Miracle-dz/UniFAR-FTUs huggingface dataset:
A Disposable Blood-on-a-Chip for Simultaneous Measurement of Multiple Biophysical Properties

Biophysical properties are widely used to detect pathophysiological processes of vascular diseases or clinical states. For early detection of cardiovascular diseases, it is necessary to simultaneously measure multiple biophysical properties in a microfluidic environment. However, a microfluidic-based technique for measuring multiple biophysical properties has not been demonstrated. In this study, a simple measurement method was suggested to quantify three biophysical properties of blood, including red blood cell (RBC) deformability, RBC aggregation, and hematocrit. To demonstrate the suggested method, a microfluidic device was constructed, being composed of a big-sized channel (BC), a parallel micropillar (MP), a main channel, a branch channel, inlet, and outlets. By operating a single syringe pump, blood was supplied into the inlet of the microfluidic device, at a periodic on-off profile (i.e., period = 240 s). The RBC deformability index (DI) was obtained by analyzing the averaged blood velocity in the branch channel. Additionally, the RBC aggregation index (AIN) and the hematocrit index (HiBC) were measured by analyzing the image intensity of blood flows in the MP and the BC, respectively. The corresponding contributions of three influencing factors, including the turn-on time (Ton), the amplitude of blood flow rate (Q0), and the hematocrit (Hct) on the biophysical indices (DI, AIN, and HiBC) were evaluated quantitatively. As the three biophysical indices varied significantly with respect to the three factors, the following conditions (i.e., Ton = 210 s, Q0 = 1 mL/h, and Hct = 50%) were maintained for consistent measurement of biophysical properties. The proposed method was employed to detect variations of biophysical properties depending on the concentrations of autologous plasma, homogeneous hardened RBCs, and heterogeneous hardened RBCs. Based on the observations, the proposed method exhibited significant differences in biophysical properties depending on base solutions, homogeneous hardened RBCs (i.e., all RBCs fixed with the same concentration of glutaraldehyde solution), and heterogeneous hardened RBCs (i.e., partially mixed with normal RBCs and homogeneous hardened RBCs). Additionally, the suggested indices (i.e., DI, AIN, and HiBC) were effectively employed to quantify three biophysical properties, including RBC deformability, RBC aggregation, and hematocrit.
A Safety and Security Architecture for Reducing Accidents in Intelligent Transportation Systems

The Internet of Things (IoT) technology is transforming the world into Smart Cities, which have a huge impact on future societal lifestyle, economy and business. Intelligent Transportation Systems (ITS), especially IoT-enabled Electric Vehicles (EVs), are anticipated to be an integral part of future Smart Cities. Assuring ITS safety and security is critical to the success of Smart Cities because human lives are at stake. The state-of-the-art understanding of this matter is very superficial because there are many new problems that have yet to be investigated. For example, the cyber-physical nature of ITS requires considering human-in-the-loop (i.e., drivers and pedestrians) and imposes many new challenges. In this paper, we systematically explore the threat model against ITS safety and security (e.g., malfunctions of connected EVs/transportation infrastructures, driver misbehavior and unexpected medical conditions, and cyber attacks). Then, we present a novel and systematic ITS safety and security architecture, which aims to reduce accidents caused or amplified by a range of threats. The architecture has appealing features: (i) it is centered at proactive cyber-physical-human defense; (ii) it facilitates the detection of early-warning signals of accidents; (iii) it automates effective defense against a range of threats.
Kernel Exponential Family Estimation via Doubly Dual Embedding

We investigate penalized maximum log-likelihood estimation for exponential family distributions whose natural parameter resides in a reproducing kernel Hilbert space. Key to our approach is a novel technique, doubly dual embedding, that avoids computation of the partition function. This technique also allows the development of a flexible sampling strategy that amortizes the cost of Monte-Carlo sampling in the inference stage. The resulting estimator can be easily generalized to kernel conditional exponential families. We establish a connection between kernel exponential family estimation and MMD-GANs, revealing a new perspective for understanding GANs. Compared to the score matching based estimators, the proposed method improves both memory and time efficiency while enjoying stronger statistical properties, such as fully capturing smoothness in its statistical convergence rate while the score matching estimator appears to saturate. Finally, we show that the proposed estimator empirically outperforms state-of-the-art
Access and mobility of wireless PDA users

