Miracle-dz/UniFAR-FTUs huggingface dataset:
Explicitizing an Implicit Bias of the Frequency Principle in Two-layer Neural Networks

It remains a puzzle that why deep neural networks (DNNs), with more parameters than samples, often generalize well. An attempt of understanding this puzzle is to discover implicit biases underlying the training process of DNNs, such as the Frequency Principle (F-Principle), i.e., DNNs often fit target functions from low to high frequencies. Inspired by the F-Principle, we propose an effective model of linear F-Principle (LFP) dynamics which accurately predicts the learning results of two-layer ReLU neural networks (NNs) of large widths. This LFP dynamics is rationalized by a linearized mean field residual dynamics of NNs. Importantly, the long-time limit solution of this LFP dynamics is equivalent to the solution of a constrained optimization problem explicitly minimizing an FP-norm, in which higher frequencies of feasible solutions are more heavily penalized. Using this optimization formulation, an a priori estimate of the generalization error bound is provided, revealing that a higher FP-norm of the target function increases the generalization error. Overall, by explicitizing the implicit bias of the F-Principle as an explicit penalty for two-layer NNs, our work makes a step towards a quantitative understanding of the learning and generalization of general DNNs.
Data mining and knowledge discovery from physiological sensors

We present in this paper our method for discovering logic rules from physiological data by applying a fusion of preprocessing and data mining using decision trees. We have focused on four sensors representative of wearable technology capabilities namely; plethysmography, galvanic skin response, respiration, and body temperature, sourced from a highly cited dataset in Affective Computing. The method involved generating a number of datasets from the physiological data and subsequently performing classification using the C4.5 decision tree algorithm with a focus on knowledge discovery. The findings of this research demonstrate that preprocessing data into three classes with extreme boundaries and a neutral class, as well as classifying the two classes without the neutral class can produce high-quality rules. The discovered knowledge in the form of the top 4 rules was mapped on the valence and arousal emotional model. Finally, these rules are interpreted with the aid of box and whisker plots in the context of the underlying physiological processes.
Privacy-aware environmental sound classification for indoor human activity recognition

This paper presents a comparative study on different feature extraction and machine learning techniques for indoor environmental sound classification. Compared to outdoor environmental sound classification systems, indoor systems need to pay special attention to power consumption and privacy. We consider feature calculation complexity, classification accuracy and privacy as evaluation metrics. To ensure privacy, we strip voice bands from sound input to make human conversations unrecognizable. With 5 classes of 2500 indoor audio events as input, our experimental results show that using SVM model with LPCC feature, 78% classification accuracy can be reached. Furthermore, the performance is improved to more than 85% by combining several simple features and dropping unreliable predictions, which only slightly increase the complexity.
Performance-Feedback Autoscaling with Budget Constraints for Cloud-based Workloads of Workflows

The growing popularity of workflows in the cloud domain promoted the development of sophisticated autoscaling policies that allow automatic allocation and deallocation of resources. However, many state-of-the-art autoscaling policies for workflows are mostly plan-based or designed for batches (ensembles) of workflows. This reduces their flexibility when dealing with workloads of workflows, as the workloads are often subject to unpredictable resource demand fluctuations. Moreover, autoscaling in clouds almost always imposes budget constraints that should be satisfied. The budget-aware autoscalers for workflows usually require task runtime estimates to be provided beforehand, which is not always possible when dealing with workloads due to their dynamic nature. To address these issues, we propose a novel Performance-Feedback Autoscaler (PFA) that is budget-aware and does not require task runtime estimates for its operation. Instead, it uses the performance-feedback loop that monitors the average throughput on each resource type. We implement PFA in the popular Apache Airflow workflow management system, and compare the performance of our autoscaler with other two state-of-the-art autoscalers, and with the optimal solution obtained with the Mixed Integer Programming approach. Our results show that PFA outperforms other considered online autoscalers, as it effectively minimizes the average job slowdown by up to 47% while still satisfying the budget constraints. Moreover, PFA shows by up to 76% lower average runtime than the competitors.
A pipeline for automated face dataset creation from unlabeled images

Computer vision tasks are very dataset dependent and with the emerging growth of image processing tasks and their applications, there is a huge demand for more datasets in terms of variety or size. Considering many face datasets are assembled largely manually, the process of manual dataset construction could be very time-consuming and labor intensive. In addition, some face datasets are constructed automatically over the past few years, but they are all constructed with previous knowledge of labels to some extent. In this work, we focus on automated face dataset generation from unlabeled images. We present a novel and effective pipeline for generating face datasets automatically from unlabeled and raw data for face-related tasks. We evaluated our pipeline on several datasets and it achieved a significant improvement compared to clustering-only approaches, which shows its potential towards practical solutions for automated face dataset generation.
Enhanced Processing of Painful Emotions in Patients With Borderline Personality Disorder: A Functional Magnetic Resonance Imaging Study

