Development of Discoid Products Stacking Machine Based on PLC

Abstract. Binding the actual production of one sporting goods factory, in order to meet the needs of automatic production line of shooting flying saucer, the discoid products stacking machine was developed based on PLC, carried out mechanical system design of discoid products stacking machine and control system development based on PLC. After production testing proved reasonable structure of mechatronics systems, easy to operate, reliable, able to meet the requirements of automated production, with a strong practical and innovative, have some application value

An Enhanced Drought-Tolerant Method Using SA-Loaded PAMPS Polymer Materials Applied on Tobacco Pelleted Seeds

Drought is one of the most important stress factors limiting the seed industry and crop production. Present study was undertaken to create novel drought-resistant pelleted seeds using the combined materials with superabsorbent polymer, poly(2-acrylamide-2-methyl propane sulfonic acid) (PAMPS) hydrogel, and drought resistance agent, salicylic acid (SA). The optimized PAMPS hydrogel was obtained as the molar ratio of 2-acrylamido-2-methyl-propanesulfonic acid (AMPS) to potassium peroxydisulfate (KPS) and N, N′-methylene-bis-acrylamide (MBA) was 1 : 0.00046 : 0.00134. The hydrogel weight after swelling in deionized water for 24 h reached 4306 times its own dry weight. The water retention ratio (RR) of PAMPS was significantly higher as compared with the control. It could keep as high as 85.3% of original weight after 30 min at 110°C; even at 25°C for 40 d, the PAMPS still kept RR at 33.67%. PAMPS disintegration ratio increased gradually and reached around 30% after embedding in soil or activated sludge for 60 d. In addition, there were better seed germination performance and seedling growth in the pelleted treatments with SA-loaded PAMPS hydrogel under drought stress than control. It suggested that SA-loaded PAMPS hydrogel, a nontoxic superabsorbent polymer, could be used as an effective drought resistance material applied to tobacco pelleted seeds

PUMA: Secure Inference of LLaMA-7B in Five Minutes

With ChatGPT as a representative, tons of companies have began to provide services based on large Transformers models. However, using such a service inevitably leak users' prompts to the model provider. Previous studies have studied secure inference for Transformer models using secure multiparty computation (MPC), where model parameters and clients' prompts are kept secret. Despite this, these frameworks are still limited in terms of model performance, efficiency, and deployment. To address these limitations, we propose framework PUMA to enable fast and secure Transformer model inference. Our framework designs high quality approximations for expensive functions, such as GeLU and Softmax, which significantly reduce the cost of secure inference while preserving the model performance. Additionally, we design secure Embedding and LayerNorm procedures that faithfully implement the desired functionality without undermining the Transformer architecture. PUMA is about 2x faster than the state-of-the-art MPC framework MPCFORMER(ICLR 2023) and has similar accuracy as plaintext models without fine-tuning (which the previous works failed to achieve). One more thing, PUMA can evaluate LLaMA-7B in around 5 minutes to generate 1 token. To our best knowledge, this is the first time that a model with such a parameter size is able to be evaluated under MPC. PUMA has been open-sourced in the Github repository of SecretFlow-SPU

MPIWiz: subgroup reproducible replay of MPI applications

ABSTRACT Message Passing Interface (MPI) is a widely used standard for managing coarse-grained concurrency on distributed computers. Debugging parallel MPI applications, however, has always been a particularly challenging task due to their high degree of concurrent execution and non-deterministic behavior. Deterministic replay is a potentially powerful technique for addressing these challenges, with existing MPI replay tools adopting either data-replay or orderreplay approaches. Unfortunately, each approach has its tradeoffs. Data-replay generates substantial log sizes by recording every communication message. Order-replay generates small logs, but requires all processes to be replayed together. We believe that these drawbacks are the primary reasons that inhibit the wide adoption of deterministic replay as the critical enabler of cyclic debugging of MPI applications. This paper describes subgroup reproducible replay (SRR), a hybrid deterministic replay method that provides the benefits of both data-replay and order-replay while balancing their trade-offs. SRR divides all processes into disjoint groups. It records the contents of messages crossing group boundaries as in data-replay, but records just message orderings for communication within a group as in order-replay. In this way, SRR can exploit the communication locality of traffic patterns in MPI applications. During replay, developers can then replay each group individually. SRR reduces recording overhead by not recording intra-group communication, and at the same time reduces replay overhead by limiting the size of each replay group. Exposing these tradeoffs gives the user the necessary control for making deterministic replay practical for MPI applications. We have implemented a prototype, MPIWiz, to demonstrate and evaluate SRR. MPIWiz employs a replay framework that allows transparent binary instrumentation of both library and system calls. As a result, MPIWiz replays MPI applications with no source code modification and relinking, and handles non-determinism in both MPI and OS system calls. Our preliminary results show that MPIWiz can reduce recording overhead by over a factor of four relative to data-replay, yet without requiring the entire application to be replayed as in order-replay. Recording increases execution time by 27% while the application can be replayed in just 53% of its base execution time