As wireless network access proliferates, understanding user behavior and wireless network performance has become crucial as a basis for developing and evaluating new applications, network infrastructure, and wireless communication architectures. Over the past few years, there have been a number of wireless workload studies characterizing user behavior and network performance in a variety of settings, including conferences [1], corporate networks [2], university campuses [3], and metropolitan networks [4].
Off-the-Shelf Unsupervised NMT

We frame unsupervised machine translation (MT) in the context of multi-task learning (MTL), combining insights from both directions. We leverage off-the-shelf neural MT architectures to train unsupervised MT models with no parallel data and show that such models can achieve reasonably good performance, competitive with models purpose-built for unsupervised MT. Finally, we propose improvements that allow us to apply our models to English-Turkish, a truly low-resource language pair.
Synaptic Strength For Convolutional Neural Network

Convolutional Neural Networks(CNNs) are both computation and memory intensive which hindered their deployment in mobile devices. Inspired by the relevant concept in neural science literature, we propose Synaptic Pruning: a data-driven method to prune connections between input and output feature maps with a newly proposed class of parameters called Synaptic Strength. Synaptic Strength is designed to capture the importance of a connection based on the amount of information it transports. Experiment results show the effectiveness of our approach. On CIFAR-10, we prune connections for various CNN models with up to 96% , which results in significant size reduction and computation saving. Further evaluation on ImageNet demonstrates that synaptic pruning is able to discover efficient models which is competitive to state-of-the-art compact CNNs such as MobileNet-V2 and NasNet-Mobile. Our contribution is summarized as following: (1) We introduce Synaptic Strength, a new class of parameters for CNNs to indicate the importance of each connections. (2) Our approach can prune various CNNs with high compression without compromising accuracy. (3) Further investigation shows, the proposed Synaptic Strength is a better indicator for kernel pruning compared with the previous approach in both empirical result and theoretical analysis.
Findings of the WMT 2018 Shared Task on Automatic Post-Editing

We present the results from the fourth round of the WMT shared task on MT Automatic Post-Editing. The task consists in automatically correcting the output of a “black-box” machine translation system by learning from human corrections. Keeping the same general evaluation setting of the three previous rounds, this year we focused on one language pair (English-German) and on domain-specific data (Information Technology), with MT outputs produced by two different paradigms: phrase-based (PBSMT) and neural (NMT). Five teams submitted respectively 11 runs for the PBSMT subtask and 10 runs for the NMT subtask. In the former subtask, characterized by original translations of lower quality, top results achieved impressive improvements, up to -6.24 TER and +9.53 BLEU points over the baseline “do-nothing” system. The NMT subtask proved to be more challenging due to the higher quality of the original translations and the availability of less training data. In this case, top results show smaller improvements up to -0.38 TER and +0.8 BLEU points.
Accurate Single-Sequence Prediction of Protein Intrinsic Disorder by an Ensemble of Deep Recurrent and Convolutional Architectures

Recognizing the widespread existence of intrinsically disordered regions in proteins spurred the development of computational techniques for their detection. All existing techniques can be classified into methods relying on single-sequence information and those relying on evolutionary sequence profiles generated from multiple-sequence alignments. The methods based on sequence profiles are, in general, more accurate because the presence or absence of conserved amino acid residues in a protein sequence provides important information on the structural and functional roles of the residues. However, the wide applicability of profile-based techniques is limited by time-consuming calculation of sequence profiles. Here we demonstrate that the performance gap between profile-based techniques and single-sequence methods can be reduced by using an ensemble of deep recurrent and convolutional neural networks that allow whole-sequence learning. In particular, the single-sequence method (called SPOT-Disorder-Single) is more accurate than SPOT-Disorder (a profile-based method) for proteins with few homologous sequences and comparable for proteins in predicting long-disordered regions. The method performance is robust across four independent test sets with different amounts of short- and long-disordered regions. SPOT-Disorder-Single is available as a Web server and as a standalone program at http://sparks-lab.org/jack/server/SPOT-Disorder-Single .
Electronics as material: littleBits

