Miracle-dz/UniFAR-FTUs huggingface dataset:
A Bi-Directional Co-Design Approach to Enable Deep Learning on IoT Devices

Developing deep learning models for resource-constrained Internet-of-Things (IoT) devices is challenging, as it is difficult to achieve both good quality of results (QoR), such as DNN model inference accuracy, and quality of service (QoS), such as inference latency, throughput, and power consumption. Existing approaches typically separate the DNN model development step from its deployment on IoT devices, resulting in suboptimal solutions. In this paper, we first introduce a few interesting but counterintuitive observations about such a separate design approach, and empirically show why it may lead to suboptimal designs. Motivated by these observations, we then propose a novel and practical bi-directional co-design approach: a bottom-up DNN model design strategy together with a top-down flow for DNN accelerator design. It enables a joint optimization of both DNN models and their deployment configurations on IoT devices as represented as FPGAs. We demonstrate the effectiveness of the proposed co-design approach on a real-life object detection application using Pynq-Z1 embedded FPGA. Our method obtains the state-of-the-art results on both QoR with high accuracy (IoU) and QoS with high throughput (FPS) and high energy efficiency.
Instructions' Latencies Characterization for NVIDIA GPGPUs

The last decade has seen a shift in the computer systems industry where heterogeneous computing has become prevalent. Nowadays, Graphics Processing Units (GPUs) are in a variety of systems from supercomputers to mobile phones and tablets. They are not only used for graphics operations but rather as general-purpose special hardware (GPGPUs) to boost the performance of compute-intensive applications. However, the percentage of undisclosed characteristics beyond what vendors provide is small. In this paper, we propose a very low overhead and portable analysis for exposing the hidden latency of each individual instruction executing in the pipeline and different access latencies of the various memory hierarchies at the microarchitecture level. We also show the impact of the possible optimizations a CUDA compiler have over the various latencies. We run our evaluation on seven different high-end NVIDIA GPUs from five different generations/architectures namely: Kepler, Maxwell, Pascal, Volta, and Turing. We believe that this work would help architects have an accurate characterization of the latencies of these GPUs, which would subsequently help in modeling the hardware accurately. In addition, this would also make application developers more aware of how to optimize their applications.
Chlorhexidine bathing and health care-associated infections: a randomized clinical trial.

IMPORTANCE Daily bathing of critically ill patients with the broad-spectrum, topical antimicrobial agent chlorhexidine is widely performed and may reduce health care-associated infections. OBJECTIVE To determine if daily bathing of critically ill patients with chlorhexidine decreases the incidence of health care-associated infections. DESIGN, SETTING, AND PARTICIPANTS A pragmatic cluster randomized, crossover study of 9340 patients admitted to 5 adult intensive care units of a tertiary medical center in Nashville, Tennessee, from July 2012 through July 2013. INTERVENTIONS Units performed once-daily bathing of all patients with disposable cloths impregnated with 2% chlorhexidine or nonantimicrobial cloths as a control. Bathing treatments were performed for a 10-week period followed by a 2-week washout period during which patients were bathed with nonantimicrobial disposable cloths, before crossover to the alternate bathing treatment for 10 weeks. Each unit crossed over between bathing assignments 3 times during the study. MAIN OUTCOMES AND MEASURES The primary prespecified outcome was a composite of central line-associated bloodstream infections (CLABSIs), catheter-associated urinary tract infections (CAUTIs), ventilator-associated pneumonia (VAP), and Clostridium difficile infections. Secondary outcomes included rates of clinical cultures that tested positive for multidrug-resistant organisms, blood culture contamination, health care-associated bloodstream infections, and rates of the primary outcome by ICU. RESULTS During the chlorhexidine bathing period, 55 infections occurred: 4 CLABSI, 21 CAUTI, 17 VAP, and 13 C difficile. During the control bathing period, 60 infections occurred: 4 CLABSI, 32 CAUTI, 8 VAP, and 16 C difficile. The primary outcome rate was 2.86 per 1000 patient-days during the chlorhexidine and 2.90 per 1000 patient-days during the control bathing periods (rate difference, -0.04; 95% CI, -1.10 to 1.01; P = .95). After adjusting for baseline variables, no difference between groups in the rate of the primary outcome was detected. Chlorhexidine bathing did not change rates of infection-related secondary outcomes including hospital-acquired bloodstream infections, blood culture contamination, or clinical cultures yielding multidrug-resistant organisms. In a prespecified subgroup analysis, no difference in the primary outcome was detected in any individual intensive care unit. CONCLUSION AND RELEVANCE In this pragmatic trial, daily bathing with chlorhexidine did not reduce the incidence of health care-associated infections including CLABSIs, CAUTIs, VAP, or C difficile. These findings do not support daily bathing of critically ill patients with chlorhexidine. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT02033187.
Observations of hydrogen and oxygen isotopes in leaf water confirm the craig-gordon model under wide-ranging environmental conditions

