Miracle-dz/UniFAR-FTUs huggingface dataset:
Environmental sound analysis with mixup based multitask learning and cross-task fusion

 Abstract—Environmental sound analysis is currently getting more and more attentions. In the domain, acoustic scene classification and acoustic event classification are two closely related tasks. In this letter, a two-stage method is proposed for the above tasks. In the first stage, a mixup based MTL solution is proposed to classify both tasks in one single convolutional neural network. Artificial multi-label samples are used in the training of the MTL model, which are mixed up using existing single-task datasets. The multitask model obtained can effectively recognize both the acoustic scenes and events. Compared with other methods such as re-annotation or synthesis, the mixup based MTL is low-cost, flexible and effective. In the second stage, the MTL model is modified into a single-task model which is fine-tuned using the original dataset corresponding to the specific task. By controlling the frozen layers carefully, the task-specific high level features are fused and the performance of the single classification task is further improved. The proposed method has confirmed the complementary characteristics of acoustic scene and acoustic event classifications. Finally, enhanced by ensemble learning, a satisfactory accuracy of 84.5 percent on TUT acoustic scene 2017 dataset and an accuracy of 77.5 percent on ESC-50 dataset are achieved respectively.
Session-aware Linear Item-Item Models for Session-based Recommendation

Session-based recommendation aims at predicting the next item given a sequence of previous items consumed in the session, e.g., on e-commerce or multimedia streaming services. Specifically, session data exhibits some unique characteristics, i.e., session consistency and sequential dependency over items within the session, repeated item consumption, and session timeliness. In this paper, we propose simple-yet-effective linear models for considering the holistic aspects of the sessions. The comprehensive nature of our models helps improve the quality of session-based recommendation. More importantly, it provides a generalized framework for reflecting different perspectives of session data. Furthermore, since our models can be solved by closed-form solutions, they are highly scalable. Experimental results demonstrate that the proposed linear models show competitive or state-of-the-art performance in various metrics on several real-world datasets.
Generalizable physics-constrained modeling using learning and inference assisted by feature-space engineering

This work presents a formalism to improve the predictive accuracy of physical models by learning generalizable augmentations from sparse data. Building on recent advances in data-driven turbulence modeling, the present approach, referred to as Learning and Inference assisted by Featurespace Engineering (LIFE), is based on the hypothesis that robustness and generalizability demand a meticulously-designed feature space that is informed by the underlying physics, and a carefully constructed features-to-augmentation map. The critical components of this approach are: (1) Maintaining consistency across the learning and prediction environments to make the augmentation case-agnostic; (2) Tightly-coupled inference and learning by constraining the augmentation to be learnable throughout the inference process to avoid loss of inferred information (and hence accuracy); (3) Identification of relevant physics-informed features in appropriate functional forms to enable significant overlap in feature space for a wide variety of cases to promote generalizability; (4) Localized learning, i.e. maintaining explicit control over feature space to change the augmentation function behavior only in the vicinity of available datapoints. To demonstrate the viability of this approach, it is used in the modeling of bypass transition. The augmentation is developed on skin friction data from two flat plate cases from the ERCOFTAC dataset. Piecewise linear interpolation on a structured grid in feature-space is used as a sample functional form for the augmentation to demonstrate the capability of localized learning. The impact of using a different function class (neural network) is also assessed. The augmented model is then applied to a variety of flat plate cases which are characterized by different freestream turbulence intensities, pressure gradients, and Reynolds numbers. The predictive capability of the augmented model is also tested on single-stage high-pressure-turbine cascade cases, and the model performance is analyzed from the perspective of information contained in the feature space. The results show consistent improvements across these cases, as long as the physical phenomena in question are well-represented in the training. ∗Electronic address: vsriv@umich.edu †Electronic address: kdur@umich.edu
Repeated Acoustic Vaporization of Perfluorohexane Nanodroplets for Contrast-Enhanced Ultrasound Imaging

