Tuning the magnetic properties of NiPS3 through organic-ion intercalation

Atomically thin van der Waals magnetic crystals are characterized by tunable magnetic properties related to their low dimensionality. While electrostatic gating has been used to tailor their magnetic response, chemical...

Chaos analysis of pressure parameter over Ahmedabad

In the present study attempt has been made to obtain the dimensionality of atmosphere by using Grassberger and Proccacia's model of correlation dimension on pressure parameter for Ahmedabad station. Based on single variable time series, the dimension of pressure at tractor is evaluated to obtain a lower bound on the number of essential variables necessary to model atmospheric dynamics. A low dimensionality of the order of five to seven for the pressure variable was obtained if interannual and seasonal variabilities are excluded by using seasonal data.

A systems framework for remedying dysfunction in US democracy

Democracy often fails to meet its ideals, and these failures may be made worse by electoral institutions. Unwanted outcomes include elite polarization, unresponsive representatives, and the ability of a faction of voters to gain power at the expense of the majority. Various reforms have been proposed to address these problems, but their effectiveness is difficult to predict against a backdrop of complex interactions. Here we outline a path for systems-level modeling to help understand and optimize repairs to US democracy. Following the tradition of engineering and biology, models of systems include mechanisms with dynamical properties that include nonlinearities and amplification (voting rules), positive feedback mechanisms (single-party control, gerrymandering), negative feedback (checks and balances), integration over time (lifetime judicial appointments), and low dimensionality (polarization). To illustrate a systems-level approach, we analyze three emergent phenomena: low dimensionality, elite polarization, and antimajoritarianism in legislatures. In each case, long-standing rules now contribute to undesirable outcomes as a consequence of changes in the political environment. Theoretical understanding at a general level will also help evaluate whether a proposed reform’s benefits will materialize and be lasting, especially as conditions change again. In this way, rigorous modeling may not only shape new lines of research but aid in the design of effective and lasting reform.

Tuning the magnetic properties of NiPS3 through organic-ion intercalation

Atomically thin van der Waals magnetic crystals are characterized by tunable magnetic properties related to their low dimensionality. While electrostatic gating has been used to tailor their magnetic response, chemical approaches like intercalation remain largely unexplored. Here, we demonstrate the manipulation of the magnetism in the van der Waals antiferromagnet NiPS3 through the intercalation of different organic cations, inserted using an engineered two-step process. First, the electrochemical intercalation of tetrabutylammonium cations (TBA+) results in a ferrimagnetic hybrid compound displaying a transition temperature of 78 K, and characterized by a hysteretic behavior with finite remanence and coercivity. Then, TBA+ cations are replaced by cobaltocenium via an ion-exchange process, yielding a ferrimagnetic phase with higher transition temperature (98 K) and higher remanent magnetization. Importantly, we demonstrate that the intercalation and cation exchange processes can be carried out in bulk crystals and few-layer flakes, opening the way to the integration of intercalated magnetic materials in devices.

New Low-Dimensional Hybrid Perovskitoids Based on Lead Bromide with Organic Cations from Charge-Transfer Complexes

We have obtained a series of low-dimensional hybrid perovskitoids (often referred to as perovskites) based on lead bromide. As organic cations, the derivatives of polyaromatic and conjugated molecules, such as anthracene, pyrene and (E)-stilbene, were chosen to form charge-transfer complexes with various organic acceptors for use as highly tunable components of hybrid perovskite solar cells. X-ray diffraction analysis showed these crystalline materials to be new 1D- and pseudo-layered 0D-perovskitoids with lead bromide octahedra featuring different sharing modes, such as in unusual mini-rods of four face- and edge-shared octahedra. Thanks to the low dimensionality, they can be of use in another type of optoelectronic device, photodetectors.

Credit Card Fraud Detection with Autoencoder and Probabilistic Random Forest

This paper proposes a method, called autoencoder with probabilistic random forest (AEPRF), for detecting credit card frauds. The proposed AE-PRF method first utilizes the autoencoder to extract features of low-dimensionality from credit card transaction data features of high-dimensionality. It then relies on the random forest, an ensemble learning mechanism using the bootstrap aggregating (bagging) concept, with probabilistic classification to classify data as fraudulent or normal. The credit card fraud detection (CCFD) dataset is applied to AE-PRF for performance evaluation and comparison. The CCFD dataset contains large numbers of credit card transactions of European cardholders; it is highly imbalanced since its normal transactions far outnumber fraudulent transactions. Data resampling schemes like the synthetic minority oversampling technique (SMOTE), adaptive synthetic (ADASYN), and Tomek link (T-Link) are applied to the CCFD dataset to balance the numbers of normal and fraudulent transactions for improving AE-PRF performance. Experimental results show that the performance of AE-PRF does not vary much whether resampling schemes are applied to the dataset or not. This indicates that AE-PRF is naturally suitable for dealing with imbalanced datasets. When compared with related methods, AE-PRF has relatively excellent performance in terms of accuracy, the true positive rate, the true negative rate, the Matthews correlation coefficient, and the area under the receiver operating characteristic curve.