The Craig-Gordon evaporative enrichment model of the hydrogen (deltaD) and oxygen (delta(18)O) isotopes of water was tested in a controlled-environment gas exchange cuvette over a wide range (400 per thousand deltaD and 40 per thousand delta(18)O) of leaf waters. (Throughout this paper we use the term "leaf water" to describe the site of evaporation, which should not be confused with "bulk leaf water" a term used exclusively for uncorrected measurements obtained from whole leaf water extractions.) Regardless of how the isotopic composition of leaf water was achieved (i.e. by changes in source water, atmospheric vapor deltaD or delta(18)O, vapor pressure gradients, or combinations of all three), a modified version of the Craig-Gordon model was shown to be sound in its ability to predict the deltaD and delta(18)O values of water at the site of evaporation. The isotopic composition of atmospheric vapor was shown to have profound effects on the deltaD and delta(18)O of leaf water and its influence was dependent on vapor pressure gradients. These results have implications for conditions in which the isotopic composition of atmospheric vapor is not in equilibrium with source water, such as experimental systems that grow plants under isotopically enriched water regimes. The assumptions of steady state were also tested and found not to be a major limitation for the utilization of the leaf water model under relatively stable environmental conditions. After a major perturbation in the deltaD and delta(18)O of atmospheric vapor, the leaf reached steady state in approximately 2 h, depending on vapor pressure gradients. Following a step change in source water, the leaf achieved steady state in 24 h, with the vast majority of changes occurring in the first 3 h. Therefore, the Craig-Gordon model is a useful tool for understanding the environmental factors that influence the hydrogen and oxygen isotopic composition of leaf water as well as the organic matter derived from leaf water.
Central limit theorems for combinatorial optimization problems on sparse Erdős–Rényi graphs

For random combinatorial optimization problems, there has been much progress in establishing laws of large numbers and computing limiting constants for the optimal value of various problems. However, there has not been as much success in proving central limit theorems. This paper introduces a method for establishing central limit theorems in the sparse graph setting. It works for problems which display a key property which has been variously called "endogeny", "long-range independence", and "replica symmetry" in the literature. Examples of such problems are maximum weight matching, $\lambda$-diluted minimum matching, and optimal edge cover.
Influence of extracorporeal shock wave therapy (ESWT) on bone turnover markers in organisms with normal and low bone mineral density during fracture healing: a randomized clinical trial

