Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Source-Target Self Attention with Locality Constraints

The dominant neural machine translation models are based on the encoder-decoder structure, and many of them rely on an unconstrained receptive field over source and target sequences. In this paper we study a new architecture that breaks with both conventions. Our simplified architecture consists in the decoder part of a transformer model, based on self-attention, but with locality constraints applied on the attention receptive field. As input for training, both source and target sentences are fed to the network, which is trained as a language model. At inference time, the target tokens are predicted autoregressively starting with the source sequence as previous tokens. The proposed model achieves a new state of the art of 35.7 BLEU on IWSLT'14 German-English and matches the best reported results in the literature on the WMT'14 English-German and WMT'14 English-French translation benchmarks.
A Simple Dual-decoder Model for Generating Response with Sentiment

How to generate human like response is one of the most challenging tasks for artificial intelligence. In a real application, after reading the same post different people might write responses with positive or negative sentiment according to their own experiences and attitudes. To simulate this procedure, we propose a simple but effective dual-decoder model to generate response with a particular sentiment, by connecting two sentiment decoders to one encoder. To support this model training, we construct a new conversation dataset with the form of (post, resp1, resp2) where two responses contain opposite sentiment. Experiment results show that our dual-decoder model can generate diverse responses with target sentiment, which obtains significant performance gain in sentiment accuracy and word diversity over the traditional single-decoder model. We will make our data and code publicly available for further study.
Latent Variable Model for Multi-modal Translation

In this work, we propose to model the interaction between visual and textual features for multi-modal neural machine translation (MMT) through a latent variable model. This latent variable can be seen as a multi-modal stochastic embedding of an image and its description in a foreign language. It is used in a target-language decoder and also to predict image features. Importantly, our model formulation utilises visual and textual inputs during training but does not require that images be available at test time. We show that our latent variable MMT formulation improves considerably over strong baselines, including a multi-task learning approach (Elliott and Kadar, 2017) and a conditional variational auto-encoder approach (Toyama et al., 2016). Finally, we show improvements due to (i) predicting image features in addition to only conditioning on them, (ii) imposing a constraint on the KL term to promote models with non-negligible mutual information between inputs and latent variable, and (iii) by training on additional target-language image descriptions (i.e. synthetic data).
Automatic identification of qualitatives characteristics in infant cry

In infant cry analysis it is of great importance to identify the qualitative characteristics present in the cry wave, this is because they provide additional information that allows recognizing variations and similarities between normal and pathological cries. Nowadays the qualitative characteristics analysis is manually done by using visual perception (inspecting spectrograms) and by auditory perception (listening cry recordings). This perceptive analysis is the one expert physicians apply to help them make a diagnosis. In this work we present a method based in the definition of a threshold applied to the energy of the signal, this threshold allows to identify automatically cry units in a sample recording and another threshold that allows eliminating inspiratory cry segments. We also present a method that allows automatically identifying the melodic shape, shifts, glides and noise concentrations in cry units. The whole process implementation as well as some experiments and results are here presented.
Response gene to complement-32 mediates TGF-β-induced epithelial-mesenchymal transition via Erk-/ p 38-MAPK signaling pathways in human pancreatic cancer cell line BxPC-3

