Miracle-dz/UniFAR-FTUs huggingface dataset:
A Low Overhead, Within-a-Cycle Adaptive Clock Stretching Circuit With Wide Operating Range in 40-nm CMOS

Adaptive voltage frequency scaling (AVFS) techniques based on in-situ timing monitoring can mitigate the excessive timing margin caused by process, voltage and temperature (PVT) variations. However, they usually adjust the frequency by clock gating, clock half-division, or phase locked loop configuration, which cause large performance loss or need a long lock time. Thus, a compact structured adaptive clock stretching circuit is proposed here for very rapid frequency adjustment. It is very useful for AVFS systems by stretching the clock cycle with only within-a-cycle response time. The proposed architecture generates multi-phase clocks through a series of delay elements and continuously picks an appropriate phase clock to form a stretched clock. It also uses a process voltage temperature monitor to adjust the phase clock selection and makes this circuit suitable under PVT variations. Fabricated in 40-nm CMOS process, it only occupies a core area of 85* $83~{\mu }\text{m}^{2}$ . Measurements show that it can stretch the clock cycle immediately upon the stretch signal with within-a-cycle response time. It has a wide voltage range from 0.43 V to 1.1 V, with power consumptions of $38.2~{\mu }\text{W}$ at 0.43 V/10 MHz and 4.55 mW at 1.1 V/1.2 GHz. Plus, it can provide configurable stretch amounts for the AVFS system. Thus, it can provide a within-a-cycle, low overhead frequency adjustment solution for AVFS systems.
HOUDINI: Lifelong Learning as Program Synthesis

We present a neurosymbolic framework for the lifelong learning of algorithmic tasks that mix perception and procedural reasoning. Reusing high-level concepts across domains and learning complex procedures are key challenges in lifelong learning. We show that a program synthesis approach that combines gradient descent with combinatorial search over programs can be a more effective response to these challenges than purely neural methods. Our framework, called HOUDINI, represents neural networks as strongly typed, differentiable functional programs that use symbolic higher-order combinators to compose a library of neural functions. Our learning algorithm consists of: (1) a symbolic program synthesizer that performs a type-directed search over parameterized programs, and decides on the library functions to reuse, and the architectures to combine them, while learning a sequence of tasks; and (2) a neural module that trains these programs using stochastic gradient descent. We evaluate HOUDINI on three benchmarks that combine perception with the algorithmic tasks of counting, summing, and shortest-path computation. Our experiments show that HOUDINI transfers high-level concepts more effectively than traditional transfer learning and progressive neural networks, and that the typed representation of networks signiﬁcantly accelerates the search.
Designing Virtual Interactive Environments for Autobiographical Memory Recall

The ability to recall our past is essential to who we are. We use our past memories as a basis to inform what we do, and how we behave, the events we remember tell us about what and who is important to us, and help us to make our decisions about the future. Traditionally we use medias such as diaries, photos, film, and mementos as cues which help us recall memories. However traditional media are limited to the level of detail, and cues they provide to trigger memory recall. As our own recollection of the event fades with time, the cues can become less effective for recall. This project investigates if more immersive forms of media such as virtual environments provide more efficient means to trigger memory recall, compared to more traditional forms of memory preservation.
SCOSY: A Biomedical Collaboration Recommendation System

Finding relevant scientific articles and collaborators is a time-consuming and challenging task in today's information-rich environment. Despite this challenge, the study and development of recommendation systems, based on the authors' collaboration network, productivity and area of research, as topics of interest, have not been practically deployed in healthcare organizations. To address this known practice gap and to promote collaboration, Schosy was developed. This system collects publication metadata from PubMed, as the data source, and combining Collaborative and ContentBased Filtering techniques coupled with the Latent Dirichlet Allocation Topic Modeling algorithm, it recommends collaborators based on the authors' work, collaboration among the authors, Medical Subject Headings (MeSH) terms and the productivity of relevant researchers. As a result, this system provides an interpretable latent structure for collaborators and biomedical databases in order to enhance the experience of finding collaboration, for and by researchers and non-technical users.
Real-Time Emotion Detection via E-See

Real-time emotion detection has being attracted to human attention recently. Recognizing the inner emotion not only assists people to communicate and understand with each other, but also prevents the occurrence of the serious diseases (e.g., autism) and the emergency (i.e., child abuse, sexual invasion). Existing works usually adopt the professional and cumbersome devices to learn the emotions, and therefore limited in the daily usage. In this work, we design a pervasive and wearable device E-See that enables to recognize the emotion in real time. The prototype of the device is deployed in a microcomputer currently, and it can be resized as a small button worn on the collar or extend as a platform to detect the real-time emotion.
Accuracy of Adult Children's Perceptions of Mothers' Caregiver Preferences.