Background: Low bone mineral density (BMD) leads to metaphyseal fractures, which are considered of delayed, qualitatively reduced healing resulting in prolonged care phases and increased socioeconomic costs. Extracorporeal shockwave therapy (ESWT) is already approved to support bone healing of pseudarthrosis and delayed unions. With this study, we examined its influence on bone turnover markers (BTM) during fracture healing in patients with low and normal BMD. Methods: Within a period of 2 years, patients with a metaphyseal fracture of the distal radius or the proximal humerus, requiring surgical osteosynthesis were included into the study. Patients were randomized within their fracture groups whether they received ESWT after surgery or not. ESWT was applied once after surgery with an energy flux density (EFD) of 0.55 mJ/mm² à 3000 shockwaves. In addition, serum levels of vitamin D3, parathyroid hormone (iPTH), bone alkaline phosphatase (BAP), c-telopeptide of type-I-collagen (β-CTX) and serum band 5 tartrate-resistant acid phosphate (TRAP5b) were determined before surgery and post-operatively in week 1, 4, 8, 52. T-score levels as an indicator of the BMD were measured with dual-energy X-ray absorptiometry (DXA). Results: 49 patients (40 females, 9 males; mean age 62 years) with fractures of the metaphyseal distal radius (n=25) or the proximal humerus (n=24) were included in the study. The follow-up time was one year. 24 of them were diagnosed of having low BMD, whereas 25 had a normal BMD. During follow-up time serum levels of bone turnover markers, as well as vitamin D3 and iPTH, showed no significant changes; however, ESWT approaches the decreased serum levels of patients with low BMD to the level of healthy organisms. Conclusions: ESWT as treatment option of fractures in patients with low BMD can lead to an equilibration of levels of bone turnover markers to the levels of patients with normal BMD.
Prediction of System Evolution by Learning Machine

The orthodox approach for understanding a dynamical system is to establish its equation of motion, by which one can unveil its dynamical behavior at a given system parameter set, and reveal how the dynamic behavior evolves as the system parameters change. Here we show that this task can be fulfilled with a learning machine in a model-free way. We find that, based only on a segmental time series of a state variable recorded at present stage, the dynamics exhibited by the learning machine at different training stages can be mapped to the dynamics of the target system along a particular path in its parameter space following an appropriate training strategy that monotonously decreases the cost. This path is important, because along which the primary dynamical properties of the target system will emerge subsequently, in the simple-to-complex order, matching closely to the evolution of a natural system. A theoretical framework is proposed to explain the underlying mechanism. This revealed function of the learning machine opens up a novel way to probe the global dynamical properties of a black-box system without the equation of motion established artificially, and as such it might have huge potential applications. As an application example, this method is applied to infer what dynamical stages a variable star has experienced and how it will evolve in future by using the light curve observed presently.
Improved Optical Flow for Gesture-based Human-robot Interaction

Gesture interaction is a natural way of communicating with a robot as an alternative to speech. Gesture recognition methods leverage optical flow in order to understand human motion. However, while accurate optical flow estimation (i.e., traditional) methods are costly in terms of runtime, fast estimation (i.e., deep learning) methods’ accuracy can be improved. In this paper, we present a pipeline for gesture-based human-robot interaction that uses a novel optical flow estimation method in order to achieve an improved speed-accuracy trade-off. Our optical flow estimation method introduces four improvements to previous deep learning-based methods: strong feature extractors, attention to contours, midway features, and a combination of these three. This results in a better understanding of motion, and a finer representation of silhouettes. In order to evaluate our pipeline, we generated our own dataset, MIBURI, which contains gestures to command a house service robot. In our experiments, we show how our method improves not only optical flow estimation, but also gesture recognition, offering a speed-accuracy trade-off more realistic for practical robot applications.
Single-particle Mie-resonant all-dielectric nanolasers

All-dielectric subwavelength structures utilizing Mie resonances provide a novel paradigm in nanophotonics for controlling and manipulating light. So far, only spontaneous emission enhancement was demonstrated with single dielectric nanoantennas, whereas stimulated emission was achieved only in large lattices supporting collective modes. Here, we demonstrate the first single-particle all-dielectric monolithic nanolaser driven by Mie resonances in visible and near-IR frequency range. We employ halide perovskite CsPbBr$_3$ as both gain and resonator material that provides high optical gain (up to $\sim 10^4$ cm$^{-1}$) and allows simple chemical synthesis of nanocubes with nearly epitaxial quality. Our smallest non-plasmonic Mie-resonant single-mode nanolaser with the size of 420 nm operates at room temperatures and wavelength 535 nm with linewidth $\sim 3.5$ meV. These novel lasing nanoantennas can pave the way to multifunctional photonic designs for active control of light at the nanoscale.
Random Walks on Hypergraphs with Edge-Dependent Vertex Weights

