Miracle-dz/UniFAR-FTUs huggingface dataset:
The Partition Spanning Forest Problem

Given a set of colored points in the plane, we ask if there exists a crossing-free straight-line drawing of a spanning forest, such that every tree in the forest contains exactly the points of one color class. We show that the problem is NP-complete, even if every color class contains at most five points, but it is solvable in $O(n^2)$ time when each color class contains at most three points. If we require that the spanning forest is a linear forest, then the problem becomes NP-complete even if every color class contains at most four points.
Design and Implementation of Convolutional Neural Network Accelerator with Variable Layer-by-layer Debugging

Deep learning algorithms have complex network structures and numerous parameters, and are typical computation-intensive and data-intensive applications. Due to the large amount of data and the difference in accuracy, it is very difficult to realize the function and to adjust the accuracy of FPGA-based deep learning accelerator, which seriously affects the applicability of the accelerator. To this problem, this paper presents a convolutional neural network accelerator with variable layer-by-layer debugging. The accelerator framework consists of host computer, PCIE interface, DDR module, transmission control module, CNN module and variable layer-by-layer debugging module. The variable layer-by-layer debugging module consists of DRAM, FIFO, read DRAM counting module, write DRAM counting module and data alignment module. The debugging module can be assembled in any layer of the convolutional neural network, and effectively achieve layer-by-layer debugging in cooperation with host computer. Supported by this design framework, the paper implements VGG-S on a FPGA board based on the Xilinx XCKU115 chip, achieving an acceleration ratio of 24.78 compared to the CPU platform and a 14.6x performance-to-power ratio. The comprehensive results show that the hardware resource overhead with variable layer-by-layer debugging module is very small and does not affect the frequency of the convolutional network implementation. The experimental process verified the high efficiency of the implementation and debugging of the structure, which can be used as a debugging method for various pipelined convolutional networks in the future.
OXA1L mutations cause mitochondrial encephalopathy and a combined oxidative phosphorylation defect

OXA1, the mitochondrial member of the YidC/Alb3/Oxa1 membrane protein insertase family, is required for the assembly of oxidative phosphorylation complexes IV and V in yeast. However, depletion of human OXA1 (OXA1L) was previously reported to impair assembly of complexes I and V only. We report a patient presenting with severe encephalopathy, hypotonia and developmental delay who died at 5 years showing complex IV deficiency in skeletal muscle. Whole exome sequencing identified biallelic OXA1L variants (c.500_507dup, p.(Ser170Glnfs*18) and c.620G>T, p.(Cys207Phe)) that segregated with disease. Patient muscle and fibroblasts showed decreased OXA1L and subunits of complexes IV and V. Crucially, expression of wild‐type human OXA1L in patient fibroblasts rescued the complex IV and V defects. Targeted depletion of OXA1L in human cells or Drosophila melanogaster caused defects in the assembly of complexes I, IV and V, consistent with patient data. Immunoprecipitation of OXA1L revealed the enrichment of mtDNA‐encoded subunits of complexes I, IV and V. Our data verify the pathogenicity of these OXA1L variants and demonstrate that OXA1L is required for the assembly of multiple respiratory chain complexes.
Relationship of Cell Compositions in Allografts with Outcomes after Haploidentical Transplantation for Acquired Severe Aplastic Anemia: Effects of CD34+ and CD14+ Cell Doses

