The main contribution of this paper is to introduce the interesting idea of "activator" (or integrator) operating on multiple metrics. Depending on how this activator (kappa) is picked, one may recover some previously proposed multiple metric learning approaches or create new relevant ones such as those discussed in Section 2.1. The proposed algorithm/analysis is an application of stochastic proximal (sub)gradient descent and is thus not a significant contribution from the optimization point of view (and the presentation of the algorithm is actually quite confusing, see below). My main problem with the paper is the experimental section. I was expecting some experiments showing how the choice of kappa influences the type of metrics that are learned, with an analysis of why some choices are better suited to some kind of problems / datasets / networks.

As Large Language Models (LLMs) grow increasingly powerful, ensuring their safety and alignment with human values remains a critical challenge. Ideally, LLMs should provide informative responses while avoiding the disclosure of harmful or sensitive information. However, current alignment approaches, which rely heavily on refusal strategies, such as training models to completely reject harmful prompts or applying coarse filters are limited by their binary nature. These methods either fully deny access to information or grant it without sufficient nuance, leading to overly cautious responses or failures to detect subtle harmful content. For example, LLMs may refuse to provide basic, public information about medication due to misuse concerns. Moreover, these refusal-based methods struggle to handle mixed-content scenarios and lack the ability to adapt to context-dependent sensitivities, which can result in over-censorship of benign content. To overcome these challenges, we introduce HiddenGuard, a novel framework for fine-grained, safe generation in LLMs. HiddenGuard incorporates Prism (rePresentation Router for In-Stream Moderation), which operates alongside the LLM to enable real-time, token-level detection and redaction of harmful content by leveraging intermediate hidden states. This fine-grained approach allows for more nuanced, context-aware moderation, enabling the model to generate informative responses while selectively redacting or replacing sensitive information, rather than outright refusal. We also contribute a comprehensive dataset with token-level fine-grained annotations of potentially harmful information across diverse contexts. Our experiments demonstrate that HiddenGuard achieves over 90% in F1 score for detecting and redacting harmful content while preserving the overall utility and informativeness of the model's responses.

The LCLS2 Free Electron Laser FEL will generate xray pulses to beamline experiments at up to 1Mhz These experimentals will require new ultrahigh rate UHR detectors that can operate at rates above 100 kHz and generate data throughputs upwards of 1 TBs a data velocity which requires prohibitively large investments in storage infrastructure Machine Learning has demonstrated the potential to digest large datasets to extract relevant insights however current implementations show latencies that are too high for realtime data reduction objectives SLAC has endeavored on the creation of a software framework which translates MLs structures for deployment on Field Programmable Gate Arrays FPGAs deployed at the Edge of the data chain close to the instrumentation This framework leverages Xilinxs HLS framework presenting an API modeled after the open source Keras interface to the TensorFlow library This SLAC Neural Network Library SNL framework is designed with a streaming data approach optimizing the data flow between layers while minimizing the buffer data buffering requirements The goal is to ensure the highest possible framerate while keeping the maximum latency constrained to the needs of the experiment Our framework is designed to ensure the RTL implementation of the network layers supporting full redeployment of weights and biases without requiring resynthesis after training The ability to reduce the precision of the implemented networks through quantization is necessary to optimize the use of both DSP and memory resources in the FPGA We currently have a preliminary version of the toolset and are experimenting with both general purpose example networks and networks being designed for specific LCLS2 experiments.

Originally published on Towards AI. In this first article, we will use one of the simplest Datasets, made up of 28 x 28 images of handwritten numbers in grayscale. I suppose you all know that it is the very famous MNIST Dataset. While we are looking at the theory, we will see the code needed to make and train the GAN. Even though this is our first GAN, we will use several of the recommendations launched by Soumith Chintala, known as GAN Hacks. Thus, we will create a GAN correctly optimized for the Dataset used.

FPGAs' ability to distribute massive workloads into parallel computation enables AI features to create highly efficient electronic devices. FREMONT, CA: Implementing FPGA increases the number of parallel computational elements and processing efficiency of the electronic devices. FPGAs that hold parallel and hardware-programmable feature enables electronics excellence at specialized workloads with high computational operations and optimal configurations. Over the past few years, FPGAs have proved to be the low-power solution, making it flexible and ideal for Neural Network (NN) architectures. Today, professionals are focusing on creating designs for supporting AI-based applications and functions.

There's more and more research done on detecting all types of cancers in early stages and thus increasing probability of survival. Since I've been passionate about machine learning for a while, I decided to bring my own contribution to this research and learn to train my own neural network detection model. The twist was to build it using Tensorflow with JavaScript, not with Python. We're also using React to manage the state and display the data we get back from the model. For this tutorial, I chose to work with a breast cancer dataset.

Hey buddy, pick a lane and stick with it! I was taken aback by a rude driver that had abruptly and without signaling opted to cut into my lane. I needed to quickly move into the lane next to me. Wait a second, unfortunately, none of the cars in that lane were willing to allow me into their jam packed lane. I'm sure that you likely have experienced the same kinds of frustrations in your daily commute as well. On the freeways here in Los Angeles, it seems like here aren't enough lanes for the number of cars. The lanes are often poorly marked and cars tend to radically veer toward each other. Plus, drivers illegally dart into and out of the HOV lane, and sometimes illegally use the emergency lane as a form of underhanded transit. We live in a world of lanes. On the open highways, there are usually a couple of lanes going in each direction.

Modeling causal dependencies often demands cycles at a coarse-grained temporal scale. If Bayesian networks are to be used for modeling uncertainties, cycles are eliminated with dynamic Bayesian networks, spreading indirect dependencies over time and enforcing an infinitesimal resolution of time. Without a "causal design," i.e., without anticipating indirect influences appropriately in time, we argue that such networks return spurious results. By introducing activator random variables, we propose template fragments for modeling dynamic Bayesian networks under a causal use of time, anticipating indirect influences on a solid mathematical basis, obeying the laws of Bayesian networks.