Hypergraphs are used in machine learning to model higher-order relationships in data. While spectral methods for graphs are well-established, spectral theory for hypergraphs remains an active area of research. In this paper, we use random walks to develop a spectral theory for hypergraphs with edge-dependent vertex weights: hypergraphs where every vertex $v$ has a weight $\gamma_e(v)$ for each incident hyperedge $e$ that describes the contribution of $v$ to the hyperedge $e$. We derive a random walk-based hypergraph Laplacian, and bound the mixing time of random walks on such hypergraphs. Moreover, we give conditions under which random walks on such hypergraphs are equivalent to random walks on graphs. As a corollary, we show that current machine learning methods that rely on Laplacians derived from random walks on hypergraphs with edge-independent vertex weights do not utilize higher-order relationships in the data. Finally, we demonstrate the advantages of hypergraphs with edge-dependent vertex weights on ranking applications using real-world datasets.
Economical Caching for Scalable Videos in Cache-Enabled Heterogeneous Networks

We develop the optimal economical caching schemes in cache-enabled heterogeneous networks, while delivering multimedia video services with personalized viewing qualities to mobile users. By applying scalable video coding (SVC), each video file to be requested is divided into one base layer (BL) and several enhancement layers (ELs). In order to assign different transmission tasks, the serving small-cell base stations (SBSs) are grouped into $K$ clusters. The SBSs are able to cache and cooperatively transmit BL and EL contents to the user. We analytically derive expressions for successful transmission probability and ergodic service rate, and then the closed-form expression for EConomical Efficiency (ECE) is obtained. In order to enhance the ECE performance, we formulate the ECE optimization problems for two cases. In the first case, with equal cache size equipped at each SBS, the layer caching indicator is determined. Since this problem is NP-hard, after the $l_{0}$ -norm approximation, the discrete optimization variables are relaxed to be continuous, and this relaxed problem is convex. Next, based on the optimal solution derived from the relaxed problem, we devise a greedy-strategy based heuristic algorithm to achieve the near-optimal layer caching indicators. In the second case, the cache size for each SBS, the layer size, and the layer caching indicator are jointly optimized. This problem is a mixed integer programming problem, which is more challenging. To effectively solve this problem, the original ECE maximization problem is divided into two subproblems. These two subproblems are iteratively solved until the original optimization problem is convergent. Numerical results verify the correctness of the theoretical derivations. Additionally, compared to the most popular layer placement strategy, the performance superiority of the proposed SVC-based caching schemes is testified.
Eukaryotic Translation Initiation Factor 5A (EIF5A) Regulates Pancreatic Cancer Metastasis by Modulating RhoA and Rho-associated Kinase (ROCK) Protein Expression Levels*

Background: Eukaryotic translation initiation factor 5A (eIF5A) regulates pancreatic cancer pathogenesis. Results: eIF5A was shown to control the expression of a set of key signaling molecules including RhoA and ROCK2, and to promote the invasive potential of pancreatic cancer cells. Conclusion: Hypusine/eIF5A/RhoA/ROCK cascade promotes pancreatic cancer cell metastasis. Significance: eIF5A may be a novel druggable target to treat metastatic pancreatic cancer. Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers with an overall survival rate of less than 5%. The poor patient outcome in PDAC is largely due to the high prevalence of systemic metastasis at the time of diagnosis and lack of effective therapeutics that target disseminated cells. The fact that the underlying mechanisms driving PDAC cell migration and dissemination are poorly understood have hindered drug development and compounded the lack of clinical success in this disease. Recent evidence indicates that mutational activation of K-Ras up-regulates eIF5A, a component of the cellular translational machinery that is critical for PDAC progression. However, the role of eIF5A in PDAC cell migration and metastasis has not been investigated. We report here that pharmacological inhibition or genetic knockdown of eIF5A reduces PDAC cell migration, invasion, and metastasis in vitro and in vivo. Proteomic profiling and bioinformatic analyses revealed that eIF5A controls an integrated network of cytoskeleton-regulatory proteins involved in cell migration. Functional interrogation of this network uncovered a critical RhoA/ROCK signaling node that operates downstream of eIF5A in invasive PDAC cells. Importantly, eIF5A mediates PDAC cell migration and invasion by modulating RhoA/ROCK protein expression levels. Together our findings implicate eIF5A as a cytoskeletal rheostat controlling RhoA/ROCK protein expression during PDAC cell migration and metastasis. Our findings also implicate the eIF5A/RhoA/ROCK module as a potential new therapeutic target to treat metastatic PDAC cells.
Adeno-associated virus mediated SOD gene therapy protects the retinal ganglion cells from chronic intraocular pressure elevation induced injury via attenuating oxidative stress and improving mitochondrial dysfunction in a rat model.

