Grant-free (GF) random access has emerged as a promising solution for massive machine-type communications (mMTC). However, the non-uniform distribution of user equipment (UE) and real-world limitations on base station (BS) placement lead to random access imbalance among BSs and heavy access collisions in some cells. To this end, an unsupervised learning-based location-aware random access (ULL-RA) scheme is proposed in this paper to select the accessing BSs and channels simultaneously. Specifically, ULL-RA adopts a deep learning model named ULL-RA-Net, comprising parameter-shared feedforward neural network (FFNN) layers that enable each active UE to select a BS and an access channel to maximize the achievable rate. The model is trained in an unsupervised manner to maximize a designed differentiable objective, mapping UE locations to channel access probabilities. Notably, we propose a rate-collision loss tailored to the model architecture, which combines a collision-free channel capacity term and a sparsity-inducing collision penalty term to reduce access collisions and enhance the achievable rate. Experimental results using the Deep-MIMO dataset indicate that ULL-RA outperforms conventional GF access in both the average achievable rate and the access success rate.

Learning-based methods, such as imitation learning (IL) and reinforcement learning (RL), can produce excel control policies over challenging agile robot tasks, such as sports robot. However, no existing work has harmonized learning-based policy with model-based methods to reduce training complexity and ensure the safety and stability for agile badminton robot control. In this paper, we introduce Hamlet, a novel hybrid control system for agile badminton robots. Specifically, we propose a model-based strategy for chassis locomotion which provides a base for arm policy. We introduce a physics-informed “IL+RL” training framework for learning-based arm policy. In this train framework, a modelbased strategy with privileged information is used to guide arm policy training during both IL and RL phases. In addition, we train the critic model during IL phase to alleviate the performance drop issue when transitioning from IL to RL. We present results on our self-engineered badminton robot, achieving 94.5% success rate against the serving machine and$\mathbf{9 0. 7 \%}$success rate against human players. Our system can be easily generalized to other agile mobile manipulation tasks e.g., agile catching, table tennis. A video demonstrating our system can be viewed at https://youtu.be/8-ixKAD18Mk.

Embedding-based recommendation models (ERMs) require large memory to host huge embedding tables and involve massive data traffic to read the embeddings. As a new interconnect, CXL suits ERMs since it can scale up single-machine memory with performant remote memory devices. However, directly running DRAM-based ERM serving systems on CXL yields poor performance because the bandwidth of CXL is notably lower than DRAM and can be easily saturated, making CXL memory the bottleneck. The non-uniform memory access (NUMA) architecture in modern CXL servers further decreased the system performance. In this paper, we design Carina for ERM serving on heterogeneous memory with CXL by considering such bandwidth asymmetry. In particular, Carina balances the memory access from different memory devices by storing hot embeddings with high access frequencies on DRAM and specifying the placement of embedding tables on the NUMA nodes. Moreover, Carina adopts bandwidth-aware task execution, which decomposes each batch of ERM requests into fine-grained tasks and schedules the tasks to control the real-time utilization of CXL bandwidth to avoid instantaneous saturation. We evaluate Carina under real CXL devices and find that it outperforms a CXL-oblivious baseline by an average of 5.38x and 4.04x in system throughput and request latency, respectively.

Smart contracts manage a large number of digital assets nowadays. Bugs in these contracts have led to significant financial loss. Verifying the correctness of smart contracts is, therefore, an important task. This paper presents an automated safety verification tool, DCV, that targets declarative smart contracts written in De-Con, a logic-based domain-specific language for smart contract implementation and specification. DCV proves safety properties by mathematical induction and can automatically infer inductive invariants using heuristic patterns, without annotations from the developer. Our evaluation on 23 benchmark contracts shows that DCV is effective in verifying smart contracts adapted from public repositories, and can verify contracts not supported by other tools. Furthermore, DCV significantly outperforms baseline tools in verification time.

In the next-generation wireless communication systems, learning-based dynamic spectrum access strategy at the medium access control layer and physical layer shows its powerful capability of achieving optimal resources allocation, and it has become a hot research topic for the harmonious coexistence of heterogeneous wireless networks. In this paper, we propose a multiple access control method to achieve high network throughput by combining deep reinforcement learning and memory module. In specific, we introduce the bidirectional gated recurrent unit (Bi-GRU) in deep Q-learning (DQL) to utilize the information of varying environment observation at each time-step. Furthermore, we apply the method in a freeway scenario with real-world datasets, where the DQL node contends the same wireless channel with other nodes. Evaluated results demonstrate that the proposed approach learns an optimal policy without using complex mechanism or prior. Moreover, we consider realistic cases involving saturated or unsaturated uplink traffic flows of nodes on a freeway segment, and the on-line training strategies of the DQL node near the roadside facilities. The experimental results show that the proposed scheme leads to the highest throughput in all cases compared with the competing approaches.

Program repair is an integral part of every software system's life-cycle but can be extremely challenging. To date, various automated program repair (APR) techniques have been proposed to reduce manual debugging efforts. However, given a real-world buggy program, a typical APR technique can generate a large number of patches, each of which needs to be validated against the original test suite, incurring extremely high computation costs. Although existing APR techniques have already leveraged various static and/or dynamic information to find the desired patches faster, they are still rather costly. In this work, we propose SeAPR (Self-Boosted Automated Program Repair), the first general-purpose technique to leverage the earlier patch execution information during APR to directly boost existing APR techniques themselves on-the-fly. Our basic intuition is that patches similar to earlier high-quality/low-quality patches should be promoted/degraded to speed up the detection of the desired patches. The experimental study on 13 state-of-the-art APR tools demonstrates that, overall, SeAPR can substantially reduce the number of patch executions with negligible overhead. Our study also investigates the impact of various configurations on SeAPR. Lastly, our study demonstrates that SeAPR can even leverage the historical patch execution information from other APR tools for the same buggy program to further boost the current APR tool.

In this paper, we propose the GAIPS framework for efficient maximum inner product search (MIPS) on GPU. We observe that a query can usually find a good lower bound of its maximum inner product in some large norm items that take up only a small portion of the dataset and utilize this fact to facilitate pruning. In addition, we design norm-based, residue-based and hash-based pruning techniques to avoid computation for items that are unlikely to be the MIPS results. Experiment results show that compared with FAISS, the state-of-the-art GPU-based similarity search framework, GAIPS has significantly shorter query processing time at the same recall.

Predicting meme burst is of great relevance to develop security-related detecting and early warning capabilities. In this paper, we propose a feature-based method for real-time meme burst predictions, namely “Semantic, Network, and Time” (SNAT). By considering the potential characteristics of bursty memes, such as the semantics and spatio-temporal characteristics during their propagation, SNAT is capable of capturing meme burst at the very beginning and in real time. Experimental results prove the effectiveness of SNAT in terms of both fixed-time and real-time meme burst prediction tasks.