Background: The dose of certain cell types in allografts affects engraftment kinetics and clinical outcomes after allogeneic stem cell transplantation (SCT). Hence, the present study investigated the association of cell compositions in allografts with outcomes after unmanipulated haploidentical SCT (haplo-SCT) for patients with acquired severe aplastic anemia (SAA). Methods: A total of 131 patients with SAA who underwent haplo-SCT were retrospectively enrolled. Cell subsets in allografts were determined using flow cytometry. To analyze the association of cellular compositions and outcomes, Mann–Whitney U nonparametric tests were conducted for patient age, sex, weight, human leukocyte antigen mismatched loci, ABO-matched status, patient ABO blood type, donor-recipient sex match, donor-recipient relationship, and each graft component. Multivariate analysis was performed using logistic regression to determine independent influence factors involving dichotomous variables selected from the univariate analysis. Results: A total of 126 patients (97.7%) achieved neutrophil engraftment, and 121 patients (95.7%) achieved platelet engraftment. At 100 days after transplantation, the cumulative incidence of II–IV acute graft-versus-host disease (GVHD) was 32.6%. After a median follow-up of 842 (range: 124–4110) days for surviving patients, the cumulative incidence of total chronic GVHD at 3 years after transplantation was 33.7%. The probability of overall survival at 3 years was 83.0%. Multivariate analysis showed that higher total doses of CD14+ (P = 0.018) and CD34+ cells (P < 0.001) were associated with a successful platelet engraftment. A successful platelet was associated with superior survival (P < 0.001). No correlation of other cell components with outcomes was observed. Conclusions: These results provide evidence and explain that higher doses of CD34+ and CD14+ cells in haploidentical allografts positively affect platelet engraftment, contributing to superior survival for patients with SAA.
Gold Rod-Polyethylene Glycol-Carbon Dot Nanohybrids as Phototheranostic Probes

Emphasis using phototheranostics has been placed on the construction of multifunctional nanoplatforms for simultaneous tumor diagnosis and therapy. Herein, we put forth a novel nanosized luminescent material using the incorporation of red emissive carbon dots on gold nanorods through polyethylene glycol as a covalent linkage for dual-modal imaging and photothermal therapy. The novel nanohybrids, not only retain the optical properties of the gold nanorod and carbon dots, but also possess superior imaging performance in both confocal laser scanning microscopy and fluorescence lifetime imaging microscopy. The nanohybrids also exhibit excellent photothermal performance as phototheranostic nanohybrid probes for in vitro assays. This study promises a new multifunctional nanoplatform for cancer diagnostics and therapeutics.
A randomized controlled trial to study the effect of gross myofascial release on mechanical neck pain referred to upper limb

Objective: Mechanical neck pain (NP) with referred pain to upper limb is a common problem and often leads to functional impairment of common activities of daily living. The present study is undertaken to study and compare the effect of gross myofascial release (MFR) of upper limb and neck alone with conventional physiotherapy against only conventional treatment in subjects with mechanical NP referred to upper limb in terms of cervical endurance, pain, range of motion, and function. Methods: Design: This was a experimental study; a total of 40 subjects clinically diagnosed with mechanical NP along with referred pain between the age group of 20 and 50 years. Intervention: Control group was given conventional treatment of hot moist pack, TENS, and stretching and strengthening exercise, and experimental group was given gross MFR of the neck and upper limb in addition to conventional therapy. Treatment was given for 6 consecutive days. Outcome measures used were pressure biofeedback to measure cervical endurance, goniometer for cervical ROM, Northwick Park NP questionnaire, and disabilities of arm, shoulder, and hand questionnaire. Results: Statistically significant change was present for pain, cervical flexure endurance, ROM, and functional abilities with P < 0.05 for both the groups except for neck flexor endurance in control group. Conclusion: Gross MFR of upper limb and neck is an effective technique for subjects with mechanical NP and has a faster rate of improvement.
The 'Self' as Barrier for Self-Management Technologies in Healthcare?

Self-management technologies are promising in healthcare. In the name of patient empowerment, they can address an important challenge: how to meet an increasing demand for care without additional resources. We designed and evaluated the PICASSO-Tx system: a technology solution to support transplant patients and their self-management of diet, physical activity and medication adherences. In a Sield study, 19 patients used the system for two weeks at home. We discovered that although we intended patients to become actors in the management of their own health, they saw themselves rather as a subject, leaving the active role for caregivers. In this paper, we describe how patients expected their data to be shared with others, how they perceived the system as a tool for monitoring by caregivers, and what expectations towards these caregivers this perception brings. Furthermore, we discuss the barriers we encountered caused by the 'self' and conclude with design recommendations for self-management technologies in healthcare related to these expectations, perceptions, and barriers.
Classification by Re-generation: Towards Classification Based on Variational Inference