Pancreatic cancer (PC) is a highly life-destructive human digestive system carcinoma, the five-year survival of which is less than 6%. The PC progression was believed to be associated with epithelial-mesenchymal transition (EMT) which could be stimulated by TGF-β. We previously found that response gene to complement-32 (RGC-32) mediated TGF-β-induced EMT. However, the underlying mechanism remained unclear. In the present study, BxPC3 cells were used and treated with 10 ng/mL TGF-β1 alone or in combination with 10 μM U0126/SB203580/ LY294002. The mRNA and protein expressions of RGC-32, E-cadherin and vimentin were analyzed by qRT-PCR and Western blot. The siRGC-32 was introduced to measure the expression of Snail and Zeb1 in BxPC-3 cells treated with or without TGF-β1. The results showed that U0126/SB203580 combined with TGF-β1 inhibited EMT development, decreased the mRNA and protein expressions of RGC-32 and vimentin, and increased those of E-cadherin compared with the cells treated with TGF-β1 alone. After combined use of siRGC-32 and TGF-β1, the EMT occurrence and the levels of RGC-32, snail and Zeb1 were all inhibited compared with the cells treated with siCtrl alone or siCtrl + TGF-β1. The present study demonstrated that the regulation of RGC-32 on TGF-β-induced EMT might be associated with non-Smad pathways, and Erk-MAPK and p38-MAPK pathways seemed to play more important roles than PI3K-Akt pathway in PC progression.
Fetal programming in 2-year-old calving heifers: peri-conception and first trimester protein restriction alters fetal growth in a gender-specific manner

Protein restriction in early bovine gestation affects post-natal reproduction and production traits in progeny. This experiment evaluated the effects of dietary protein restriction during the peri-conception period and first trimester in yearling heifers on conceptus growth and development; this period of dietary intervention being earlier than any previous bovine fetal programming studies. Three-hundred and sixty primiparous 12-month-old Santa Gertrudis heifers were individually fed high [14% crude protein (CP)] or low (7% CP) diets for 60 days before conception. At 23 days post-conception (dpc), each high (HPERI) or low (LPERI) group was again split into high (HPOST) or low (LPOST) protein groups yielding four treatment groups in a 2 × 2 factorial design. From the end of the first trimester of gestation (98dpc), the pregnant heifers were individually fed a 12% CP diet until parturition. Forty-six fetuses were excised at 98dpc. Sixty-four heifers went on to calve. Conceptus development was assessed via transrectal ultrasound from 36dpc, fetal necropsy at 98dpc and live calf measures at term. At 36dpc, HPERI diet increased fetal crown–rump length (CRL) (P 0.1). At term, no dietary effect on birthweight was observed (P > 0.1) and males were not heavier than females (P > 0.1). These results suggest that maternal protein intake during the peri-conception (–60 to 23dpc) and first trimester (24–98dpc) may influence early conceptus growth and development in the bovine. The long-term effects on offspring metabolism and post-natal development of this dietary intervention are yet to be determined.
Evaluation of MRSA Colonization Epidemiology before and after Practical Training in Hospitals and Healthcare Settings among Health Profession Students in Hong Kong

Staphylococcus aureus has been a global threat to the medical and healthcare community as it is notorious for causing nosocomial infections which can result in serious consequences such as pneumonia and bacteremia. S. Aureus infections can also put heavy pressure on the economy of the society because of the extra resources needed on the preventing the widespread of the bacteria and providing medical treatments for infected individuals. This burden will be doubled should the bacteria become resistant to antibiotics such as methicillin and vancomycin. As medical and healthcare workers can be colonized by the bacteria during their duty hours in the hospital and thus carrying HA-MRSA, they can also spread the bacteria into the community after their working hours, causing CA-MRSA. Therefore, it is important to know the MRSA carriage rate of this medical and healthcare professional in order to prevent a widespread of the bacteria. Besides hospital workers, students going to hospitals for their clinical training can also be colonized by the bacteria during their time of stay. This study investigates the MRSA colonization rate in the nasal cavities of MLS students before and after their clinical training in various hospitals. A prevalence study is also conducted among MLS and nursing students to find out their nasal MRSA carriage rate. It is found that MLS students generally have a higher nasal MRSA carriage rate than that of nursing students whether they have been to clinical training or not. Moreover, the MRSA colonization rate was relatively higher among MLS students before they go to clinical practicum. Despite it is shown that association is statistically insignificant between pre-exposure and post-exposure to the clinical environment, clinical exposure is still a major factor in affecting the MRSA colonization rate and a larger sample size can be recruited when further studies are done. In order to distinguish between HA-MRSA and CA-MRSA among MRSA isolates, questionnaires can be distributed to subjects and molecular methods such as ribotyping can be employed.
Molecular Characterization of a Putative K-Cl Cotransporter in Rat Brain

