The world is currently seeing frequent local outbreaks of epidemics, such as COVID-19 and Monkeypox. Preventing further propagation of the outbreak requires prompt implementation of control measures, and a critical step is to quickly infer patient zero. This backtracking task is challenging for two reasons. First, due to the sudden emergence of local epidemics, information recording the spreading process is limited. Second, the spreading process has strong randomness. To address these challenges, we tailor a gnn-based model to establish the inverse statistical association between the current and initial state implicitly. This model uses contact topology and the current state of the local population to determine the possibility that each individual could be patient zero. We benchmark our model on data from important epidemiological models on five real temporal networks, showing performance significantly superior to previous methods. We also demonstrate that our method is robust to missing information about contact structure or current state. Further, we find the individuals assigned higher inferred possibility by model are closer to patient zero in terms of core number and the activity sequence recording the times at which the individual had contact with other nodes.

We consider the problem of reconstructing coupled networks (e.g., biological neural networks) connecting large numbers of variables (e.g.,nerve cells), of which state evolution is governed by dissipative dynamics consisting of strong self-drive (dominants the evolution) and weak coupling-drive. The core difficulty is sparseness of coupling effect that emerges (the coupling force is significant) only momentarily and otherwise remains quiescent in time series (e.g., neuronal activity sequence). Here we learn the idea from attention mechanism to guide the classifier to make inference focusing on the critical regions of time series data where coupling effect may manifest. Specifically, attention coefficients are assigned autonomously by artificial neural networks trained to maximise the Attentive Transfer Entropy (ATEn), which is a novel generalization of the iconic transfer entropy metric. Our results show that, without any prior knowledge of dynamics, ATEn explicitly identifies areas where the strength of coupling-drive is distinctly greater than zero. This innovation substantially improves reconstruction performance for both synthetic and real directed coupling networks using data generated by neuronal models widely used in neuroscience.

Rigid body disentanglement puzzles are challenging for both humans and motion planning algorithms because their solutions involve tricky twisting and sliding moves that correspond to navigating through narrow tunnels in the puzzle's configuration space (C-space). We propose a tunnel-discovery and planning strategy for solving these puzzles. First, we locate important features on the pieces using geometric heuristics and machine learning, and then match pairs of these features to discover collision free states in the puzzle's C-space that lie within the narrow tunnels. Second, we propose a Rapidly-exploring Dense Tree (RDT) motion planner variant that builds tunnel escape roadmaps and then connects these roadmaps into a solution path connecting start and goal states. We evaluate our approach on a variety of challenging disentanglement puzzles and provide extensive baseline comparisons with other motion planning techniques.

Despite the popularity of GPUs in high-performance and scientific computing, and despite increasingly general-purpose hardware capabilities, the use of GPUs in network servers or distributed systems poses significant challenges. GPUnet is a native GPU networking layer that provides a socket abstraction and high-level networking APIs for GPU programs. We use GPUnet to streamline the development of high-performance, distributed applications like in-GPU-memory MapReduce and a new class of low-latency, high-throughput GPU-native network services such as a face verification server.

As the physical link speeds grow and the size of routing table continues to increase, IP address lookup has been a challenging problem at routers. There have been growing demands in achieving high-performance IP lookup cost-effectively. Existing approaches typically resort to specialized hardwares, such as TCAM. While these approaches can take advantage of hardware parallelism to achieve high-performance IP lookup, they also have the disadvantage of high cost. This paper investigates a new way to build a cost-effective IP lookup scheme using graphics processor units (GPU). Our contribution here is to design a practical architecture for high-performance IP lookup engine with GPU, and to develop efficient algorithms for routing prefix update operations such as deletion, insertion, and modification. Leveraging GPU's many-core parallelism, the proposed schemes addressed the challenges in designing IP lookup at GPU-based software routers. Our experimental results on real-world route traces show promising gains in IP lookup and update operations.

Graphics processing unit (GPU) has evolved into a general-purpose computing platform. Inspired by the GPU technology advantage, this paper concerns the design and performance evaluation of practical GPU-accelerated server architecture for Video-on-Demand (VoD) services with network coding. Following

Throughput and programmability have always been the central, but generally conflicting concerns for modern IP router designs. Current high performance routers depend on proprietary hardware solutions, which make it difficult to adapt to ever-changing network protocols. On the other hand, software routers offer the best flexibility and programmability, but could only achieve a throughput one order of magnitude lower. Modern GPUs are offering significant computing power, and its data-parallel computing model well matches the typical patterns of packet processing on routers. Accordingly, in this research we investigate the potential of CUDA-enabled GPUs for IP routing applications. As a first step toward exploring the architecture of a GPU based software router, we developed GPU solutions for a series of core IP routing applications such as IP routing table lookup and pattern match. For the deep packet inspection application, we implemented both a Bloom-filter based string matching algorithm and a finite automata based regular expression matching algorithm. A GPU based routing table lookup solution is also proposed in this work. Experimental results proved that GPU could accelerate the routing processing by one order of magnitude. Our work suggests that, with proper architectural modifications, GPU based software routers could deliver significant higher throughput than previous CPU based solutions.

IP address lookup is a challenging problem due to the increasing routing table size, and higher line rate. This paper investigates a new way to build an efficient IP lookup scheme using graphics processor units(GPU). Our contribution here is to design a basic architecture for high-performance IP lookup engine with GPU, and to develop efficient algorithms for routing prefix operations such as lookup, deletion, insertion, and modification. In particular, the IP lookup scheme can achieve O(1) time complexity. Our experimental results on real-world route traces show promising 6x gains in IP lookup throughput.