As Deep Neural Networks (DNNs) are considered the state-of-the-art in many classification tasks, the question of their semantic generalizations has been raised. To address semantic interpretability of learned features, we introduce a novel idea of classification by re-generation based on variational autoencoder (VAE) in which a separate encoder-decoder pair of VAE is trained for each class. Moreover, the proposed architecture overcomes the scalability issue in current DNN networks as there is no need to re-train the whole network with the addition of new classes and it can be done for each class separately. We also introduce a criterion based on Kullback-Leibler divergence to reject doubtful examples. This rejection criterion should improve the trust in the obtained results and can be further exploited to reject adversarial examples.
The Enhanced Interactive Physical and Cognitive Exercise System (iPACESTM v2.0): Pilot Clinical Trial of an In-Home iPad-Based Neuro-Exergame for Mild Cognitive Impairment (MCI)

Given increasing longevity worldwide, older adults and caregivers are seeking ways to curb cognitive decline especially for those with mild cognitive impairment (MCI, now mild neurocognitive disorder, mNCD, Diagnostic and Statistical Manual of Mental Disorders, 5th ed. (DSM-V). This quasi-experimental, within-subjects pilot clinical trial was designed to replicate and extend the study of cognitive benefits for MCI by improving upon our prior interactive Physical and Cognitive Exercise Study (iPACESTM v1.0) by increasing the usability of the neuro-exergame and exploring possible underlying neurobiological mechanisms. Older adults were enrolled in a three-month, in-home trial of a portable neuro-exergame (iPACES™ v2.0) where participants pedaled and steered along a virtual bike path (Memory Lane™). Neuropsychological function was assessed at baseline after component familiarization intervals (e.g., two weeks of exercise-only, game-only, etc.) and after three months of interactive neuro-exergame intervention. Fourteen participants were enrolled in the study and seven completed the final evaluation. Intent-to-treat analyses were conducted with imputed missing data (total n = 14). Significant improvement in executive function (Stroop) was found (d = 0.68, p = 0.02) only. Changes in salivary biomarkers (cortisol and insulin-like growth factor 1; IGF-1) were significantly associated with improved cognition. Further research is needed, but pilot data suggest that a portable in-home neuro-exergame may be an additional, practical tool to fight back against cognitive decline and dementia.
Iron Absorption in Iron-Deficient Women, Who Received 65 mg Fe with an Indonesian Breakfast, Is Much Better from NaFe(III)EDTA than from Fe(II)SO4, with an Acceptable Increase of Plasma NTBI. A Randomized Clinical Trial

Plasma non-transferrin-bound iron (NTBI) is potentially harmful due to the generation of free radicals that cause tissue damage in vascular and other diseases. Studies in iron-replete and iron-deficient subjects, receiving a single oral test dose of Fe(II)SO4 or NaFe(III)EDTA with water, revealed that FeSO4 was well absorbed when compared with NaFeEDTA, while only the Fe(II) compound showed a remarkable increase of NTBI. As NaFeEDTA is successfully used for food fortification, a double-blind randomized cross-over trial was conducted in 11 healthy women with uncomplicated iron deficiency. All subjects received a placebo, 6.5 mg FeSO4, 65 mg FeSO4, 6.5 mg NaFeEDTA, and 65 mg NaFeEDTA with a traditional Indonesian breakfast in one-week intervals. Blood tests were carried out every 60 min for five hours. NTBI detection was performed using the fluorescein-labeled apotransferrin method. Plasma iron values were highly increased after 65 mg NaFeEDTA, twice as high as after FeSO4. A similar pattern was seen for NTBI. After 6.5 mg of NaFeEDTA and FeSO4, NTBI was hardly detectable. NaFeEDTA was highly effective for the treatment of iron deficiency if given with a meal, inhibiting the formation of nonabsorbable Fe-complexes, while NTBI did not exceed the range of normal values for iron-replete subjects.
Toward a Standardized and More Accurate Indonesian Part-of-Speech Tagging

Previous work in Indonesian part-of-speech (POS) tagging are hard to compare as they are not evaluated on a common dataset. Furthermore, in spite of the success of neural network models for English POS tagging, they are rarely explored for Indonesian. In this paper, we explored various techniques for Indonesian POS tagging, including rule-based, CRF, and neural network-based models. We evaluated our models on the IDN Tagged Corpus. A new state-of-the-art of 97.47 F1 score is achieved with a recurrent neural network. To provide a standard for future work, we release the dataset split that we used publicly.
Marine Microalgae: Promising Source for New Bioactive Compounds