Previous research has demonstrated that patients with borderline personality disorder (BPD) are more sensitive to negative emotions and often show poor cognitive empathy, yet preserved or even superior emotional empathy. However, little is known about the neural correlates of empathy. Here, we examined empathy for pain in 20 patients with BPD and 19 healthy controls (HC) in a functional magnetic resonance imaging (fMRI) study, which comprised an empathy for pain paradigm showing facial emotions prior to hands exposed to painful stimuli. We found a selectively enhanced activation of the right supramarginal gyrus for painful hand pictures following painful facial expressions in BPD patients, and lower activation to nonpainful pictures following angry expressions. Patients with BPD showed less activation in the left supramarginal gyrus when viewing angry facial expressions compared to HC, independent of the pain condition. Moreover, we found differential activation of the left anterior insula, depending on the preceding facial expression exclusively in patients. The findings suggest that empathy for pain becomes selectively enhanced, depending on the emotional context information in patients with BPD. Another preliminary finding was an attenuated response to emotions in patients receiving psychotropic medication compared to unmedicated patients. These effects need to be replicated in larger samples. Together, increased activation during the observation of painful facial expressions seems to reflect emotional hypersensitivity in BPD.
Likelihood-Free Inference and Generation of Molecular Graphs

Recent methods for generating novel molecules use graph representations of molecules and employ various forms of graph convolutional neural networks for inference. However, training requires solving an expensive graph isomorphism problem, which previous approaches do not address or solve only approximately. In this work, we propose LF-MolGAN, a likelihood-free approach for de novo molecule generation that avoids explicitly computing a reconstruction loss. Our approach extends generative adversarial networks by including an adversarial cycle-consistency loss to implicitly enforce the reconstruction property. To capture properties unique to molecules, such as valence, we extend Graph Isomorphism Network to multi-graphs. To quantify the performance of models, we propose to compute the distance between distributions of physicochemical properties with the 1-Wasserstein distance. We demonstrate that LF-MolGAN more accurately learns the distribution over the space of molecules than all baselines. Moreover, it can be utilized for drug discovery by efficiently searching the space of molecules using molecules' continuous latent representation.
Cysteine-scanning Mutagenesis of Transmembrane Segment 7 of the GLUT1 Glucose Transporter*

The human erythrocyte facilitative glucose transporter (Glut1) is predicted to contain 12 transmembrane spanning α-helices based upon hydropathy plot analysis of the primary sequence. Five of these helices (3, 5, 7, 8, and 11) are capable of forming amphipathic structures. A model of GLUT1 tertiary structure has therefore been proposed in which the hydrophilic faces of several amphipathic helices are arranged to form a central aqueous channel through which glucose traverses the hydrophobic lipid bilayer. In order to test this model, we individually mutated each of the amino acid residues in transmembrane segment 7 to cysteine in an engineered GLUT1 molecule devoid of all native cysteines (C-less). Measurement of 2-deoxyglucose uptake in a Xenopus oocyte expression system revealed that nearly all of these mutants retain measurable transport activity. Over one-half of the cysteine mutants had significantly reduced specific activity relative to the C-less protein. The solvent accessibility and relative orientation of the residues within the helix was investigated by determining the sensitivity of the mutant transporters to inhibition by the sulfhydryl directed reagentp-chloromercuribenzene sulfonate (pCMBS). Cysteine replacement at six positions (Gln282, Gln283, Ile287, Ala289, Val290, and Phe291), all near the exofacial side of the cell membrane, produced transporters that were inhibited by incubation with extracellular pCMBS. Residues predicted to be near the cytoplasmic side of the cell membrane were minimally affected by pCMBS. These data demonstrate that the exofacial portion of transmembrane segment 7 is accessible to the external solvent and provide evidence for the positioning of this α-helix within the glucose permeation pathway.
Is the Clinical Version of the Iowa Gambling Task Relevant for Assessing Choice Behavior in Cases of Internet Addiction?