Using a combination of data base searching, polymerase chain reaction, and library screening, we have identified a putative K-Cl cotransporter isoform (KCC2) in rat brain that is specifically localized in neurons. A cDNA of 5566 bases was obtained from overlapping clones and encoded a protein of 1116 amino acids with a deduced molecular mass of 123.6 kDa. Over its full length, the amino acid sequence of KCC2 is 67% identical to the widely distributed K-Cl cotransporter isoform (KCC1) identified in rat brain and rabbit kidney (Gillen, C., Brill, S., Payne, J.A., and Forbush, B., III (1996) J. Biol. Chem. 271, 16237-16244) but only ∼25% identical to other members of the cation-chloride cotransporter gene family, including “loop” diuretic-sensitive Na-K-Cl cotransport and thiazide-sensitive Na-Cl cotransport. Based on analysis of the primary structure as well as homology with other cation-chloride cotransporters, we predict 12 transmembrane segments bounded by N- and C-terminal cytoplasmic regions. Four sites for N-linked glycosylation are predicted on an extracellular intermembrane loop between putative transmembrane segments 5 and 6. Northern blot analysis using a KCC2-specific cDNA probe revealed a very highly expressed ∼5.6-kilobase transcript only in brain. Reverse transcriptase-polymerase chain reaction revealed that KCC1 was present in rat primary astrocytes and rat C6 glioma cells but that KCC2 was completely absent from these cells, suggesting KCC2 was not of glial cell origin. In situ hybridization studies demonstrated that the KCC2 transcript was expressed at high levels in neurons throughout the central nervous system, including CA1-CA4 pyramidal neurons of the hippocampus, granular cells and Purkinje neurons of the cerebellum, and many groups of neurons throughout the brainstem.
Universal Trimers from Three-Body Interactions in One-Dimensional Lattices

We investigate the formation of trimers in an infinite one-dimensional lattice model with single-particle hopping $t$ and hard-core two-body $U$ and three-body $V$ interactions of relevance to Rydberg atoms and polar molecules. For sufficiently attractive $U \leq- 2t$ and positive $V>0$ a large trimer is stabilized, which persists as $V\rightarrow \infty$, while both attractive $U \leq 0$ and $V \leq 0$ bind a small trimer. Surprisingly, the excited state above this small trimer is also bound and has a large extent; its behavior as $V\rightarrow -\infty$ resembles that of the large ground-state trimer.
Application of Infrared and Raman Spectroscopy in Paint Trace Examination

Raman spectroscopy has proved to be a promising technique in forensic examinations, where optical microscopy, micro‐infrared spectroscopy, and microspectrophotometry in the visible and UV range are used for identification and differentiation between paint traces. Often no organic pigments are detected using Fourier transform infrared spectroscopy, because their content in a trace is usually undetectable. Application of a micro‐Raman spectrometer equipped with several excitation lasers helps forensic experts in paint analysis enabling identification of main pigments. Three cases concerning comparative examination of car paint are discussed in detail. The comparison of Raman spectra of paint chips found on clothing of a victim or smears found on body of a damaged car to these of paint chips originated from the suspected car enabled us to identify the car involved in the accident. When no comparative material is available, the method can be useful in establishing the color and make of the car.
Leverage eye-movement data for saliency modeling: Invariance Analysis and a Robust New Model