IMU-Based Hand Gesture Interface Implementing a Sequence-Matching Algorithm for the Control of Assistive Technologies

Assistive technologies (ATs) often have a high-dimensionality of possible movements (e.g., assistive robot with several degrees of freedom or a computer), but the users have to control them with low-dimensionality sensors and interfaces (e.g., switches). This paper presents the development of an open-source interface based on a sequence-matching algorithm for the control of ATs. Sequence matching allows the user to input several different commands with low-dimensionality sensors by not only recognizing their output, but also their sequential pattern through time, similarly to Morse code. In this paper, the algorithm is applied to the recognition of hand gestures, inputted using an inertial measurement unit worn by the user. An SVM-based algorithm, that is aimed to be robust, with small training sets (e.g., five examples per class) is developed to recognize gestures in real-time. Finally, the interface is applied to control a computer’s mouse and keyboard. The interface was compared against (and combined with) the head movement-based AssystMouse software. The hand gesture interface showed encouraging results for this application but could also be used with other body parts (e.g., head and feet) and could control various ATs (e.g., assistive robotic arm and prosthesis).

A novel computer vision-based data driven modelling approach for person specific fall detection

In this paper, we propose a novel person specific fall detection system based on a monocular camera, which can be applied for assisting the independent living of an older adult living alone at home. A single camera covering the living area is used for video recordings of an elderly person’s normal daily activities. From the recorded video data, the human silhouette regions in every frame are then extracted based on the codebook background subtraction technique. Low-dimensionality representative features of extracted silhouetted are then extracted by convolutional neural network-based autoencoder (CNN-AE). Features obtained from the CNN-AE are applied to construct an one class support vector machine (OCSVM) model, which is a data driven model based on the video recordings and can be applied for fall detection. From the comprehensive experimental evaluations on different people in a real home environment, it is shown that the proposed fall detection system can successfully detect different types of falls (falls towards different orientations at different positions in a real home environment) with small false alarms.

Flexible Recruitments of Fundamental Muscle Synergies in the Trunk and Lower Limbs for Highly Variable Movements and Postures

The extent to which muscle synergies represent the neural control of human behavior remains unknown. Here, we tested whether certain sets of muscle synergies that are fundamentally necessary across behaviors exist. We measured the electromyographic activities of 26 muscles, including bilateral trunk and lower limb muscles, during 24 locomotion, dynamic and static stability tasks, and we extracted the muscle synergies using non-negative matrix factorization. Our results show that 13 muscle synergies that may have unique functional roles accounted for almost all 24 tasks by combinations of single and/or merging of synergies. Therefore, our results may support the notion of the low dimensionality in motor outputs, in which the central nervous system flexibly recruits fundamental muscle synergies to execute diverse human behaviors. Further studies are required to validate the neural representation of the fundamental components of muscle synergies.

MIDI2vec: Learning MIDI embeddings for reliable prediction of symbolic music metadata

An important problem in large symbolic music collections is the low availability of high-quality metadata, which is essential for various information retrieval tasks. Traditionally, systems have addressed this by relying either on costly human annotations or on rule-based systems at a limited scale. Recently, embedding strategies have been exploited for representing latent factors in graphs of connected nodes. In this work, we propose MIDI2vec, a new approach for representing MIDI files as vectors based on graph embedding techniques. Our strategy consists of representing the MIDI data as a graph, including the information about tempo, time signature, programs and notes. Next, we run and optimise node2vec for generating embeddings using random walks in the graph. We demonstrate that the resulting vectors can successfully be employed for predicting the musical genre and other metadata such as the composer, the instrument or the movement. In particular, we conduct experiments using those vectors as input to a Feed-Forward Neural Network and we report good comparable accuracy scores in the prediction with respect to other approaches relying purely on symbolic music, avoiding feature engineering and producing highly scalable and reusable models with low dimensionality. Our proposal has real-world applications in automated metadata tagging for symbolic music, for example in digital libraries for musicology, datasets for machine learning, and knowledge graph completion.