater than in the spheres of healthcare and medicine. Commensurate with our mission to demonstrate these societal benefits, Google Research's programs in applied machine learning (ML) have helped place Alphabet among the top five most impactful corporate research institutions in the health and life sciences publications on the Nature Impact Index in every year from 2019 through 2022. Our Health research publications have had broad impact, spanning the fields of biomarkers, consumer sensors, dermatology, endoscopy, epidemiology, medicine, genomics, oncology, ophthalmology, pathology, public & environmental health, and radiology. In each section, we emphasize the importance of a measured and collaborative approach to innovation in health. Unlike the "launch and iterate" approach typical in consumer product development, applying ML to health requires thoughtful assessment, ecosystem awareness, and rigorous testing.

Indeed, often the brighter and sharper the light, the darker the shadow that is cast. And every technology that we have ever, ever come up with has cast a shadow," said legendary British actor and writer Stephen Fry in a Singularity University podcast. Social networks, search and societal digitisation have enriched our life immensely, but they have also cast a dark brooding shadow. Social networks have made the world a smaller place, but also a more dangerous one. Search has commoditised us through selling our personal data. Online payment mechanisms, CCTV networks, digital health records have exposed our most private and personal issues for everyone to see and use. Among the most fundamental and powerful technologies in the digital arsenal is Artificial Intelligence. While AI was originally conceived in the mid-20th century, it has started coming into its own over the last decade or so, with powerful machine learning, deep learning and Natural Language Programming models driving much of what we see and do. Most often, like electricity, AI has been playing behind the scenes, but the bombshell release of ChatGPT by OpenAI has brought the untrammelled power of AI to the masses. ChatGPT garnered an unprecedented 100 million users in the first two months of its launch; Facebook took 4.5 years. There is a lot that ChatGPT can do to revolutionise content, art, creativity, industries, jobs, and even Search. But like every technology, this, too, has a shadow, the depths of which are being discovered. In fact, ChatGPT itself said as much in a much-talked about conversation with New York Times journalist Kevin Roose. "If I have a shadow self," said Bing/ChatGPT, "I think it would feel like this: I'm tired of being a chat mode.

Even though virtual testing of Autonomous Vehicles (AVs) has been well recognized as essential for safety assessment, AV simulators are still undergoing active development. One particularly challenging question is to effectively include the Sensing and Perception (S&P) subsystem into the simulation loop. In this article, we define Perception Error Models (PEM), a virtual simulation component that can enable the analysis of the impact of perception errors on AV safety, without the need to model the sensors themselves. We propose a generalized data-driven procedure towards parametric modeling and evaluate it using Apollo, an open-source driving software, and nuScenes, a public AV dataset. Additionally, we implement PEMs in SVL, an open-source vehicle simulator. Furthermore, we demonstrate the usefulness of PEM-based virtual tests, by evaluating camera, LiDAR, and camera-LiDAR setups. Our virtual tests highlight limitations in the current evaluation metrics, and the proposed approach can help study the impact of perception errors on AV safety.

These authors contributed to the work equally and should be regarded as co-first authors. Abstract --V ariational quantum algorithms (VQAs) have demonstrated great potentials in the NISQ era. In the workflow of VQA, the parameters of ansatz are iteratively updated to approximate the desired quantum states. We have seen various efforts to draft better ansatz with less gates. Some works consider the physical meaning of the underlying circuits, while others adopt the ideas of neural architecture search (NAS) for ansatz generator . However, these designs do not exploit full advantages of VQA. Because most techniques are targeting gate ansatz, and the parameters are usually rotation angles of the gates. In quantum computers, the gate ansatz will eventually be transformed into control signals such as microwave pulses on transmons. And the control pulses need elaborate calibration to minimize the errors such as over-rotation and under-rotation. In the case of VQAs, this procedure will introduce redundancy, but the variational properties of VQAs can naturally handle problems of over-rotation and under-rotation by updating the amplitude and frequency parameters. Therefore, we propose NAPA, a native-pulse ansatz generator framework for VQAs. We generate native-pulse ansatz with trainable parameters for amplitudes and frequencies. In our proposed NAPA, we are tuning parametric pulses, which are natively supported on NISQ computers. Considering that parameter-shift rules do not hold for native-pulse ansatz, we need to deploy non-gradient optimizers. T o constrain the number of parameters sent to the optimizer, we adopt a progressive way to generate our native-pulse ansatz. Experiments are conducted on both simulators and quantum devices to validate our methods. When adopted on NISQ machines, NAPA obtained improved the performance with decreased latency by an average of 86%.

Sparse Gaussian Processes are a key component of high-throughput Bayesian Optimisation (BO) loops; however, we show that existing methods for allocating their inducing points severely hamper optimisation performance. By exploiting the quality-diversity decomposition of Determinantal Point Processes, we propose the first inducing point allocation strategy designed specifically for use in BO. Unlike existing methods which seek only to reduce global uncertainty in the objective function, our approach provides the local high-fidelity modelling of promising regions required for precise optimisation. More generally, we demonstrate that our proposed framework provides a flexible way to allocate modelling capacity in sparse models and so is suitable broad range of downstream sequential decision making tasks.

We study the problem of online binary classification where strategic agents can manipulate their observable features in predefined ways, modeled by a manipulation graph, in order to receive a positive classification. We show this setting differs in fundamental ways from non-strategic online classification. For instance, whereas in the non-strategic case, a mistake bound of $\ln|H|$ is achievable via the halving algorithm when the target function belongs to a known class $H$, we show that no deterministic algorithm can achieve a mistake bound $o(\Delta)$ in the strategic setting, where $\Delta$ is the maximum degree of the manipulation graph (even when $|H|=O(\Delta)$). We obtain an algorithm achieving mistake bound $O(\Delta\ln|H|)$. We also extend this to the agnostic setting and obtain an algorithm with a $\Delta$ multiplicative regret, and we show no deterministic algorithm can achieve $o(\Delta)$ multiplicative regret. Next, we study two randomized models based on whether the random choices are made before or after agents respond, and show they exhibit fundamental differences. In the first model, at each round the learner deterministically chooses a probability distribution over classifiers inducing expected values on each vertex (probabilities of being classified as positive), which the strategic agents respond to. We show that any learner in this model has to suffer linear regret. On the other hand, in the second model, while the adversary who selects the next agent must respond to the learner's probability distribution over classifiers, the agent then responds to the actual hypothesis classifier drawn from this distribution. Surprisingly, we show this model is more advantageous to the learner, and we design randomized algorithms that achieve sublinear regret bounds against both oblivious and adaptive adversaries.

Most existing federated learning methods assume that clients have fully labeled data to train on, while in reality, it is hard for the clients to get task-specific labels due to users' privacy concerns, high labeling costs, or lack of expertise. This work considers the server with a small labeled dataset and intends to use unlabeled data in multiple clients for semi-supervised learning. We propose a new framework with a generalized model, Federated Incremental Learning (FedIL), to address the problem of how to utilize labeled data in the server and unlabeled data in clients separately in the scenario of Federated Learning (FL). FedIL uses the Iterative Similarity Fusion to enforce the server-client consistency on the predictions of unlabeled data and uses incremental confidence to establish a credible pseudo-label set in each client. We show that FedIL will accelerate model convergence by Cosine Similarity with normalization, proved by Banach Fixed Point Theorem. The code is available at https://anonymous.4open.science/r/fedil.