Data size is the bottleneck for developing deep saliency models, because collecting eye-movement data is very time consuming and expensive. Most of current studies on human attention and saliency modeling have used high quality stereotype stimuli. In real world, however, captured images undergo various types of transformations. Can we use these transformations to augment existing saliency datasets? Here, we first create a novel saliency dataset including fixations of 10 observers over 1900 images degraded by 19 types of transformations. Second, by analyzing eye movements, we find that observers look at different locations over transformed versus original images. Third, we utilize the new data over transformed images, called data augmentation transformation (DAT), to train deep saliency models. We find that label preserving DATs with negligible impact on human gaze boost saliency prediction, whereas some other DATs that severely impact human gaze degrade the performance. These label preserving valid augmentation transformations provide a solution to enlarge existing saliency datasets. Finally, we introduce a novel saliency model based on generative adversarial network (dubbed GazeGAN). A modified UNet is proposed as the generator of the GazeGAN, which combines classic skip connections with a novel center-surround connection (CSC), in order to leverage multi level features. We also propose a histogram loss based on Alternative Chi Square Distance (ACS HistLoss) to refine the saliency map in terms of luminance distribution. Extensive experiments and comparisons over 3 datasets indicate that GazeGAN achieves the best performance in terms of popular saliency evaluation metrics, and is more robust to various perturbations. Our code and data are available at: this https URL.
Exercise Training and Functional Connectivity Changes in Mild Cognitive Impairment and Healthy Elders.

BACKGROUND Effective interventions are needed to improve brain function in mild cognitive impairment (MCI), an early stage of Alzheimer's disease (AD). The posterior cingulate cortex (PCC)/precuneus is a hub of the default mode network (DMN) and is preferentially vulnerable to disruption of functional connectivity in MCI and AD. OBJECTIVE We investigated whether 12 weeks of aerobic exercise could enhance functional connectivity of the PCC/precuneus in MCI and healthy elders. METHODS Sixteen MCI and 16 healthy elders (age range = 60-88) engaged in a supervised 12-week walking exercise intervention. Functional MRI was acquired at rest; the PCC/precuneus was used as a seed for correlated brain activity maps. RESULTS A linear mixed effects model revealed a significant interaction in the right parietal lobe: the MCI group showed increased connectivity while the healthy elders showed decreased connectivity. In addition, both groups showed increased connectivity with the left postcentral gyrus. Comparing pre to post intervention changes within each group, the MCI group showed increased connectivity in 10 regions spanning frontal, parietal, temporal and insular lobes, and the cerebellum. Healthy elders did not demonstrate any significant connectivity changes. CONCLUSION The observed results show increased functional connectivity of the PCC/precuneus in individuals with MCI after 12 weeks of moderate intensity walking exercise training. The protective effects of exercise training on cognition may be realized through the enhancement of neural recruitment mechanisms, which may possibly increase cognitive reserve. Whether these effects of exercise training may delay further cognitive decline in patients diagnosed with MCI remains to be demonstrated.
Practical figures of merit and thresholds for entanglement distribution in quantum networks

Before global-scale quantum networks become operational, it is important to consider how to evaluate their performance so that they can be suitably built to achieve the desired performance. In this work, we consider three figures of merit for the performance of a quantum network: the average global connection time, the average point-to-point connection time, and the average largest entanglement cluster size. These three quantities are based on the generation of elementary links in a quantum network, which is a crucial initial requirement that must be met before any long-range entanglement distribution can be achieved. We evaluate these figures of merit for a particular class of quantum repeater protocols consisting of repeat-until-success elementary link generation along with entanglement swapping at intermediate nodes in order to achieve long-range entanglement. We obtain lower and upper bounds on these three quantities, which lead to requirements on quantum memory coherence times and other aspects of quantum network implementations. Our bounds are based solely on the inherently probabilistic nature of elementary link generation in quantum networks, and they apply to networks with arbitrary topology.
Robust Real-time Pedestrian Detection in Aerial Imagery on Jetson TX2

