Large language models (LLMs) often face a bottleneck in inference speed due to their reliance on auto-regressive decoding. Recently, parallel decoding has shown significant promise in enhancing inference efficiency. However, we have identified two key issues with existing parallel decoding frameworks: (1) decoding heads fail to balance prediction accuracy and the parallelism of execution, and (2) parallel decoding is not a universal solution, as it can bring unnecessary overheads at some challenging decoding steps. To address these issues, we propose Cerberus, an adaptive parallel decoding framework introduces the gating mechanism to enable the LLMs to adaptively choose appropriate decoding approaches at each decoding step, along with introducing a new paradigm of decoding heads that introduce the sequential knowledge while maintaining execution parallelism. The experiment results demonstrate that the Cerberus can achieve up to 2.12x speed up compared to auto-regressive decoding, and outperforms one of the leading parallel decoding frameworks, Medusa, with a 10% - 30% increase in acceleration and superior generation quality.

Public and commercial organizations extensively share cyberthreat Cyber Threat Intelligence (CTI) offers crucial insights into the intelligence (CTI) to prepare systems to defend against existing rapidly evolving cyber threat landscape. This information includes and emerging cyberattacks. However, traditional CTI has primarily any evidence to identify and assess the associated threats, such as focused on tracking known threat indicators such as IP addresses indicators of compromise (IOCs), IP addresses, domain names, and and domain names, which may not provide long-term value in file hashes, and any associated tactics, techniques, and procedures defending against evolving attacks. To address this challenge, we (TTPs) used by the attacker(s). For instance, CTI can provide comprehensive, propose to use more robust threat intelligence signals called attack contextual information on emerging threats like the patterns. LADDER is a knowledge extraction framework that can advanced persistent threat (APT), ScarCruft [58]. Also known as extract text-based attack patterns from CTI reports at scale. The APT37, the cyber threat intelligence on ScarCruft reported that the framework characterizes attack patterns by capturing the phases of APT targets "individuals in South Korean organizations" with the an attack in Android and enterprise networks and systematically primary objective of "cyber espionage."

We present an open-source Visual-Inertial-Leg Odometry (VILO) state estimation solution, Cerberus, for legged robots that estimates position precisely on various terrains in real time using a set of standard sensors, including stereo cameras, IMU, joint encoders, and contact sensors. In addition to estimating robot states, we also perform online kinematic parameter calibration and contact outlier rejection to substantially reduce position drift. Hardware experiments in various indoor and outdoor environments validate that calibrating kinematic parameters within the Cerberus can reduce estimation drift to lower than 1% during long distance high speed locomotion. Our drift results are better than any other state estimation method using the same set of sensors reported in the literature. Moreover, our state estimator performs well even when the robot is experiencing large impacts and camera occlusion. The implementation of the state estimator, along with the datasets used to compute our results, are available at https://github.com/ShuoYangRobotics/Cerberus.

Modern defenses against cyberattacks increasingly rely on proactive approaches, e.g., to predict the adversary's next actions based on past events. Building accurate prediction models requires knowledge from many organizations; alas, this entails disclosing sensitive information, such as network structures, security postures, and policies, which might often be undesirable or outright impossible. In this paper, we explore the feasibility of using Federated Learning (FL) to predict future security events. To this end, we introduce Cerberus, a system enabling collaborative training of Recurrent Neural Network (RNN) models for participating organizations. The intuition is that FL could potentially offer a middle-ground between the non-private approach where the training data is pooled at a central server and the low-utility alternative of only training local models. We instantiate Cerberus on a dataset obtained from a major security company's intrusion prevention product and evaluate it vis-a-vis utility, robustness, and privacy, as well as how participants contribute to and benefit from the system. Overall, our work sheds light on both the positive aspects and the challenges of using FL for this task and paves the way for deploying federated approaches to predictive security.

With direct funding plus prize cash that reached into the hundreds of thousands, DARPA inspired worldwide collaborations amongst prime educational establishments in addition to business. A sequence of three preliminary circuit occasions would give groups expertise with every atmosphere. In the course of the Tunnel Circuit occasion, which happened in August 2019 within the Nationwide Institute for Occupational Security and Well being's experimental coal mine, on the outskirts of Pittsburgh, many groups misplaced communication with their robots after the primary bend within the tunnel. Six months later, on the City Circuit occasion, held at an unfinished nuclear energy station in Satsop, Wash., groups beefed up their communications with every part from an easy tethered Ethernet cable to battery-powered mesh community nodes that robots would drop like breadcrumbs as they went alongside, ideally simply earlier than they handed out of communication vary. The Cave Circuit, scheduled for the autumn of 2020, was canceled on account of COVID-19. By the point groups reached the SubT Remaining Occasion within the Louisville Mega Cavern, the main target was on autonomy slightly than communications.

To generalize to novel visual scenes with new viewpoints and new object poses, a visual system needs representations of the shapes of the parts of an object that are invariant to changes in viewpoint or pose. 3D graphics representations disentangle visual factors such as viewpoints and lighting from object structure in a natural way. It is possible to learn to invert the process that converts 3D graphics representations into 2D images, provided the 3D graphics representations are available as labels. When only the unlabeled images are available, however, learning to derender is much harder. We consider a simple model which is just a set of free floating parts. Each part has its own relation to the camera and its own triangular mesh which can be deformed to model the shape of the part. At test time, a neural network looks at a single image and extracts the shapes of the parts and their relations to the camera. Each part can be viewed as one head of a multi-headed derenderer. During training, the extracted parts are used as input to a differentiable 3D renderer and the reconstruction error is backpropagated to train the neural net. We make the learning task easier by encouraging the deformations of the part meshes to be invariant to changes in viewpoint and invariant to the changes in the relative positions of the parts that occur when the pose of an articulated body changes. Cerberus, our multi-headed derenderer, outperforms previous methods for extracting 3D parts from single images without part annotations, and it does quite well at extracting natural parts of human figures.