Objective: A critical issue in research related to the Iowa gambling task (IGT) is the use of the alternative factors expected value and gain–loss frequency to distinguish between clinical cases and control groups. When the IGT has been used to examine cases of Internet addiction (IA), the literature reveals inconsistencies in the results. However, few studies have utilized the clinical version of IGT (cIGT) to examine IA cases. The present study aims to resolve previous inconsistencies and to examine the validity of the cIGT by comparing performances of controls with cases of Internet gaming disorder (IGD), a subtype of IA defined by the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders. Methods: The study recruited 23 participants with clinically diagnosed IGD and 38 age-matched control participants. Based on the basic assumptions of IGT and the gain–loss frequency viewpoint, a dependent variables analysis was carried out. Results: The results showed no statistical difference between the two groups in most performance indices and therefore support the findings of most IGT-IA studies; in particular, expected value and gain–loss frequency did not distinguish between the IGD cases and controls. However, the participants in both groups were influenced by the gain–loss frequency, revealing the existence of the prominent deck B phenomenon. Conclusion: The findings provide two possible interpretations. The first is that choice behavior deficits do not constitute a characteristic feature of individuals who have been diagnosed with IGD/IA. The second is that, as the cIGT was unable to distinguish the choice behavior of the IGD/IA participants from that of controls, the cIGT may not be relevant for assessing IGD based on the indices provided by the expected value and gain–loss frequency perspectives in the standard administration of IGT.
E-textile capacitive electrodes: Fabric or thread: designing an E-textile cushion for sitting posture detection

Back pain is one of the most common illnesses in Western civilizations. Office work and lack of motion can lead to deterioration over time. Many people already use seat cushions to improve their posture during work or leisure. In this work, we present an E-Textile cushion. This seat cushion is equipped with capacitive proximity sensors that track the proximity and motion of the sitting user and distinguish up to 7 postures. Giving a user immediate feedback on the posture can facilitate more healthy behavior. We evaluated a number of different electrode setups, materials, and classification methods, leading to a maximum accuracy of 97.1%.
Object Files Can Be Purely Episodic

Our ability to track an object as the same persisting entity over time and motion may primarily rely on spatiotemporal representations which encode some, but not all, of an object's features. Previous researchers using the ‘object reviewing’ paradigm have demonstrated that such representations can store featural information of well-learned stimuli such as letters and words at a highly abstract level. However, it is unknown whether these representations can also store purely episodic information (ie information obtained from a single, novel encounter) that does not correspond to pre-existing type-representations in long-term memory. Here, in an object-reviewing experiment with novel face images as stimuli, observers still produced reliable object-specific preview benefits in dynamic displays: a preview of a novel face on a specific object speeded the recognition of that particular face at a later point when it appeared again on the same object compared to when it reappeared on a different object (beyond display-wide priming), even when all objects moved to new positions in the intervening delay. This case study demonstrates that the mid-level visual representations which keep track of persisting identity over time—eg ‘object files’, in one popular framework—can store not only abstract types from long-term memory, but also specific tokens from online visual experience.
Affective effects of gamification: using biosignals to measure the effects on working and learning users

What emotional effects does gamification have on users who work or learn with repetitive tasks? In this work, we use biosignals to analyze these affective effects of gamification. After a brief discussion of related work, we describe the implementation of an assistive system augmenting work by projecting elements for guidance and gamification. We also show how this system can be extended to analyse users' emotions. In a user study, we analyse both biosignals (facial expressions and electrodermal activity), and regular performance measures (error rate and task completion time). For the performance measures, the results confirm known effects like increased speed and slightly increased error rate. In addition, the analysis of the biosignals provides strong evidence for two major affective effects: the gamification of work and learning tasks incites highly significantly more positive emotions and increases emotionality altogether. The results add to the design of assistive systems, which are aware of the physical as well as the affective context.
A survey on AI nutrition recommender systems

The goal of this work is to provide an overview of existing approaches regarding AI nutrition recommender systems. A breakdown of such systems into task-specific components is presented, as well as methodologies concerned with each individual component. The components of an idealized AI nutrition recommender system are presented and compared to state-of-the-art approaches in the corresponding area of research. Finally identified issues in some of these areas are also discussed.
A 17 /spl times/ 69 bit multiply and add unit with redundant binary feedback and single cycle latency

The authors describe a numeric processor with a kernel that is a tree of redundant binary adders and effects either a 17 /spl times/ 69-b multiply-and-add or a 19 /spl times/ 69-b multiply with exact redundant binary output and single cycle latency. Feedback paths selectively allow a high-order or low-order part of the adder tree output to be fed back in redundant binary form to the multiplicand and/or addend inputs to the adder tree. The authors describe algorithms iteratively using this adder tree kernel for IEEE double extended multiplication, division, and square root; conversions between 18-digit BCD integers and 64-b binary integers; and transcendental function evaluation. The multiplier design described was implemented in the Cyrix 83D87 numeric coprocessor (typically 33 MHz). Results for this coprocessor as compared with competitive x87 units are included. <<ETX>>
Shortest Paths on a Polyhedron , Part I : Computing Shortest Paths

We present an algorithm for determining the shortest path between any two points along the surface of a polyhedron which need not be convex. This algorithm also computes for any source point on the surface of a polyhedron the inward layout and the subdivision of the polyhedron which can be used for processing queries of shortest paths between the source point and any destination point. Our algorithm uses a new approach which deviates from the conventional “continuous Dijkstra” technique. Our algorithm has time complexity O(n) and space complexity Θ(n).
Performance of Halbach magnetised brushless ac motor