The study of marine natural products for their bioactive potential has gained strength in recent years. Oceans harbor a vast variety of organisms that offer a biological and chemical diversity with metabolic abilities unrivalled in terrestrial systems, which makes them an attractive target for bioprospecting as an almost untapped resource of biotechnological applications. Among them, there is no doubt that microalgae could become genuine “cell factories” for the biological synthesis of bioactive substances. Thus, in the course of inter-laboratory collaboration sponsored by the European Union (7th FP) into the MAREX Project focused on the discovery of novel bioactive compounds of marine origin for the European industry, a bioprospecting study on 33 microalgae strains was carried out. The strains were cultured at laboratory scale. Two extracts were prepared for each one (biomass and cell free culture medium) and, thus, screened to provide information on the antimicrobial, the anti-proliferative, and the apoptotic potential of the studied extracts. The outcome of this study provides additional scientific data for the selection of Alexandrium tamarensis WE, Gambierdiscus australes, Prorocentrum arenarium, Prorocentrum hoffmannianum, and Prorocentrum reticulatum (Pr-3) for further investigation and offers support for the continued research of new potential drugs for human therapeutics from cultured microalgae.
Investigating Decision Tree in Churn Prediction with Class Imbalance

Class imbalance presents significant challenges to customer churn prediction. Traditional machine learning algorithms like decision tree tend to be biased towards majority class. In this paper, we comprehensively study the performance of decision tree in churn prediction with class imbalance. We investigate the issue of pruning setting and optimal sampling strategy based on a recently developed expected maximum profit criterion. The experiments present some different conclusions from the previous research when the area under the ROC curve is used and the optimal sampling strategy are recommended. Our findings provides a useful guideline for usage of decision tree in churn prediction.
Entity Retrieval in the Knowledge Graph with Hierarchical Entity Type and Content

We investigate the task of ad-hoc entity retrieval from a knowledge graph with hierarchical entity types and entity descriptions. Our model directly encodes them into a Markov random field based framework via a path aware smoothing method. We conduct experiments on recent benchmark datasets and investigate the incorporation of the Wikipedia type and article information. The results show that our framework achieves improvements over the existing and state-of-the-art models.
Entire Information Attentive GRU for Text Representation

Recurrent Neural Networks~(RNNs), such as Long Short-Term Memory~(LSTM) and Gated Recurrent Unit~(GRU), have been widely utilized in sequence representation. However, RNNs neglect variational information and long-term dependency. In this paper, we propose a new neural network structure for extracting a comprehension sequence embedding by handling the entire representation of the sequence. Unlike previous works that put attention mechanism after all steps of GRU, we add the entire representation to the input of the GRU which means the GRU model takes the entire information of the sequence into consideration in every step. We provide three various strategies to adding the entire information which are the Convolutional Neural Network~(CNN) based attentive GRU~(CBAG), the GRU inner attentive GRU~(GIAG) and the pre-trained GRU inner attentive GRU~(Pre-GIAG). To evaluate our proposed methods, we conduct extensive experiments on a benchmark sentiment classification dataset. Our experimental results show that our models outperform state-of-the-art baselines significantly.
Cerium Oxide Nanoparticles Sensitize Pancreatic Cancer to Radiation Therapy through Oxidative Activation of the JNK Apoptotic Pathway

Side effects of radiation therapy (RT) remain the most challenging issue for pancreatic cancer treatment. Cerium oxide nanoparticles (CONPs) are currently being tested in pre-clinical trials as an adjuvant to sensitize pancreatic cancer cells to RT and protect normal tissues from the harmful side effects. CONPs were not able to significantly affect RT-induced DNA damage in cancer cells, thereby ruling out sensitization through increased mitotic catastrophe. However, activation of c-Jun terminal kinase (JNK), a key driver of RT-induced apoptosis, was significantly enhanced by co-treatment with CONPs and RT in pancreatic cancer cells in vitro and human pancreatic tumors in nude mice in vivo compared to CONPs or RT treatment alone. Further, CONP-driven increase in RT-induced JNK activity was associated with a marked increase in Caspase 3/7 activation, indicative of apoptosis. We have previously shown that CONPs increase reactive oxygen species (ROS) production in cancer cells. ROS has been shown to drive the oxidation of thioredoxin 1 (TRX1) which results in the activation of apoptosis signaling kinase 1 (ASK1). The increase in ASK1 activation following the co-treatment with CONPs followed by RT suggests that the increased JNK activation is the result of increased TRX1 oxidation. The ability of CONPs to sensitize pancreatic cancer cells to RT was mitigated when the TRX1 oxidation was prevented by mutagenesis of a cysteine residue or when the JNK activation was blocked by an inhibitor. Taken together, these data demonstrate an important mechanism for CONPs in specifically killing cancer cells and provide novel insights into the utilization of CONPs as a radiosensitizer and therapeutic agent for pancreatic cancer.
Rate-Adaptive Neural Networks for Spatial Multiplexers