Superheated perfluorocarbon nanodroplets are emerging ultrasound imaging contrast agents that boast biocompatible components, unique phase-change dynamics, and therapeutic loading capabilities. Upon exposure to a sufficiently high-intensity pulse of acoustic energy, the nanodroplet’s perfluorocarbon core undergoes a liquid-to-gas phase change and becomes an echogenic microbubble, providing ultrasound contrast. The controllable activation leads to high-contrast images, while the small size of the nanodroplets promotes longer circulation times and better in vivo stability. One drawback, however, is that the nanodroplets can only be vaporized a single time, limiting their versatility. Recently, we and others have addressed this issue by using a perfluorohexane core, which has a boiling point above body temperature. Thus after vaporization, the microbubbles recondense back into their stable nanodroplet form. Previous work with perfluorohexane nanodroplets relied on optical activation via pulsed laser absorption of an encapsulated dye. This strategy limits the imaging depth and temporal resolution of the method. In this study, we overcome these limitations by demonstrating acoustic droplet vaporization with 1.1-MHz high-intensity focused ultrasound (HIFU). A short-duration, high-amplitude pulse of focused ultrasound provides a sufficiently strong peak negative pressure to initiate vaporization. A custom imaging sequence was developed to enable the synchronization of a HIFU transducer and a linear array imaging transducer. We show a visualization of repeated acoustic activation of perfluorohexane nanodroplets in polyacrylamide tissue-mimicking phantoms. We further demonstrate the detection of hundreds of vaporization events from individual nanodroplets with activation thresholds well below the tissue cavitation limit. Overall, this approach has the potential to result in reliable and repeatable contrast-enhanced ultrasound imaging at clinically relevant depths.
Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Codesign

Editor’s notes: This work is an introduction and a survey for the Special Issue on Machine Intelligence at the Edge. The authors argue that workloads that were formerly performed in the cloud are increasingly moving to resource-limited edge computing systems, which raises a new set of challenges for machine learning as well as new opportunities. The topic is introduced by means of building blocks ranging from edge fundamentals to edge AI enabling methodologies as well as future trends and challenges. —Jörg Henkel, Karlsruhe Institute of Technology
Infrared variable stars in the compact elliptical galaxy M32

Variable stars in the compact elliptical galaxy M32 are identified, using three epochs of photometry from the Spitzer Space Telescope at 3.6 and 4.5 μm, separated by 32 to 381 d. We present a high-fidelity catalogue of sources detected in multiple epochs at both 3.6 and 4.5 μm, which we analysed for stellar variability using a joint probability error-weighted flux difference. Of these, 83 stars are identified as candidate large-amplitude, long-period variables, with 28 considered high-confidence variables. The majority of the variable stars are classified as asymptotic giant branch star candidates using colour-magnitude diagrams. We find no evidence supporting a younger, infrared-bright stellar population in our M32 field.
Dynamically Modelling Heterogeneous Higher-Order Interactions for Malicious Behavior Detection in Event Logs

Anomaly detection in event logs is a promising approach for intrusion detection in enterprise networks. By building a statistical model of usual activity, it aims to detect multiple kinds of malicious behavior, including stealthy tactics, techniques and procedures (TTPs) designed to evade signature-based detection systems. However, finding suitable anomaly detection methods for event logs remains an important challenge. This results from the very complex, multi-faceted nature of the data: event logs are not only combinatorial, but also temporal and heterogeneous data, thus they fit poorly in most theoretical frameworks for anomaly detection. Most previous research focuses on either one of these three aspects, building a simplified representation of the data that can be fed to standard anomaly detection algorithms. In contrast, we propose to simultaneously address all three of these characteristics through a specifically tailored statistical model. We introduce \textsc{Decades}, a \underline{d}ynamic, h\underline{e}terogeneous and \underline{c}ombinatorial model for \underline{a}nomaly \underline{d}etection in \underline{e}vent \underline{s}treams, and we demonstrate its effectiveness at detecting malicious behavior through experiments on a real dataset containing labelled red team activity. In particular, we empirically highlight the importance of handling the multiple characteristics of the data by comparing our model with state-of-the-art baselines relying on various data representations.
3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding

The ability to understand the ways to interact with objects from visual cues, a.k. a. visual affordance, is essential to vision-guided robotic research. This involves categorizing, segmenting and reasoning of visual affordance. Relevant studies in 2D and 2.5D image domains have been made previously, however, a truly functional understanding of object affordance requires learning and prediction in the 3D physical domain, which is still absent in the community. In this work, we present a 3D AffordanceNet dataset, a bench-mark of 23k shapes from 23 semantic object categories, annotated with 18 visual affordance categories. Based on this dataset, we provide three benchmarking tasks for evaluating visual affordance understanding, including full-shape, partial-view and rotation-invariant affordance estimations. Three state-of-the-art point cloud deep learning networks are evaluated on all tasks. In addition we also investigate a semi-supervised learning setup to explore the possibility to benefit from unlabeled data. Comprehensive results on our contributed dataset show the promise of visual affordance understanding as a valuable yet challenging benchmark.
A Fast Spatial Pool Learning Algorithm of Hierarchical Temporal Memory Based on Minicolumn's Self-Nomination