BACKGROUND AND OBJECTIVES Most older mothers have strong preferences regarding which offspring will serve as their future caregivers, and violation of these preferences has been found to have consequences for mothers' psychological well-being. However, no study has examined the accuracy of adult children's perceptions of their mothers' caregiver preferences. In this article, we compare mothers' stated preferences for particular caregivers with their adult children's perceptions of their mothers' preferences. RESEARCH DESIGN AND METHODS Data were collected from 675 adult children and their mothers nested within 285 families as part of the Within-Family Differences Study. RESULTS Only 44.6% of adult children accurately reported their mothers' preferences for particular offspring as caregivers. Consistent with our hypotheses, accuracy was higher when mothers and children shared values regarding filial piety, and lower when children were parents, had poor health, and lived further away. Surprisingly, primary caregivers were substantially less likely to accurately report mothers' caregiver preferences than were noncaregivers. This counterintuitive pattern can be explained by the finding that most mothers were cared for by children whom they did not prefer and may have therefore been reluctant to share their preferences with those caregivers. DISCUSSION AND IMPLICATIONS Given the negative psychological consequences for mothers whose caregiver preferences are violated, the high level of inaccuracy found among adult children has important implications when mothers face serious health events. These findings underscore the need for intervention efforts to encourage practitioners and clinicians to collect information directly from mothers regarding preferences for particular offspring as caregivers.
Formate cross‐feeding and cooperative metabolic interactions revealed by transcriptomics in co‐cultures of acetogenic and amylolytic human colonic bacteria

Summary Interspecies cross‐feeding is a fundamental factor in anaerobic microbial communities. In the human colon, formate is produced by many bacterial species but is normally detected only at low concentrations. Ruminococcus bromii produces formate, ethanol and acetate in approximately equal molar proportions in pure culture on RUM‐RS medium with 0.2% Novelose resistant starch (RS3) as energy source. Batch co‐culturing on starch with the acetogen Blautia hydrogenotrophica however led to the disappearance of formate and increased levels of acetate, which is proposed to occur through the routing of formate via the Wood Ljungdahl pathway of B. hydrogenotrophica. We investigated these inter‐species interactions further using RNAseq to examine gene expression in continuous co‐cultures of R. bromii and B. hydrogenotrophica. Transcriptome analysis revealed upregulation of B. hydrogenotrophica genes involved in the Wood‐Ljungdahl pathway and of a 10 gene cluster responsible for increased branched chain amino acid fermentation in the co‐cultures. Cross‐feeding between formate‐producing species and acetogens may be a significant factor in short chain fatty acid formation in the colon contributing to high rates of acetate production. Transcriptome analysis also indicated competition for the vitamin thiamine and downregulation of dissimilatory sulfate reduction and key redox proteins in R. bromii in the co‐cultures, thus demonstrating the wide‐ranging consequences of inter‐species interactions in this model system.
Quantitative Assessment of Tetrel Bonding Utilizing Vibrational Spectroscopy

A set of 35 representative neutral and charged tetrel complexes was investigated with the objective of finding the factors that influence the strength of tetrel bonding involving single bonded C, Si, and Ge donors and double bonded C or Si donors. For the first time, we introduced an intrinsic bond strength measure for tetrel bonding, derived from calculated vibrational spectroscopy data obtained at the CCSD(T)/aug-cc-pVTZ level of theory and used this measure to rationalize and order the tetrel bonds. Our study revealed that the strength of tetrel bonds is affected by several factors, such as the magnitude of the σ-hole in the tetrel atom, the negative electrostatic potential at the lone pair of the tetrel-acceptor, the positive charge at the peripheral hydrogen of the tetrel-donor, the exchange-repulsion between the lone pair orbitals of the peripheral atoms of the tetrel-donor and the heteroatom of the tetrel-acceptor, and the stabilization brought about by electron delocalization. Thus, focusing on just one or two of these factors, in particular, the σ-hole description can only lead to an incomplete picture. Tetrel bonding covers a range of −1.4 to −26 kcal/mol, which can be strengthened by substituting the peripheral ligands with electron-withdrawing substituents and by positively charged tetrel-donors or negatively charged tetrel-acceptors.
Multi-scale Trajectory Data Management and Query

