KD-MVS: Knowledge Distillation Based Self-supervised Learning for Multi-view Stereo

Supervised multi-view stereo (MVS) methods have achieved remarkable progress in terms of reconstruction quality, but suffer from the challenge of collecting large-scale ground-truth depth. In this paper, we propose a novel self-supervised training pipeline for MVS based on knowledge distillation, termed KD-MVS, which mainly consists of self-supervised teacher training and distillation-based student training. Specifically, the teacher model is trained in a self-supervised fashion using both photometric and featuremetric consistency. Then we distill the knowledge of the teacher model to the student model through probabilistic knowledge transferring. With the supervision of validated knowledge, the student model is able to outperform its teacher by a large margin. Extensive experiments performed on multiple datasets show our method can even outperform supervised methods

Serving MoE Models on Resource-constrained Edge Devices via Dynamic Expert Swapping

Mixture of experts (MoE) is a popular technique in deep learning that improves model capacity with conditionally-activated parallel neural network modules (experts). However, serving MoE models in resource-constrained latency-critical edge scenarios is challenging due to the significantly increased model size and complexity. In this paper, we first analyze the behavior pattern of MoE models in continuous inference scenarios, which leads to three key observations about the expert activations, including temporal locality, exchangeability, and skippable computation. Based on these observations, we introduce PC-MoE, an inference framework for resource-constrained continuous MoE model serving. The core of PC-MoE is a new data structure, Parameter Committee, that intelligently maintains a subset of important experts in use to reduce resource consumption. The optimal configuration of Parameter Committee is found offline by a profiling-guided committee planner, and expert swapping and request handling at runtime are managed by an adaptive committee scheduler. To evaluate the effectiveness of PC-MoE, we conduct experiments using state-of-the-art MoE models on common computer vision and natural language processing tasks. The results demonstrate optimal trade-offs between resource consumption and model accuracy achieved by PC-MoE. For instance, on object detection tasks with the Swin-MoE model, our approach can reduce memory usage and latency by 42.34% and 18.63% with only 0.10% accuracy degradation

Alantolactone exerts anti-proliferative and apoptotic effects on BGC823 and SGC7901 cells via activation of p38MAPK and inhibition of NF-κB signaling pathway

Purpose: To investigate the anti-proliferative and apoptotic influences of alantolactone on gastric carcinoma (GC) cell lines, and the mechanism(s) involved. Methods: Human gastric cancer cell line (BGC823) and gastric adenocarcinoma lymph node metastasis cell line (SGC7901) were maintained in Ham’s F12 medium supplemented with 10 % heatinactivated fetal bovine serum (FBS). In each group of cancer cell line, 5 groups of cells were used: control and four alantolactone groups which were treated with increasing concentrations of alantolactone (5 - 30 μM) for varying periods. Proliferation was determined using MTT assay, while realtime quantitative polymerase chain reaction (qRT-PCR) was used to assay the expressions of apoptosis- and metastasis-related genes. The expressions of p38MAPK and nuclear transcription factor-κB (NF-κB) in BGC823 and SGC7901 cells were measured with Western blotting. Results: Phosphorylated protein (p-p38 protein) expression was significantly higher in both groups of GC cells, relative to control (p < 0.05). The expressions of NF-κB in plasma protein were markedly higher in both groups of GC cells than in control group, but the corresponding expressions in nuclear protein were significantly lower in both groups of GC cells, relative to control (p < 0.05). Conclusion: Alantolactone exerts anti-proliferative and apoptotic effects on BGC823 and SGC7901 cells via mechanisms involving activation of the p38MAPK, and inhibition of the NF-κB signaling pathways. Thus, alantolactone may be a new and effective anti-gastric cancer drug

Privacy-preserving behavioral correctness verification of cross-organizational workflow with task synchronization patterns

Workflow management technology has become a key means to improve enterprise productivity. More and more workflow systems are crossing organizational boundaries and may involve multiple interacting organizations. This article focuses on a type of loosely coupled workflow architecture with collaborative tasks, i.e., each business partner owns its private business process and is able to operate independently, and all involved organizations need to be synchronized at a certain point to complete certain public tasks. Because of each organization's privacy consideration, they are unwilling to share the business details with others. In this way, traditional correctness verification approaches via reachability analysis are not practical as a global business process model is unavailable for privacy preservation. To ensure its globally correct execution, this work establishes a correctness verification approach for the cross-organizational workflow with task synchronization patterns. Its core idea is to use local correctness of each suborganizational workflow process to guarantee its global correctness. We prove that the proposed approach can be used to investigate the behavioral property preservation when synthesizing suborganizational workflows via collaborative tasks. A medical diagnosis running case is used to illustrate the applicability of the proposed approaches

Energy-Efficient Opportunistic Transmission Scheduling for Sparse Sensor Networks with Mobile Relays

Wireless sensing devices have been widely used in civilian and military applications over the past decade. In some application scenarios, the sensors are sparsely deployed in the field and are costly or infeasible to have stable communication links for delivering the collected data to the destined server. A possible solution is to utilize the motion of entities that are already present in the environment to provide opportunistic relaying services for sensory data. In this paper, we design and propose a new scheduling scheme that opportunistically schedules data transmissions based on the optimal stopping theory, with a view of minimizing the energy consumption on network probes for data delivery. In fact, by exploiting the stochastic characteristics of the relay motion, we can postpone the communication up to an acceptable time deadline until the best relay is found. Simulation results validate the effectiveness of the derived optimal strategy

Straightforward data transfer in a blockwise dataflow for an analog RRAM-based CIM system

Analog resistive random-access memory (RRAM)-based computation-in-memory (CIM) technology is promising for constructing artificial intelligence (AI) with high energy efficiency and excellent scalability. However, the large overhead of analog-to-digital converters (ADCs) is a key limitation. In this work, we propose a novel LINKAGE architecture that eliminates PE-level ADCs and leverages an analog data transfer module to implement inter-array data processing. A blockwise dataflow is further proposed to accelerate convolutional neural networks (CNNs) to speed up compute-intensive layers and solve the unbalanced pipeline problem. To obtain accurate and reliable benchmark results, key component modules, such as straightforward link (SFL) modules and Tile-level ADCs, are designed in standard 28 nm CMOS technology. The evaluation shows that LINKAGE outperforms the conventional ADC/DAC-based architecture with a 2.07×∼11.22× improvement in throughput, 2.45×∼7.00× in energy efficiency, and 22%–51% reduction in the area overhead while maintaining accuracy. Our LINKAGE architecture can achieve 22.9∼24.4 TOPS/W energy efficiency (4b-IN/4b-W) and 1.82 ∼4.53 TOPS throughput with the blockwise method. This work demonstrates a new method for significantly improving the energy efficiency of CIM chips, which can be applied to general CNNs/FCNNs