Dual adversarial models with cross-coordination consistency constraint for domain adaption in brain tumor segmentation

The brain tumor segmentation task with different domains remains a major challenge because tumors of different grades and severities may show different distributions, limiting the ability of a single segmentation model to label such tumors. Semi-supervised models (e.g., mean teacher) are strong unsupervised domain-adaptation learners. However, one of the main drawbacks of using a mean teacher is that given a large number of iterations, the teacher model weights converge to those of the student model, and any biased and unstable predictions are carried over to the student. In this article, we proposed a novel unsupervised domain-adaptation framework for the brain tumor segmentation task, which uses dual student and adversarial training techniques to effectively tackle domain shift with MR images. In this study, the adversarial strategy and consistency constraint for each student can align the feature representation on the source and target domains. Furthermore, we introduced the cross-coordination constraint for the target domain data to constrain the models to produce more confident predictions. We validated our framework on the cross-subtype and cross-modality tasks in brain tumor segmentation and achieved better performance than the current unsupervised domain-adaptation and semi-supervised frameworks

GmNF-YC4-2 Increases Protein, Exhibits Broad Disease Resistance and Expedites Maturity in Soybean

The NF-Y gene family is a highly conserved set of transcription factors. The functional transcription factor complex is made up of a trimer between NF-YA, NF-YB, and NF-YC proteins. While mammals typically have one gene for each subunit, plants often have multigene families for each subunit which contributes to a wide variety of combinations and functions. Soybean plants with an overexpression of a particular NF-YC isoform GmNF-YC4-2 (Glyma.04g196200) in soybean cultivar Williams 82, had a lower amount of starch in its leaves, a higher amount of protein in its seeds, and increased broad disease resistance for bacterial, viral, and fungal infections in the field, similar to the effects of overexpression of its isoform GmNF-YC4-1 (Glyma.06g169600). Interestingly, GmNF-YC4-2-OE (overexpression) plants also filled pods and senesced earlier, a novel trait not found in GmNF-YC4-1-OE plants. No yield difference was observed in GmNF-YC4-2-OE compared with the wild-type control. Sequence alignment of GmNF-YC4-2, GmNF-YC4-1 and AtNF-YC1 indicated that faster maturation may be a result of minor sequence differences in the terminal ends of the protein compared to the closely related isoforms

Taming hardware event samples for FDO compilation

Feedback-directed optimization (FDO) is effective in improving application runtime performance, but has not been widely adopted due to the tedious dual-compilation model, the difficulties in generating representative training data sets, and the high runtime overhead of profile collection. The use of hardware-event sampling to generate estimated edge profiles overcomes these drawbacks. Yet, hardware event samples are typically not precise at the instruction or basic-block granularity. These inaccuracies lead to missed performance when compared to instrumentation-based FDO@. In this paper, we use multiple hardware event profiles and supervised learning techniques to generate heuristics for improved precision of basic-block-level sample profiles, and to further improve the smoothing algorithms used to construct edge profiles. We demonstrate that sampling-based FDO can achieve an average of 78% of the performance gains obtained using instrumentation-based exact edge profiles for SPEC2000 benchmarks, matching or beating instrumentation-based FDO in many cases. The overhead of collection is only 0.74% on average, while compiler based instrumentation incurs 6.8%-53.5% overhead (and 10x overhead on an industrial web search application), and dynamic instrumentation incurs 28.6%-1639.2% overhead. ? 2010 ACM.EI