In recent years, with the development of Internet big data, the popularity of mobile terminals, and the extensive services of the LBS platform, mass-level trajectory data has been formed. Due to the large amount of data, various types, different sampling frequency, and storage of point sequences of spatio-temporal data, researchers often need to perform a series of processing to convert the original data into available data for trajectory mining and analysis. In this paper, we study the management of multi-scale trajectories and provide an ordered KNN query for it. In our paper, we first propose a data model based on trajectory segments(DMTS) for multi-scale trajectories. The model converts the trajectory from the original sampling point to a form of trajectory segment that is easy to understand and organize. Next, we propose a fast ordered KNN query based on DMTS, and verify the correctness and efficiency of the algorithm by several experiments. The innovations in this paper mainly include: (1) Propose DMTS-based organization and management for trajectory data, which will effectively improve the efficiency of trajectory mining and analysis. By dividing the trajectory into moving objects, point objects, and trajectory objects, the DMTS will be applied to multi-scale heterogeneous trajectory data. (2) A fast ordered KNN query based on DMTS is proposed. Firstly, we use shape-based compression to reduce the amount of data and complexity of calculation. Secondly, we approve the measurement of trajectory distance from point-to-point distance to point-to-segment projection distance, which improves the accuracy and efficiency of querying heterogeneous trajectory data.
Domain-Specific Optimization and Generation of High-Performance GPU Code for Stencil Computations

Stencil computations arise in a number of computational domains. They exhibit significant data parallelism and are thus well suited for execution on graphical processing units (GPUs), but can be memory-bandwidth limited unless temporal locality is utilized via tiling. This paper describes how effective tiled code can be generated for GPUs from a domain-specific language (DSL) for stencils. Experimental results demonstrate the benefits of such a domain-specific optimization approach over state-of-the-art general-purpose compiler optimizations.
High-energy focused extracorporeal shockwave therapy relieved pain in Dupuytren's disease: a series of seven hands.

BACKGROUND Dupuytren's disease of the hand is characterized by progressive thickening and shortening of palmar fascia resulting in flexion deformities of the digits and hence limitation of hand function. The objective of this study was to evaluate the effectiveness of shock-wave therapy on patients with Dupuytren's disease of the hand. CASE SERIES A case series study observed a total of four patients with Dupuytren's disease of the hand. All four patients had diabetes. Three patients had bilateral hand involvement. Two patients had flexion deformity. High-energy focused extracorporeal shockwave therapy was applied using a Storz Duolith SD1 (2000 impulses, 3 Hz, 1.24 mJ/mm2) in five sessions with 7 days between. Outcome measured included pain score, tender score, hand grip, and nodule size from the Michigan Hand Outcome Questionnaire, JAMAR hand held dynamometer, and ultrasonography. Data was collected at the entry and conclusion of the study at 0 and 8 weeks. CLINICAL REHABILITATION IMPACT Significant statistical improvement was observed for pain score 8.7±0.5 vs. 2.0±0.9, p<0.001 and for tender score 8.5±0.5 vs. 2.5±0.9, p<0.009 after the therapy. Decrease in size of nodules with improvement of flexion deformity was observed in one patient who had multiple bilateral nodules. No adverse effects were noted. Extracorporeal shockwave therapy was beneficial in relieving symptoms in Dupuytren's disease of the hand.
Activation Server for Bidirectional Limited Usages of Digital Objects

Limiting data usage is an important issue for data sharing. Big data, in particular, has numerous potential values; thus, an arbitrary value can be extracted as an output by processing data with an arbitrary program. If a data provider simply shares his data with users, the users may get information that the data provider does not want to disclose. However, currently, there is no mechanism to limit the usage of such data. In our approach, a data provider and a program provider specify a combination of data and program files bidirectionally to limit their usages. The Activation Server binds the specified data and program, and then the Activation Server processes the data with the program and sends the output to the user specified by the data provider. We assume that a third-party organization has certified the security for the Activation Server. We implemented and evaluated the Activation Server to demonstrate that it enables data sharing by limiting the usage of digital objects.
A Simple Controller for the Transition Maneuver of a Tail-Sitter Drone

This paper presents a controller for the transition maneuver of a tail-sitter drone. The tail-sitter model considers aerodynamic terms whereas the proposed controller considers the time-scale separation between drone attitude and position dynamics. The controller design is based on Lyapunov approach and linear saturation functions. Simulations experiments demonstrate the effectiveness of the derived theoretical results.
An over 120 dB wide-dynamic-range 3.0 μm pixel image sensor with in-pixel capacitor of 41.7 fF/um2 and high reliability enabled by BEOL 3D capacitor process

We realized a simultaneous-capture wide-dynamic-range image sensor with 3.0 μm pixels using novel in-pixel 3D capacitors located in BEOL. We achieved high capacitance density of 41.7 fF/μm<sup>2</sup> and low leakage current density of 3.6×10<sup>−10</sup> A/cm<sup>2</sup> at 1 V by applying 3D structure and optimizing dielectric deposition process. TDDB investigations showed that estimated failure time at 125°C is more than 10 years. We demonstrated over 120 dB dynamic range image sensor with 3.0 μm pixels, which is enabled by this BEOL 3D capacitor process technology. (Keywords: image sensor, capacitor, 3D, BEOL, high reliability and wide dynamic range)
Seizure Prediction In EEG Records Based On Spatial-Frequency Features And Preictal Period Selection