Electronics are everywhere. We now produce, consume and throw out more electronic gadgets than ever before. Yet, creativity with electronics starts when they can be thought of as sketching materials like paper, screws, cardboard. This Studio will allow participants to create prototypes of interactive gadgets using a diverse library of materials: littleBits (an opensource kit of pre-assembled electronics that snap together with tiny magnets), combined with traditional design materials (foam, fabric, paper, clay etc). The Studio structure is in several phases. The participants start by conceptualizing an interactive object or small scale installation, break down the interaction, and build it using familiar and unfamiliar materials. Participants will then present their creation and discuss the process of expanding their materials library to include light, sound, sensing, and motion. Finally, the participants will come up with ideas for new littleBits modules to suggest or contribute to the open source kit and community.
Low-Rank and Sparse Matrix Factorization for Scientific Paper Recommendation in Heterogeneous Network

With the rapid growth of scientific publications, it is hard for researchers to acquire appropriate papers that meet their expectations. Recommendation system for scientific articles is an essential technology to overcome this problem. In this paper, we propose a novel low-rank and sparse matrix factorization-based paper recommendation (LSMFPRec) method for authors. The proposed method seamlessly combines low-rank and sparse matrix factorization method with fine-grained paper and author affinity matrixes that are extracted from heterogeneous scientific network. Thus, it can effectively alleviate the sparsity and cold start problems that exist in traditional matrix factorization based collaborative filtering methods. Moreover, LSMFPRec can significantly reduce the error propagated from intermediate outputs. In addition, the proposed method essentially captures the low-rank and sparse characteristics that exist in scientific rating activities; therefore, it can generate more reasonable predicted ratings for influential and uninfluential papers. The effectiveness of the proposed LSMFPRec is demonstrated by the recommendation evaluation conducted on the AAN and CiteULike data sets.
Comment on “Dermatoscopic features of lichen nitidus”

To the Editor, We read with great interest the article by Jakhar et al on dermoscopic features observed in eight instances of lichen nitidus (LN). The authors found ill‐defined hypopigmentation and the absence of dermatoglyphics (better visible in polarizing and nonpolarizing mode, respectively) to be the main dermoscopic clues of such a condition. These findings are basically in line with what we previously described in the literature based on the observation of 10 cases, yet we would like to underline a few relevant points in order to better define the dermoscopic aspect of LN:
Detailed Visual Cortical Responses Generated by Retinal Sheet Transplants in Rats with Severe Retinal Degeneration

To combat retinal degeneration, healthy fetal retinal sheets have been successfully transplanted into both rodent models and humans, with synaptic connectivity between transplant and degenerated host retina having been confirmed. In rodent studies, transplants have been shown to restore responses to flashes of light in a region of the superior colliculus corresponding to the location of the transplant in the host retina. To determine the quality and detail of visual information provided by the transplant, visual responsivity was studied here at the level of visual cortex where higher visual perception is processed. For our model, we used the transgenic Rho-S334ter line-3 rat (both sexes), which loses photoreceptors at an early age and is effectively blind at postnatal day 30. These rats received fetal retinal sheet transplants in one eye between 24 and 40 d of age. Three to 10 months following surgery, visually responsive neurons were found in regions of primary visual cortex matching the transplanted region of the retina that were as highly selective as normal rat to stimulus orientation, size, contrast, and spatial and temporal frequencies. Conversely, we found that selective response properties were largely absent in nontransplanted line-3 rats. Our data show that fetal retinal sheet transplants can result in remarkably normal visual function in visual cortex of rats with a degenerated host retina and represents a critical step toward developing an effective remedy for the visually impaired human population. SIGNIFICANCE STATEMENT Age-related macular degeneration and retinitis pigmentosa lead to profound vision loss in millions of people worldwide. Many patients lose both retinal pigment epithelium and photoreceptors. Hence, there is a great demand for the development of efficient techniques that allow for long-term vision restoration. In this study, we transplanted dissected fetal retinal sheets, which can differentiate into photoreceptors and integrate with the host retina of rats with severe retinal degeneration. Remarkably, we show that transplants generated visual responses in cortex similar in quality to normal rats. Furthermore, transplants preserved connectivity within visual cortex and the retinal relay from the lateral geniculate nucleus to visual cortex, supporting their potential application in curing vision loss associated with retinal degeneration.
Evaluation of a Novel General Pituitary Hormone Score to Evaluate the Function of the Residual Anterior Pituitary (Adenohypophysis) in Patients Following Surgery for Pituitary Adenoma