PURPOSE This study aimed to determine whether chronic intraocular pressure (IOP) elevation induces retinal oxidative stress and alters mitochondrial morphology and function of retinal ganglion cells (RGC) and to explore the effects of AAV-SOD2 gene therapy on the RGC survival and mitochondrial dysfunction. METHODS Chronic experimental glaucoma was induced unilaterally in adult male Sprague-Dawley rats by laser burns at trabecular meshwork and episcleral veins 2 times with an interval of one week. One eye of each rat was intravitreally pretreated with recombinant adeno-associated virus expressing SOD2 (AAV-SOD2) or recombinant AAV expressing GFP (AAV-GFP) 21 days before glaucoma induction. RGCs counting, morphometric analysis of retina and optic nerve, and detection of activities of retinal SOD2 and catalase, MDA, mitochondrial morphology, mitochondrial dynamin protein OPA1 and DRP-1 expressions were conducted at 4, 8, 12 and 24 weeks. RESULTS Severe RGC loss, degeneration of optic nerve, reduced thickness of RGC layer and nerve fiber layer, significant decrease in total SOD and catalase activities, mitochondrial dysfunction and increased MDA were observed at 4, 8, 12 and 24 weeks after glaucoma. Pretreatment with AAV-SOD2 significantly reduced MDA and attenuated the damage to RGCs through a mitochondria-related pathway. CONCLUSION AAV mediated pre-treatment with SOD2 is able to attenuate oxidative stress and improve mitochondrial dysfunction of RGC and optic nerve secondary to glaucoma. Thus, SOD2 may be used to prevent the retinal RGCs from glaucoma, which provides a promising strategy for glaucoma therapy.
A Seq-to-Seq Transformer Premised Temporal Convolutional Network for Chinese Word Segmentation

The prevalent approaches of Chinese word segmentation task almost rely on the Bi-LSTM neural network. However, the methods based the Bi-LSTM have some inherent drawbacks: hard to parallel computing, little efficient in applying the Dropout method to inhibit the Overfitting and little efficient in capturing the character information at the more distant site of a long sentence for the word segmentation task. In this work, we propose a sequence-to-sequence transformer model for Chinese word segmentation, which is premised a type of convolutional neural network named temporal convolutional network. The model uses the temporal convolutional network to construct an encoder, and uses one layer of fully-connected neural network to build a decoder, and applies the Dropout method to inhibit the Overfitting, and captures the character information at the distant site of a sentence by adding the layers of the encoder, and binds Conditional Random Fields model to train parameters, and uses the Viterbi algorithm to infer the final result of the Chinese word segmentation. The experiments on traditional Chinese corpora and simplified Chinese corpora show that the performance of Chinese word segmentation of the model is equivalent to the performance of the methods based the Bi-LSTM, and the model has a tremendous growth in parallel computing than the models based the Bi-LSTM.
Autonomous Wireless Systems with Artificial Intelligence.

This paper discusses technology and opportunities to embrace artificial intelligence (AI) in the design of autonomous wireless systems. We aim to provide readers with motivation and general AI methodology of autonomous agents in the context of self-organization in real time by unifying knowledge management with sensing, reasoning and active learning. We highlight differences between training-based methods for matching problems and training-free methods for environment-specific problems. Finally, we conceptually introduce the functions of an autonomous agent with knowledge management.