As a new type of artificial neural network model, HTM has become the focus of current research and application. The sparse distributed representation is the basis of the HTM model, but the existing spatial pool learning algorithms have high training time overhead and may cause the spatial pool to become unstable. To overcome these disadvantages, we propose a fast spatial pool learning algorithm of HTM based on minicolumn's nomination, where the minicolumns are selected according to the load-carrying capacity and the synapses are adjusted using compressed encoding. We have implemented the prototype of the algorithm and carried out experiments on three datasets. It is verified that the training time overhead of the proposed algorithm is almost unaffected by the encoding length, and the spatial pool becomes stable after fewer iterations of training. Moreover, the training of the new input does not affect the already trained results.
Nanofibrous Gelatin-Based Biomaterial with Improved Biomimicry Using D-Periodic Self-Assembled Atelocollagen

Design of bioinspired materials that mimic the extracellular matrix (ECM) at the nanoscale is a challenge in tissue engineering. While nanofibrillar gelatin materials mimic chemical composition and nano-architecture of natural ECM collagen components, it lacks the characteristic D-staggered array (D-periodicity) of 67 nm, which is an important cue in terms of cell recognition and adhesion properties. In this study, a nanofibrous gelatin matrix with improved biomimicry is achieved using a formulation including a minimal content of D-periodic self-assembled atelocollagen. We suggest a processing route approach consisting of the thermally induced phase separation of the gelatin based biopolymeric mixture precursor followed by chemical-free material cross-linking. The matrix nanostructure is characterized using field emission gun scanning electron microscopy (FEG-SEM), transmission electron microscopy (TEM), wide angle X-ray diffraction (XRD) and Fourier-transform infrared spectroscopy (FT-IR). The cell culture assays indicate that incorporation of 2.6 wt. % content of D-periodic atelocollagen to the gelatin material, produces a significant increase of MC3T3-E1 mouse preosteoblast cells attachment and human mesenchymal stem cells (hMSCs) proliferation, in comparison with related bare gelatin matrices. The presented results demonstrate the achievement of an efficient route to produce a cost-effective, compositionally defined and low immunogenic “collagen-like” instructive biomaterial, based on gelatin.
Increased thalamo-cortical functional connectivity in patients with diabetic painful neuropathy: A resting-state functional MRI study

Functional changes in the brain of patients with painful diabetic neuropathy (PDN) have remained largely elusive. The aim of the present study was to explore changes in thalamo-cortical functional connectivity (FC) of patients with PDN using resting-state functional MRI. A total of 20 patients with type 2 diabetes mellitus (T2DM) with non-painful diabetic neuropathy (Group NDN), 19 patients with T2DM with PDN (Group-PDN) and 13 age-, sex- and education-matched healthy controls were recruited. The differences in thalamo-cortical FC among the three groups were compared. Patients in Group PDN had increased FC in the left thalamus, the right angular gyrus and the occipital gyrus as compared to those in Group NDN. Furthermore, patients in Group PDN had increased FC in the right thalamus and angular gyrus as compared to those in Group NDN. In conclusion, the present results suggested that the thalamo-cortical FC is increased in patients with T2DM and PDN. Furthermore, the increased FC in the thalamic-parietal-occipital connectivity may be a central pathophysiological mechanism for PDN. The study was retrospectively registered at ClinicalTrials.gov on 3 October 2018 (identifier no. NCT03700502).
Clinical Profile of Non-neutropenic Patients with Invasive Candidiasis: A Retrospective Study in a Tertiary Care Center

Abstract Introduction: Invasive candidiasis (IC) is a major cause of morbidity and mortality in critically ill patients in the intensive care unit (ICU). In this study, we aim to analyze the clinical profile, species distribution, and susceptibility pattern of patients with IC. Methods: Case records of non-neutropenic patients ≥18 years of age with IC between January 2016 and June 2019 at a tertiary care referral hospital were analyzed. IC was defined as either candidemia or isolation of Candida species from a sterile site (such as CSF; ascitic, pleural, or pericardial fluid; or pus or tissue from an intraoperative sample) in a patient with clinical signs and symptoms of infection. Results: A total of 114 patients were analyzed, out of which 105 (92.1%) patients had bloodstream infection (BSI) due to Candida and 9 (7.9%) had IC identified from a sterile site. Central line-associated blood stream infection (27 patients, 23.6%) and a gastrointestinal source (30 patients, 26.3%) were the most common presumed sources for candidemia. The commonest species was Candida tropicalis 42 (36.8%), followed by Candida glabrata 20 (17.5%). Serum beta-D-glucan (BDG) was done only in 32 patients of the 114 (35.3%); among those who were tested, 5 (15.6%) had a BDG value of less than 80 pg/mL despite having Candida BSI. Fluconazole sensitivity was 69.5% overall. At 14 days after diagnosis of IC, 49.1% had recovered, with the remainder having an unfavorable outcome (32.4% had died and 18.4% had left against medical advice). Clinical significance: IC is a major concern in Indian ICUs, with a satisfactory outcome in only half of our patients. Serum BDG is a valuable test to diagnose blood culture-negative IC, but more studies are needed to determine its role in the exclusion of IC, as we had a small minority of patients with negative tests despite proven IC. Conclusion: We recommend sending two sets of blood cultures and serum BDG assay for all suspected patients. Initiating empiric antifungal therapy with an echinocandin is advisable, in view of increasing azole resistance and the emergence of Candida auris, with de-escalation to fluconazole for sensitive isolates after clinical stability and blood culture clearance. How to cite this article: Sridharan S, Gopalakrishnan R, Nambi PS, Kumar S, Sethuraman N, Ramasubramanian V. Indian J Crit Care Med 2021;25(3):267-272.
Patterns of Calcium- and Chloride-Ion Disorders and Predictors among Obese Outpatient Adults in Southern Ethiopia