Background The aim was to develop and assess a general pituitary hormone score to evaluate the function of the anterior pituitary (adenohypophysis) in patients following resection of pituitary adenomas. Material/Methods Sixty-six patients with pituitary null cell macroadenoma (1–3 cm diameter) (N=38) and pituitary null cell giant adenoma (≥3 cm diameter) (N=28) had preoperative and postoperative data including magnetic resonance imaging (MRI) and measurement of six pituitary hormones levels, adrenocorticotropic hormone (ACTH), growth hormone (GH), thyroid-stimulating hormone (TSH), prolactin (PRL), follicle-stimulating hormone (FSH), and luteinizing hormone (LH). The postoperative general pituitary hormone score, for 57 patients who underwent subtotal resection (>60%) and nine patients who underwent partial resection (≤60%), was 1–5 for each hormone level (score range, 6–30). Results ACTH, GH, TSH, PRL, FSH, and LH levels in 38 patients with pituitary null cell macroadenoma were not statistically different from the 28 patients with pituitary null cell giant adenoma; the general pituitary hormone score in the former group was significantly increased compared with the latter group (P<0.05). ACTH, GH, TSH, PRL, FSH, and LH levels in the 57 patients with subtotal tumor resection were not significantly different from the nine patients with partial tumor resection; the general pituitary hormone score in the former group was significantly reduced compared with the latter group (P<0.05). Conclusions A general pituitary hormone score was developed that might be relevant to the evaluation of pituitary function following surgical resection of pituitary null cell macroadenoma and giant adenoma.
Are Deep Policy Gradient Algorithms Truly Policy Gradient Algorithms?

We study how the behavior of deep policy gradient algorithms reflects the conceptual framework motivating their development. We propose a fine-grained analysis of state-of-the-art methods based on key aspects of this framework: gradient estimation, value prediction, optimization landscapes, and trust region enforcement. We find that from this perspective, the behavior of deep policy gradient algorithms often deviates from what their motivating framework would predict. Our analysis suggests first steps towards solidifying the foundations of these algorithms, and in particular indicates that we may need to move beyond the current benchmark-centric evaluation methodology.
Not Just Depressed: Bipolar Disorder Prediction on Reddit

Bipolar disorder, an illness characterized by manic and depressive episodes, affects more than 60 million people worldwide. We present a preliminary study on bipolar disorder prediction from user-generated text on Reddit, which relies on users’ self-reported labels. Our benchmark classifiers for bipolar disorder prediction outperform the baselines and reach accuracy and F1-scores of above 86%. Feature analysis shows interesting differences in language use between users with bipolar disorders and the control group, including differences in the use of emotion-expressive words.
Sensor-Based Datasets for Human Activity Recognition – A Systematic Review of Literature

