Computer Simulation of Pineapple Growth, Development and Yield

Fruit yield and harvest date of pineapple {Amnas comosus (L.) Merr.] are difficult to predict. Site-specific studies improve the predictability at one location but usually cannot be generalized to other environments. This study examined the effects of plant population density (PPD) and planting date (PD) on pineapple growth and fruiting and the data were used to develop a pineapple growth simulation model. ‘Smooth Cayenne’ pineapple was planted at Kunia, Hawaii; the crop was drip-irrigated. PPDs ranged from 2.61 to 12.81 plants m-2 and PDs were June and August 15, and October 18, 1989. Flower development was forced with ethylene on September 18, 1990. Leaf emergence rate was constant until 200 days after planting (DAP) and then decreased 0.9 leaves 1000-°C-day-1 with each increase in PPD of one plant m-2. Dry weight per plant decreased as PPD increased and as PD was delayed. Light interception reached 95% at a leaf area index of 4 to 5, which was attained at 350 DAP at 12.81 plants m-2 and later as PPD decreased. Dry matter partitioning (DMP) to leaves and stem during vegetative growth was not affected by PPD or PD. DMP to stem during fruiting decreased linearly and DMP to fruit increased curvilinearly as PPD increased and as PD was delayed. Fruit harvest date was delayed seven days for each PPD increase of 2.5 plants m-2 from 2.61 to 12.81 plants m-2. Fruit yield was asymptotically related to PPD; the economic yield-PPD relationship was parabolic. There was no effect of PD on rate of leaf emergence or fruit development. A pineapple simulation model (ALOHA-Pineapple) was developed using data from the experiment and the literature. ALOHA-Pineapple is process-oriented and incremented daily. It simulates the effects of PPD, PD, plant size at planting and forcing, and weather on crop growth and yield. When ALOHA-Pineapple was validated with data from eleven plantings in four locations in Hawaii, pineapple growth, fruit development and yield was simulated with reasonable accuracy although harvest date and yield were over- and under-predicted in some locations. ALOHA-Pineapple has potential to serve as a frame-work for pineapple research and as a decision aid for farmers

Joint Protection Scheme for Deep Neural Network Hardware Accelerators and Models

Deep neural networks (DNNs) are utilized in numerous image processing, object detection, and video analysis tasks and need to be implemented using hardware accelerators to achieve practical speed. Logic locking is one of the most popular methods for preventing chip counterfeiting. Nevertheless, existing logic-locking schemes need to sacrifice the number of input patterns leading to wrong output under incorrect keys to resist the powerful satisfiability (SAT)-attack. Furthermore, DNN model inference is fault-tolerant. Hence, using a wrong key for those SAT-resistant logic-locking schemes may not affect the accuracy of DNNs. This makes the previous SAT-resistant logic-locking scheme ineffective on protecting DNN accelerators. Besides, to prevent DNN models from being illegally used, the models need to be obfuscated by the designers before they are provided to end-users. Previous obfuscation methods either require long time to retrain the model or leak information about the model. This paper proposes a joint protection scheme for DNN hardware accelerators and models. The DNN accelerator is modified using a hardware key (Hkey) and a model key (Mkey). Different from previous logic locking, the Hkey, which is used to protect the accelerator, does not affect the output when it is wrong. As a result, the SAT attack can be effectively resisted. On the other hand, a wrong Hkey leads to substantial increase in memory accesses, inference time, and energy consumption and makes the accelerator unusable. A correct Mkey can recover the DNN model that is obfuscated by the proposed method. Compared to previous model obfuscation schemes, our proposed method avoids model retraining and does not leak model information

Algorithmic Obfuscation for LDPC Decoders

In order to protect intellectual property against untrusted foundry, many logic-locking schemes have been developed. The main idea of logic locking is to insert a key-controlled block into a circuit to make the circuit function incorrectly without right keys. However, in the case that the algorithm implemented by the circuit is naturally fault-tolerant or self-correcting, existing logic-locking schemes do not affect the system performance much even if wrong keys are used. One example is low-density parity-check (LDPC) error-correcting decoder, which has broad applications in digital communications and storage. This paper proposes two algorithmic-level obfuscation methods for LDPC decoders. By modifying the decoding process and locking the stopping criterion, our new designs substantially degrade the decoder throughput and/or error-correcting performance when the wrong key is used. Besides, our designs are also resistant to the SAT, AppSAT and removal attacks. For an example LDPC decoder, our proposed methods reduce the throughput to less than 1/3 and/or increase the decoder error rate by at least two orders of magnitude with only 0.33% area overhead

An Attention-based Collaboration Framework for Multi-View Network Representation Learning

Learning distributed node representations in networks has been attracting increasing attention recently due to its effectiveness in a variety of applications. Existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network. However, in reality there usually exists multiple types of proximities between nodes, yielding networks with multiple views. This paper studies learning node representations for networks with multiple views, which aims to infer robust node representations across different views. We propose a multi-view representation learning approach, which promotes the collaboration of different views and lets them vote for the robust representations. During the voting process, an attention mechanism is introduced, which enables each node to focus on the most informative views. Experimental results on real-world networks show that the proposed approach outperforms existing state-of-the-art approaches for network representation learning with a single view and other competitive approaches with multiple views.Comment: CIKM 201