Background Obesity is a condition characterized by abnormal or excess accumulation of fat in body tissue, which may impair health and result in electrolyte derangement. Hypercalcemia and hypochloremia are significant problems in obese patients, and can cause substantial morbidity and mortality. Determination of patterns of calcium and chloride may play a major role in the management of obese patients. Therefore, this study aimed to determine calcium and chloride disorders and their predictors among obese adults in the outpatient department at Wolkite University Specialized Hospital, southern Ethiopia from May to August 2020. Methods This institution-based cross-sectional study was conducted on 250 obese adults attending the outpatient department from May to August 2020. Structured questionnaires through face-to-face interviews and participants’ medical records were used to collect information on determinants related to calcium- and chloride-ion disturbances. Levels of serum calcium and chloride were measured using an ion-selective electrode analyzer. Bivariate and multivariate logistic regression analyses were done to identify determinants associated with calcium and chloride disorders. Using ORs and 95% CIs,P<0.05 was considered significant. Results Among the 250 patients, 18% (95% CI 53.02–111) and 17.6% (95% CI 0.78–12) of study participants were hypochloremic and hypercalcemic, respectively. Age ≥50 years (AOR 3.58, 95% CI 2.57–11.10), diuretic use (AOR 2.35, 95% CI 1.31–4.23), and obesity (AOR, 6.82, 95% CI 2.26–14.62) were independent predictors of hypochloremia on multivariate logistic regression, and obesity (AOR 9.21, 95% CI 2.50–14.81), overweight (AOR 8.32, 95% CI 1.61–10.20) and physical inactivity (AOR 4.28, 95% CI 1.71–10.44) were predictors of hypercalcemia. Conclusion High burdens of hypochloremia (18%) and hypercalcemia (17.6%) were observed in these patients. Increased age, diuretic use, being overweight, and physical inactivity were predictors of electrolyte disorders. The findings of this study should be taken into consideration to implement preventive interventions on these predictors in obese patients. Promoting sufficient fruit and vegetable consumption and physical exercise and determination of serum-chloride and -calcium levels in adult overweight/obese patients are recommended to minimize the emergence of electrolyte disorders.
Promelanogenic Effects by an Annurca Apple-Based Natural Formulation in Human Primary Melanocytes

Introduction Melanocytes are engaged in synthesis, transport, and release of pigments at the epidermal-melanin units in response to the finely regulated melanogenic pathway. A multifaceted combination of both intrinsic and extrinsic factors – from endocrine and paracrine dynamics to exogenous stimuli such as sunlight and xenobiotics – modulates expression and activity of proteins involved in pigmentation, including the rate-limiting enzyme tyrosinase. As well as playing critical physiological functions comprising skin photoprotection, melanins define hair and skin pigmentation which in turn have impacted considerably to human social communication since time immemorial. Additionally, numerous skin diseases based on pigmentation alterations can have serious public influence. While several melanogenesis inhibitors are already available, the number of melanin activators and tyrosinase stimulators as drug-like agents is still limited. Methods To explore the biological effects of an Annurca Apple-based nutraceutical preparation (AMS) on melanin production, experiments in cellular models of human skin were performed. Both primary cultures and co-cultures of epidermal melanocytes (HEMa) and follicular keratinocytes (HHFK) were used. Results We show that AMS, by now branded for its cutaneous beneficial effects, induces in total biocompatibility a significant promelanogenic effect in human primary melanocytes. In line, we found melanin cytosolic accumulation consistent with tyrosinase up-regulation. Conclusion Disposal of skin pigmenting agents would be attractive for the treatment of hypopigmentation disorders, to postpone skin photoaging or simply for fashion, so that discovery and development of melanogenesis stimulators, especially from natural sources, is nowadays a dynamic area of research.