The research area of ambient assisted living has led to the development of activity recognition systems (ARS) based on human activity recognition (HAR). These systems improve the quality of life and the health care of the elderly and dependent people. However, before making them available to end users, it is necessary to evaluate their performance in recognizing activities of daily living, using data set benchmarks in experimental scenarios. For that reason, the scientific community has developed and provided a huge amount of data sets for HAR. Therefore, identifying which ones to use in the evaluation process and which techniques are the most appropriate for prediction of HAR in a specific context is not a trivial task and is key to further progress in this area of research. This work presents a systematic review of the literature of the sensor-based data sets used to evaluate ARS. On the one hand, an analysis of different variables taken from indexed publications related to this field was performed. The sources of information are journals, proceedings, and books located in specialized databases. The analyzed variables characterize publications by year, database, type, quartile, country of origin, and destination, using scientometrics, which allowed identification of the data set most used by researchers. On the other hand, the descriptive and functional variables were analyzed for each of the identified data sets: occupation, annotation, approach, segmentation, representation, feature selection, balancing and addition of instances, and classifier used for recognition. This paper provides an analysis of the sensor-based data sets used in HAR to date, identifying the most appropriate dataset to evaluate ARS and the classification techniques that generate better results.
FRAME-LEVEL PROXIMITY AND TOUCH RECOGNITION USING CAPACITIVE SENSING AND SEMI-SUPERVISED SEQUENTIAL MODELING

This paper demonstrates a semi-supervised learning approach to frame-level proximity and touch recognition with machine learning algorithms for sequential modeling. We focus on capacitive sensing, which is employable in low cost embedded devices and provides high sensing capability. We optimize our models to run with minimum complexity to enable the use of state-of-the-art machine learning models in low cost embedded devices. We evaluate two different models, either based on recurrent neural networks (RNN) with gated recurrent units or hidden markov models (HMM). We show that the developed models are capable of a robust proximity and touch recognition invariant to interference factors. However, the RNN model outperforms the HMM model reaching a superior frame-level recognition accuracy of 97.1% on a challenging set of touches containing multiple interference factors like the use of gloves with different materials and invalid touches, where the test persons swiped over the sensor.
Hunter NMT System for WMT18 Biomedical Translation Task: Transfer Learning in Neural Machine Translation

This paper describes the submission of Hunter Neural Machine Translation (NMT) to the WMT’18 Biomedical translation task from English to French. The discrepancy between training and test data distribution brings a challenge to translate text in new domains. Beyond the previous work of combining in-domain with out-of-domain models, we found accuracy and efficiency gain in combining different in-domain models. We conduct extensive experiments on NMT with transfer learning. We train on different in-domain Biomedical datasets one after another. That means parameters of the previous training serve as the initialization of the next one. Together with a pre-trained out-of-domain News model, we enhanced translation quality with 3.73 BLEU points over the baseline. Furthermore, we applied ensemble learning on training models of intermediate epochs and achieved an improvement of 4.02 BLEU points over the baseline. Overall, our system is 11.29 BLEU points above the best system of last year on the EDP 2017 test set.
PROX1 Is Associated with Cancer Progression and Prognosis in Gastric Cancer

Background: It was recently reported that expression of prospero homeobox protein-1 (PROX1) is correlated with the prognosis of esophageal cancer and colorectal cancer. However, its correlation with gastric cancer is unclear. Materials and Methods: Our study analyzed the effect of PROX1 knockdown on the migration, invasion and proliferation of the MKN45 human gastric cancer cell line. The correlation between PROX1 expression levels and clinicopathological factors were also analyzed in tumor samples from 99 patients with gastric cancer. Results: Migration, invasion and proliferation were significantly reduced in MKN45 cells with PROX1 knockdown. PROX1 expression was detected in gastric cancer tissues at various levels. PROX1 expression levels were positively correlated with cancer stage, N factor, lymphatic vascular invasion, and vascular invasion in patients with gastric cancer. Analysis of overall and recurrence-free survival indicated that high PROX1 expression was significantly correlated with poor prognosis. Conclusion: PROX1 can be an indicator of poor prognosis and a molecular target for gastric cancer treatment.