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Studying extreme ideas in routine choices and discussions is of utmost importance to understand the increasing polarization in society. In this study, we focus on understanding the generation and influence of extreme ideas in routine conversations which we label “eccentric” ideas. The eccentricity of any idea is defined as the deviation of that idea from the norm of the social neighborhood. We collected and analyzed data from two sources of different nature: public social media and online experiments in a controlled environment. We compared the popularity of ideas against their eccentricity to understand individuals’ fascination towards eccentricity. We found that more eccentric ideas have a higher probability of getting a greater number of “likes”. Additionally, we demonstrate that the social neighborhood of an individual conceals eccentricity changes in one’s own opinions and facilitates generation of eccentric ideas at a collective level.

 

As the size and complexity of high-performance computing (HPC) systems keep growing, scientists' ability to trust the data produced is paramount due to potential data corruption for various reasons, which may stay undetected. While employing machine learning-based anomaly detection techniques could relieve scientists of such concern, it is practically infeasible due to the need for labels for volumes of scientific datasets and the unwanted extra overhead associated. In this paper, we exploit spatial sparsity profiles exhibited in scientific datasets and propose an approach to detect anomalies effectively. Our method first extracts block-level sparse representations of original datasets in the transformed domain. Then it learns from the extracted sparse representations and builds the boundary threshold between normal and abnormal without relying on labeled data. Experiments using real-world scientific datasets show that the proposed approach requires 13% on average (less than 10% in most cases and as low as 0.3%) of the entire dataset to achieve competitive detection accuracy (70.74%-100.0%) as compared to two state-of-the-art unsupervised techniques. 

Advances in flexible and printable sensor technologies have made it possible to use posture classification for providing timely services in digital healthcare, especially for bedsores or decubitus ulcers. However, managing a large amount of sensor data and ensuring accurate predictions can be challenging. While lossy compressors can reduce data volume, it is still unclear whether this would lead to losing important information and affect downstream application performance. In this paper, we propose LCDNN (Lossy Compression using Deep Neural Network) to reduce the size of sensor data and evaluate the performance of posture classification models. Our sensors, placed under hospital beds, have a thickness of just 0.4mm and collect pressure data from 28 sensors (7 by 4) at an 8 Hz cycle, categorizing postures into 4 types from 5 patients. Our evaluation, which includes reduced datasets by LCDNN, demonstrates that the results are promising. 

Recent years have witnessed an upsurge of interest in lossy compression due to its potential to significantly reduce data volume with adequate exploitation of the spatiotemporal properties of IoT datasets. However, striking a balance between compression ratios and data fidelity is challenging, particularly when losing data fidelity impacts downstream data analytics noticeably. In this paper, we propose a lossy prediction model dealing with binary classification analytics tasks to minimize the impact of the error introduced due to lossy compression. We specifically focus on five classification algorithms for frost prediction in agricultural fields allowing preparation by the predictive advisories to provide helpful information for timely services. While our experimental evaluations reaffirm the nature of lossy compressions where allowing higher errors offers higher compression ratios, we also observe that the classification performance in terms of accuracy and F-1 score differs among all the algorithms we evaluated. Specifically, random forest is the best lossy prediction model for classifying frost. Lastly, we show the robustness of the lossy prediction model based on the data fidelity in prediction performance. 

As the scale and complexity of high-performance computing (HPC) systems keep growing, data compression techniques are often adopted to reduce the data volume and processing time. While lossy compression becomes preferable to a lossless one because of the potential benefit of generating a high compression ratio, it would lose its worth the effort without finding an optimal balance between volume reduction and information loss. Among many lossy compression techniques, transform-based lossy algorithms utilize spatial redundancy better. However, the transform-based lossy compressor has received relatively less attention because there is a lack of understanding of its compression performance on scientific data sets. The insight of this paper is that, in transform-based lossy compressors, quantifying dominant coefficients at the block level reveals the right balance, potentially impacting overall compression ratios. Motivated by this, we characterize three transformation-based lossy compression mechanisms with different information compaction methods using the statistical features that capture data characteristics. And then, we build several prediction models using the statistical features and the characteristics of dominant coefficients and evaluate the effectiveness of each model using six HPC datasets from three production-level simulations at scale. Our results demonstrate that the random forest classifier captures the behavior of dominant coefficients precisely, achieving nearly 99% of prediction accuracy. 

Reconfigurable modular robots can dynamically assemble/disassemble to accomplish the desired task better. Magnetic modular cubes are scalable modular subunits with embedded permanent magnets in a 3D-printed cubic body and can be wirelessly controlled by an external, uniform, timevarying magnetic field. This paper considers the problem of self-assembling these modules into desired 2D polyomino shapes using such magnetic fields. Although the applied magnetic field is the same for each magnetic modular cube, we use collisions with workspace boundaries to rearrange the cubes. We present a closed-loop control method for self-assembling the magnetic modular cubes into polyomino shapes, using computer vision-based feedback with re-planning. Experimental results demonstrate that the proposed closed-loop control improves the success rate of forming 2D user-specified polyominoes compared to an open-loop baseline. We also demonstrate the validity of the approach over changes in length scales, testing with both 10mm edge length cubes and 2.8mm edge length cubes. 

Alternating current (AC) modulation of command voltage applied across a Self-induced Back Action Actuated Nanopore Electrophoresis (SANE) sensor, a type of plasmonic nanopore sensor that we have developed previously, enables acquisition of new data types that could potentially enhance the characterization of nanoparticles (NPs) and single molecules. In particular, AC voltage frequency response provides insight into the charge and dielectric constant of analytes that are normally obfuscated using DC command voltages. We first analyzed Axopatch 200B data to map the frequency response of the empty SANE sensor in terms of phase shift and amplitude modulation, with and without plasmonic excitation. We then tested the frequency response of 20 nm diameter silica NPs and 20 nm gold NPs trapped optically, which made these particles hover over an underlying 25 nm nanopore at the center of the SANE sensor. By applying a DC command voltage with a superimposed AC frequency sweep while keeping the NPs optically trapped in the vicinity of the nanopores’s entrance, we have found that silica and gold NPs to have distinctly different electrical responses. This pilot work demonstrates the feasibility of performing AC measurements with a plasmonic nanopore, which encourages us to pursue more detailed characterization studies with NPs and single molecules in future work. 

This paper examines a family of designs for magnetic cubes and counts how many configurations are possible for each design as a function of the number of modules. Magnetic modular cubes are cubes with magnets arranged on their faces. The magnets are positioned so that each face has either magnetic south or north pole outward. Moreover, we require that the net magnetic moment of the cube passes through the center of opposing faces. These magnetic arrangements enable coupling when cube faces with opposite polarity are brought in close proximity and enable moving the cubes by controlling the orientation of a global magnetic field. This paper investigates the 2D and 3D shapes that can be constructed by magnetic modular cubes, and describes all possible magnet arrangements that obey these rules. We select ten magnetic arrangements and assign a "color" to each of them for ease of visualization and reference. We provide a method to enumerate the number of unique polyominoes and polycubes that can be constructed from a given set of colored cubes. We use this method to enumerate all arrangements for up to 20 modules in 2D and 16 modules in 3D. We provide a motion planner for 2D assembly and through simulations compare which arrangements require fewer movements to generate and which arrangements are more common. Hardware demonstrations explore the self-assembly and disassembly of these modules in 2D and 3D.