News and Events
Tensor decomposition is an unsupervised learning methodology that has applications in a wide variety of domains, including chemometrics, criminology, and neuroscience. We focus on low-rank tensor decomposition, which is the minimum of some nonlinear optimization problem. The usual objective function is the sum of squares error (SSE). This leads to a nicely structured problem with linear least squares subproblems, which can be solved efficiently in closed form. However, the SSE metric is not always ideal. Thus, we consider using other objective functions. For instance, Kullback–Leibler (KL) divergence is an alternative metric that is useful for count data and results in a nonnegative factorization. We can also consider various objectives such as logistic odds for binary data, beta-divergence for nonnegative data, and so on. We show the benefits of alternative objective functions on real-world data sets. We also consider how these methods scale to large-scale datasets using stochastic optimization with a specialized sampling procedure. Along the way, we also pose some open problems.
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Selected Publications
This paper describes a compact, highly scalable, low-power, and multi-channel charge digitizer (MCCD) designed for synchrotron beam profile monitoring. The MCCD utilizes charge amplifiers, voltage amplifiers, programmable-gain ampli-fiers (PGAs), and ADCs to amplify and digitize input signals. The MCCD also demonstrates a novel stackable PCB design to easily reconfigure the channel count. Laboratory measurement results show a sample rate of 200 Hz per channel, gain of 6.64×1011 V/C, noise of 1.36×105e−rms , and power of 97 mW/channel.
Advances in artificial intelligence (AI) in the medical sector necessitate the development of AI literacy among future physicians. This article explores the pioneering efforts of the AI in Medicine Association (AIM) at Brigham Young University, which offers a framework for undergraduate pre-medical students to gain hands-on experience, receive principled education, explore ethical considerations, and learn appraisal of AI models. By supplementing formal, university-organized pre-medical education with a student-led, faculty-supported introduction to AI through an extracurricular academic association, AIM alleviates apprehensions regarding AI in medicine early and empowers students preparing for medical school to navigate the evolving landscape of AI in healthcare responsibly.
The mathematical structure, description and classification of magnetic space groups is briefly reviewed, with special emphasis on the recently proposed notation, the so-called UNI symbols [Campbell et al. (2022). Acta Cryst. A78, 99–106]. As illustrative examples, very simple magnetic space groups from each of the four possible types are described in detail.
In aerospace and acoustical research, there has been significant focus on understanding the negative effects of noise from jet aircraft and flyover vehicles. However, there has been relatively little investigation into the specific noise impacts of launch vehicles. Despite a considerable rise in the number of launches from various spaceports globally in the past decade, there appears to be poor understanding of the potential harm these increasing launches could pose to nearby communities, including both humans and wildlife. This paper aims to apply established noise metrics such as overall sound pressure level, sound exposure level, and effective perceived noise level, commonly used in aircraft policy, to quantify the potential noise impact of launch vehicles, using measurements from the Artemis-I mission as a case study.
CrMnFeCoNi, also called the Cantor alloy, is a well-known high-entropy alloy whose magnetic properties have recently become a focus of attention. We present a detailed muon spin relaxation study of the influence of chemical composition and sample processing protocols on the magnetic phase transitions and spin dynamics of several different Cantor alloy samples. Specific samples studied include a pristine equiatomic sample, samples with deficient and excess Mn content, and equiatomic samples magnetized in a field of 9 T or plastically deformed in pressures up to 0.5 GPa. The results confirm the sensitive dependence of the transition temperature on composition and demonstrate that post-synthesis pressure treatments cause the transition to become significantly less homogeneous throughout the sample volume. In addition, we observe critical spin dynamics in the vicinity of the transition in all samples, reminiscent of canonical spin glasses and magnetic materials with ideal continuous phase transitions. Application of an external magnetic field suppresses the critical dynamics in the Mn-deficient sample, while the equiatomic and Mn-rich samples show more robust critical dynamics. The spin-flip thermal activation energy in the paramagnetic phase increases with Mn content, ranging from 3.1(3) ×10−21J for 0% Mn to 1.2(2)×10−20J for 30% Mn content. These results shed light on critical magnetic behavior in environments of extreme chemical disorder and demonstrate the tunability of spin dynamics in the Cantor alloy via chemical composition and sample processing.
In medical infections such as blood sepsis and in food quality control, fast and accurate bacteria analysis is required. Using magnetic nanoparticles (MNPs) for bacterial capture and concentration is very promising for rapid analysis. When MNPs are functionalized with the proper surface chemistry, they have the ability to bind to bacteria and aid in the removal and concentration of bacteria from a sample for further analysis. This study introduces a novel approach for bacterial concentration using polydopamine (pDA), a highly adhesive polymer often purported to create antibacterial and antibiofouling coatings on medical devices. Although pDA has been generally studied for its ability to coat surfaces and reduce biofilm growth, we have found that when coated on magnetic nanoclusters (MNCs), more specifically iron oxide nanoclusters, it effectively binds to and can remove from suspension some types of bacteria. This study investigated the binding of pDA-coated MNCs (pDA-MNCs) to various Gram-negative and Gram-positive bacteria, including Staphylococcus aureus, Staphylococcus epidermidis, Pseudomonas aeruginosa, and several E. coli strains. MNCs were successfully coated with pDA, and these functionalized MNCs bound a wide variety of bacterial strains. The efficiency of removing bacteria from a suspension can range from 0.99 for S. aureus to 0.01 for an E. coli strain. Such strong capture and differential capture have important applications in collecting bacteria from dilute samples found in medical diagnostics, food and water quality monitoring, and other industries.