In resource-constrained environments, one can employ spatial multiplexing cameras to acquire a small number of measurements of a scene, and perform effective reconstruction or high-level inference using purely data-driven neural networks. However, once trained, the measurement matrix and the network are valid only for a single measurement rate (MR) chosen at training time. To overcome this drawback, we answer the following question: How can we jointly design the measurement operator and the reconstruction/inference network so that the system can operate over a \textit{range} of MRs? To this end, we present a novel training algorithm, for learning \textbf{\textit{rate-adaptive}} networks. Using standard datasets, we demonstrate that, when tested over a range of MRs, a rate-adaptive network can provide high quality reconstruction over a the entire range, resulting in up to about 15 dB improvement over previous methods, where the network is valid for only one MR. We demonstrate the effectiveness of our approach for sample-efficient object tracking where video frames are acquired at dynamically varying MRs. We also extend this algorithm to learn the measurement operator in conjunction with image recognition networks. Experiments on MNIST and CIFAR-10 confirm the applicability of our algorithm to different tasks.
Selective Oxidation of Hydrogen Sulfide to Sulfur Using Vanadium Oxide Supported on Porous Clay Heterostructures (PCHs) Formed by Pillars Silica, Silica-Zirconia or Silica-Titania

Vanadium oxide (V2O5) species has been supported on different porous clay heterostructures (with silica pillars, silica-zirconia with a molar ratio Si/Zr = 5 and silica-titania with a molar ratio Si/Ti = 5) by wetness incipient method. All catalysts were characterized by X-ray diffraction (XRD), N2 adsorption-desorption at −196 °C, NH3 thermoprogrammed desorption (NH3-TPD), Raman spectroscopy, diffuse reflectance UV-Vis and X-ray photoelectron spectroscopy (XPS). After that, the catalytic activity of the vanadium-based catalysts was evaluated in the selective oxidation of H2S to elemental sulfur. The catalytic data show that both the activity and the catalytic stability increase with the vanadium content, obtaining the highest conversion values and sulfur yield for the catalysts with vanadium content of 16 wt. %. The comparison among all supports reveals that the incorporation of TiO2 species in the pillars of the PCH improves the resistance to the deactivation, attaining as best results a H2S conversion of 89% for SiTi-PCH-16V catalyst and elemental sulfur is the only compound detected by gas chromatography.
Simplified Hierarchical Recurrent Encoder-Decoder for Building End-To-End Dialogue Systems

As a generative model for building end-to-end dialogue systems, Hierarchical Recurrent Encoder-Decoder (HRED) consists of three layers of Gated Recurrent Unit (GRU), which from bottom to top are separately used as the word-level encoder, the sentence-level encoder, and the decoder. Despite performing well on dialogue corpora, HRED is computationally expensive to train due to its complexity. To improve the training efficiency of HRED, we propose a new model, which is named as Simplified HRED (SHRED), by making each layer of HRED except the top one simpler than its upper layer. On the one hand, we propose Scalar Gated Unit (SGU), which is a simplified variant of GRU, and use it as the sentence-level encoder. On the other hand, we use Fixed-size Ordinally-Forgetting Encoding (FOFE), which has no trainable parameter at all, as the word-level encoder. The experimental results show that compared with HRED under the same word embedding size and the same hidden state size for each layer, SHRED reduces the number of trainable parameters by 25\%--35\%, and the training time by more than 50\%, but still achieves slightly better performance.