Detection of pedestrians in aerial imagery captured by drones has many applications including intersection monitoring, patrolling, and surveillance, to name a few. However, the problem is involved due to continuouslychanging camera viewpoint and object appearance as well as the need for lightweight algorithms to run on on-board embedded systems. To address this issue, the paper proposes a framework for pedestrian detection in videos based on the YOLO object detection network [6] while having a high throughput of more than 5 FPS on the Jetson TX2 embedded board. The framework exploits deep learning for robust operation and uses a pre-trained model without the need for any additional training which makes it flexible to apply on different setups with minimum amount of tuning. The method achieves ~81 mAP when applied on a sample video from the Embedded Real-Time Inference (ERTI) Challenge where pedestrians are monitored by a UAV.
Persistent Buffer Management with Optimistic Consistency

Finding the best way to leverage non-volatile memory (NVM) on modern database systems is still an open problem. The answer is far from trivial since the clear boundary between memory and storage present in most systems seems to be incompatible with the intrinsic memory-storage duality of NVM. Rather than treating NVM either solely as memory or solely as storage, in this work we propose how NVM can be simultaneously used as both in the context of modern database systems. We design a persistent buffer pool on NVM, enabling pages to be directly read/written by the CPU (like memory) while recovering corrupted pages after a failure (like storage). The main benefits of our approach are an easy integration in the existing database architectures, reduced costs (by replacing DRAM with NVM), and faster peak-performance recovery.
Exploration of Segmented Bus As Scalable Global Interconnect for Neuromorphic Computing

Spiking Neural Networks (SNNs) are efficient computation models for spatio-temporal pattern recognition on resource and power constrained platforms. Dedicated SNN hardware, also called neuromorphic hardware, can further reduce the energy consumption of these platforms. A neuromorphic hardware consists of crossbars, which are arrangements of input and output neurons with fully-connected synapses. Time-multiplexed interconnects are used to communicate spikes between crossbars. When a SNN model is mapped on multiple crossbars, the time-multiplexed interconnect increases spike latency and energy consumption, and disorders spike arrivals at output neurons, which reduces application accuracy. In this paper, we propose segmented bus interconnect for global synapses in a neuromorphic architecture. The objective is to reduce power consumption and enable parallel processing compared to traditional time-multiplexed interconnects. The fundamental idea for the segmented bus is to partition a single bus into several segments, with the segmentation switches controlled by software. We evaluate the scalability of segmented bus using synthetic applications. Our results show that segmented bus reduces the latency and energy consumption of the global synapse network significantly with respect to state-of-the-art techniques.
A Skyrmion Racetrack Memory based Computing In-memory Architecture for Binary Neural Convolutional Network

A Skyrmion Racetrack Memory (SRM) based Computing In-Memory Architecture (SRM-CIM) was proposed in this paper. Both data and computing operation can be achieved in SRM-CIM. SRM-CIM is used to support convolutional computing in Binary Convolutional Neural Network (BCNN). Experimental results show that SRM-CIM achieves 98.7% and 82% energy reduction when compared with RRAM and SOT-MRAM based counterparts.
An Overview of Thermal Challenges and Opportunities for Monolithic 3D ICs

Monolithic 3D (Mono3D) is a three-dimensional integration technology that can overcome some of the fundamental limitations faced by traditional, two-dimensional scaling. This paper analyzes the unique thermal characteristics of Mono3D ICs by simulating a two-tier flip-chip Mono3D IC and highlights the primary differences in comparison to a similarly-sized flip-chip TSV-based 3D IC. Specifically, we perform architectural-level thermal simulations for both technologies and demonstrate that vertical thermal coupling is stronger in Mono3D ICs, leading to lower upper tier temperatures. We also investigate the significance of lateral versus vertical flow of heat in Mono3D ICs. We simulate different hot spot scenarios in a two-tier Mono3D IC and show that although the lateral heat flow is limited as compared to TSV-based 3D ICs, ignoring this mechanism can cause nonnegligible error (~4°C) in temperature estimation, particularly for layers farther from the heat sink. In addition, we show that with increasing interconnect utilization (due to the contribution of Joule heating to overall temperature), the on-chip temperatures and the significance of lateral heat flow within the two-tier Mono3D IC also increase. Finally, we discuss potential opportunities in Mono3D ICs to enhance their thermal integrity.