In this paper, an anisotropic bonded NdFeB Halbach magnetised ring magnet, which is oriented in powder alignment system during injection moulding and subsequently impulse magnetised, is employed. The features of such a Halbach magnetised machine, in terms of the airgap field distribution, and the back-emf and cogging torque waveforms, etc. have been compared with those which result with a Halbach magnet having discrete magnet segments have previously been compared.
Detection of penicillin and extended-spectrum cephalosporin resistance among Streptococcus pneumoniae clinical isolates by use of the E test

Increasing penicillin resistance and the initial recognition of resistance to extended-spectrum cephalosporins among Streptococcus pneumoniae isolates have placed greater emphasis on accurate methods for susceptibility testing of clinical isolates. This study has evaluated the use of the E test (AB Biodisk NA, Piscataway, N.J.) for the detection of penicillin and cefotaxime resistance among 147 pneumococcal clinical isolates in three geographically separate laboratories. These included 42 penicillin-resistant (MIC, > or = 2 micrograms/ml) and 14 cefotaxime-resistant (defined here as an MIC of > or = 2 micrograms/ml) isolates. E test strips were applied to the surface of Mueller-Hinton sheep blood agar plates and incubated at 35 degrees C in 5% CO2 for 20 to 24 h. E test MICs were compared with MICs determined with lysed horse blood-supplemented Mueller-Hinton broth in a microdilution format as recommended by the National Committee for Clinical Laboratory Standards. Penicillin MICs agreed within one log2 dilution for 136 of 147 (92.5%) isolates, and cefotaxime MICs agreed within one log2 dilution for 142 of 147 (96.6%) isolates. No very major or major interpretive errors occurred with either penicillin or cefotaxime E test MIC results. There were 9.5 and 5.4% minor interpretive category errors with penicillin and cefotaxime E test MICs, respectively. These data indicate that the E test represents a convenient and reliable method for the detection of penicillin or cephalosporin resistance in pneumococci.
Automatic network recognition by feature extraction: A case study in the ISM band

Automatic network recognition offers a promising framework for the integration of the cognitive concept at the network layer. This work addresses the problem of automatic classification of technologies operating in the ISM band, with particular focus on Wi-Fi vs. Bluetooth recognition. The proposed classifier is based on feature extraction related to time-varying patterns of packet sequences, i.e. MAC layer procedures, and adopts different linear classification algorithms. Results of classification confirmed the ability to reveal both technologies based on Mac layer feature identification.
Gain-Scheduled Control of a Quadcopter UAV

In this thesis we develop a gain-scheduled control law for the quadcopter unmanned aerial vehicle (UAV). Techniques from linear control theory are introduced and used to construct adaptive proportional and proportional-integral control laws for use with both state and observer-based output feedback. The controller monitors the yaw angle of the quadcopter and updates a gain matrix as the system evolves through operating points. To demonstrate the effectiveness of the gain-scheduled controller, trajectories involving significant variation in the yaw angle are tracked by the quadcopter, including a helix and Lissajous curve. We consider physical implementation of the controller, and offer suggestions for improvement and future work.
Intake and performance of feedlot cattle fed diets based on high and low Brix sugar cane with or without calcium oxide and corn silage

The objective of this study was to evaluate low and high Brix diets, treated or not with 5 g of calcium oxide per kg of natural matter, and corn silage on intake, digestibility and performance of beef cattle. Forty cattle with initial body weight (BW) of 350 kg were used: five composed the control group, 30 were distributed into random blocks (control) and the other five were distributed in a 5 × 5 incomplete Latin square, with the objective of determining digestibility. The 30 animals evaluated for performance were slaughtered and empty body weight (EPW), carcass dressing and meat cuts were determined. The diet with corn silage (CS) presented the best intake of the other ingredients and the best weight gain, except for neutral detergent fiber intake in g/kg of BW. Only carcass dressing, in relation to BW and EBW, was not affected by the treatments, and the others were greater for animals fed diets with sugar cane silage. Animals fed diets with high brix sugar cane silage and treated high brix sugar cane silage presented lower intake of indigestible neutral detergent fiber and neutral detergent fiber corrected for ash and protein (g/kg of BW) in relation to diets with low and high brix sugar cane silage, respectively. Animals fed diets with corn silage presented higher digestibility, except for crude protein and non-fibrous carbohydrates. Animals subjected to diets with corn silage presented low excretion of nitrogen compounds and higher microbial crude protein synthesis. Animals fed sugar cane silage present greater intake, performance and digestibility. The use of lime during 15 or 20o Brix sugar cane ensilage does not alter intake, digestibility or performance of beef cattle.