Algorithms can automatically predict seizures to reduce the occurrences of accidental injury and improve living conditions of patients. This paper proposes a novel patient-specific algorithm based on multi-channel scalp EEG recordings. 26 features for each channel are extracted from each one-second data, including 8 absolute spectral powers, 8 normalized spectral powers, 8 power spectral entropies, the shortest path length and clustering coefficient. Then, a new step to select the most discriminative five minute preictal period is proposed. The features of preictal period are combined with that of five minute non-seizure period to form a training set in order to achieve the maximum linear separability criteria. Then, the effective features of each channel are selected by Elastic Net. At the same time, greedy algorithm is used to select effective channels. The ten minute effective features obtained from effective channels are input to Logistic Regression. The algorithm is tested on 62 seizures from 12 patients in 217 hours of recordings in MIT database. Results are finally given by average of each 1 minute values of Logistic Regression. It is shown that the proposed algorithm can achieve a sensitivity of 91% and an averaged false positive rate of 0.3 per hour.
Exploring Hierarchical Convolutional Features for Hyperspectral Image Classification

Hyperspectral image (HSI) classification is an active and important research task driven by many practical applications. To leverage deep learning models especially convolutional neural networks (CNNs) for HSI classification, this paper proposes a simple yet effective method to extract hierarchical deep spatial feature for HSI classification by exploring the power of off-the-shelf CNN models, without any additional retraining or fine-tuning on the target data set. To obtain better classification accuracy, we further propose a unified metric learning-based framework to alternately learn discriminative spectral–spatial features, which have better representation capability and train support vector machine (SVM) classifiers. To this end, we design a new objective function that explicitly embeds a metric learning regularization term into SVM training. The metric learning regularization term is used to learn a powerful spectral–spatial feature representation by fusing spectral feature and deep spatial feature, which has small intraclass scatter but big between class separation. By transforming HSI data into new spectral–spatial feature space through CNN and metric learning, we can pull the pixels from the same class closer, while pushing the different class pixels farther away. In the experiments, we comprehensively evaluate the proposed method on three commonly used HSI benchmark data sets. State-of-the-art results are achieved when compared with the existing HSI classification methods.
Integrated Virtual Laboratory in Engineering Mathematics Education: Fourier Theory

In this paper, we present a virtual learning laboratory environment for undergraduate mathematics education using an inquiry-based learning approach. The Visible Thinking pedagogical framework is also suggested to achieve a good complement to traditional lecture–tutorial systems. The virtual laboratory is implemented in an open-access Java interactive software. We demonstrate a viable instruction procedure, providing a set of virtual laboratory activities with real-world applications spanning signal processing, data science and analytics, sustainable infrastructure engineering, and theoretical physics. A preliminary study on a pilot cohort indicates that the proposed virtual laboratory can enhance students’ learning. The virtual laboratory implementation is scalable and can be easily expanded in scope to other mathematical topics; transitioning to a tablet-based system for use in smart classrooms is also readily achieved. The Java interactive software is freely available on Open Science Framework.
Choline Kinase Alpha Inhibition by EB-3D Triggers Cellular Senescence, Reduces Tumor Growth and Metastatic Dissemination in Breast Cancer

Choline kinase (ChoK) is the first enzyme of the Kennedy pathway leading to the biosynthesis of phosphatidylcholine (PtdCho), the most abundant phospholipid in eukaryotic cell membranes. EB-3D is a novel choline kinase α1 (ChoKα1) inhibitor with potent antiproliferative activity against a panel of several cancer cell lines. ChoKα1 is particularly overexpressed and hyperactivated in aggressive breast cancer. By NMR analysis, we demonstrated that EB-3D is able to reduce the synthesis of phosphocholine, and using flow cytometry, immunoblotting, and q-RT-PCR as well as proliferation and invasion assays, we proved that EB-3D strongly impairs breast cancer cell proliferation, migration, and invasion. EB-3D induces senescence in breast cancer cell lines through the activation of the metabolic sensor AMPK and the subsequent dephosphorylation of mTORC1 downstream targets, such as p70S6K, S6 ribosomal protein, and 4E-BP1. Moreover, EB-3D strongly synergizes with drugs commonly used for breast cancer treatment. The antitumorigenic potential of EB-3D was evaluated in vivo in the syngeneic orthotopic E0771 mouse model of breast cancer, where it induces a significant reduction of the tumor mass at low doses. In addition, EB-3D showed an antimetastatic effect in experimental and spontaneous metastasis models. Altogether, our results indicate that EB-3D could be a promising new anticancer agent to improve aggressive breast cancer treatment protocols.