Domain Adaptation for Sustainable Soil Management using Causal and Contrastive Constraint Minimization

Monitoring organic matter is pivotal for maintaining soil health and can help inform sustainable soil management practices. While sensor-based soil information offers higher-fidelity and reliable insights into organic matter changes, sampling and measuring sensor data is cost-prohibitive. We propose a multi-modal, scalable framework that can estimate organic matter from remote sensing data, a more readily available data source while leveraging sparse soil information for improving generalization. Using the sensor data, we preserve underlying causal relations among sensor attributes and organic matter. Simultaneously we leverage inherent structure in the data and train the model to discriminate among domains using contrastive learning. This causal and contrastive constraint minimization ensures improved generalization and adaptation to other domains. We also shed light on the interpretability of the framework by identifying attributes that are important for improving generalization. Identifying these key soil attributes that affect organic matter will aid in efforts to standardize data collection efforts.Comment: Neurips workshop on Tackling Climate Change 202

DBO: Response Time Fairness for Cloud-Hosted Financial Exchanges

In this paper, we consider the problem of hosting financial exchanges in the cloud. Financial exchanges require predictable, equal latency to all market participants to ensure fairness for various tasks, such as high speed trading. However, it is extremely difficult to ensure equal latency to all market participants in existing cloud deployments, because of various reasons, such as congestion, and unequal network paths. In this paper, we address the unfairness that stems from lack of determinism in cloud networks. We argue that predictable or bounded latency is not necessary to achieve fairness. Inspired by the use of logical clocks in distributed systems, we present Delivery Based Ordering (DBO), a new approach that ensures fairness by instead correcting for differences in latency to the participants. We evaluate DBO both in our hardware test bed and in a public cloud deployment and demonstrate that it is feasible to achieve guaranteed fairness and sub-100 microsecond latency while operating at high transaction rates

Machine learning can guide experimental approaches for protein digestibility estimations

Food protein digestibility and bioavailability are critical aspects in addressing human nutritional demands, particularly when seeking sustainable alternatives to animal-based proteins. In this study, we propose a machine learning approach to predict the true ileal digestibility coefficient of food items. The model makes use of a unique curated dataset that combines nutritional information from different foods with FASTA sequences of some of their protein families. We extracted the biochemical properties of the proteins and combined these properties with embeddings from a Transformer-based protein Language Model (pLM). In addition, we used SHAP to identify features that contribute most to the model prediction and provide interpretability. This first AI-based model for predicting food protein digestibility has an accuracy of 90% compared to existing experimental techniques. With this accuracy, our model can eliminate the need for lengthy in-vivo or in-vitro experiments, making the process of creating new foods faster, cheaper, and more ethical.Comment: 50 pages, submitted to Nature Foo

Opportunistic Use of Client Repeaters to Improve Performance of WLANs

Currently deployed IEEE 802.11WLANs (Wi-Fi networks) share access point (AP) bandwidth on a per-packet basis. However, the various stations communicating with the AP often have different signal qualities, resulting in different transmission rates. This induces a phenomenon known as the rate anomaly problem, in which stations with lower signal quality transmit at lower rates and consume a significant majority of airtime, thereby dramatically reducing the throughput of stations transmitting at high rates. We propose a practical, deployable system, called SoftRepeater, in which stations cooperatively address the rate anomaly problem. Specifically, higher-rate Wi-Fi stations opportunistically transform themselves into repeaters for stations with low data-rates when transmitting to/from the AP. The key challenge is to determine when it is beneficial to enable the repeater functionality. In this paper, we propose an initiation protocol that ensures that repeater functionality is enabled only when appropriate. Also, our system can run directly on top of today's 802.11 infrastructure networks. We also describe a novel, zero-overhead network coding scheme that further alleviates undesirable symptoms of the rate anomaly problem. We evaluate our system using simulation and testbed implementation, and find that SoftRepeater can improve cumulative throughput by up to 200%

Towards Large-Scale Learned Solvers for Parametric PDEs with Model-Parallel Fourier Neural Operators

Fourier neural operators (FNOs) are a recently introduced neural network architecture for learning solution operators of partial differential equations (PDEs), which have been shown to perform significantly better than comparable approaches based on convolutional networks. Once trained, FNOs can achieve speed-ups of multiple orders of magnitude over conventional numerical PDE solvers. However, due to the high dimensionality of their input data and network weights, FNOs have so far only been applied to two-dimensional or small three-dimensional problems. To remove this limited problem-size barrier, we propose a model-parallel version of FNOs based on domain-decomposition of both the input data and network weights. We demonstrate that our model-parallel FNO is able to predict time-varying PDE solutions of over 3.2 billions variables on Summit using up to 768 GPUs and show an example of training a distributed FNO on the Azure cloud for simulating multiphase CO$_2$ dynamics in the Earth's subsurface

On the Energy Overhead of Mobile Storage Systems

Abstract Secure digital cards and embedded multimedia cards are pervasively used as secondary storage devices in portable electronics, such as smartphones and tablets. These devices cost under 70 cents per gigabyte. They deliver more than 4000 random IOPS and 70 MBps of sequential access bandwidth. Additionally, they operate at a peak power lower than 250 milliwatts. However, software storage stack above the device level on most existing mobile platforms is not optimized to exploit the low-energy characteristics of such devices. This paper examines the energy consumption of the storage stack on mobile platforms. We conduct several experiments on mobile platforms to analyze the energy requirements of their respective storage stacks. Software storage stack consumes up to 200 times more energy when compared to storage hardware, and the security and privacy requirements of mobile apps are a major cause. A storage energy model for mobile platforms is proposed to help developers optimize the energy requirements of storage intensive applications. Finally, a few optimizations are proposed to reduce the energy consumption of storage systems on these platforms