Miracle-dz/UniFAR-FTUs huggingface dataset:
Targeting unlesioned pharyngeal motor cortex improves swallowing in healthy individuals and after dysphagic stroke.

BACKGROUND & AIMS Patients with stroke experience swallowing problems (dysphagia); increased risk of aspiration pneumonia, malnutrition, and dehydration; and have increased mortality. We investigated the behavioral and neurophysiological effects of a new neurostimulation technique (paired associative stimulation [PAS]), applied to the pharyngeal motor cortex, on swallowing function in healthy individuals and patients with dysphagia from stroke. METHODS We examined the optimal parameters of PAS to promote plasticity by combining peripheral pharyngeal (electrical) with cortical stimulation. A virtual lesion was used as an experimental model of stroke, created with 1-Hz repetitive transcranial magnetic stimulation over the pharyngeal cortex in 12 healthy individuals. We tested whether hemispheric targeting of PAS altered swallowing performance before applying the technique to 6 patients with severe, chronic dysphagia from stroke (mean of 38.8 ± 24.4 weeks poststroke). RESULTS Ten minutes of PAS to the unlesioned pharyngeal cortex reversed (bilaterally) the cortical suppression induced by virtual lesion (lesioned: F(1,9) = 21.347, P = . 001; contralesional: F(1,9) = 9.648, P = . 013; repeated-measures analysis of variance) compared with sham PAS. It promoted changes in behavior responses measured with a swallowing reaction time task (F(1,7) = 21.02, P = . 003; repeated-measures analysis of variance). In patients with chronic dysphagia, real PAS induced short-term bilateral changes in the brain; the unaffected pharyngeal cortex had increased excitability (P = .001; 95% confidence interval, 0.21-0.05; post hoc paired t test) with reduced penetration-aspiration scores and changes in swallowing biomechanics determined by videofluoroscopy. CONCLUSIONS The beneficial neurophysiological and behavioral properties of PAS, when applied to unlesioned brain, provide the foundation for further investigation into the use of neurostimulation as a rehabilitative approach for patients with dysphagia from stroke.
Superstition and Financial Decision Making

In Chinese culture, certain digits are lucky and others unlucky. We test how such numerological superstition affects financial decision in the China IPO market. We find that the frequency of lucky numerical stock listing codes exceeds what would be expected by chance. Also consistent with superstition effects, newly listed firms with lucky listing codes experience inferior post-IPO abnormal returns. Further tests suggest that our conclusions are not driven by endogeneity.
SUMS-OF-SQUARES FORMULAS

The following is the extended version of my notes from my ATC talk given on June 4, 2014 at UCLA. I begin with a basic introduction to sums-of-squares formulas, and move on to giving motivation for studying these formulas and discussing some results about them over the reals. More recent techniques have made it possible to obtain similar results over arbitrary fields, and some of these are discussed later in the paper. I finish with some open questions about sums-of-squares formulas. All of the background comes from Daniel Shapiro’s book [1] and online notes [2, 3, 4], and the more recent results in section 7 come from the papers [5, 6, 7] by Daniel Dugger and Daniel Isaksen.
Sensing technologies for advanced smart agricultural systems

We introduce our sensing technologies to acquire agricultural information, such as image-based plant phenotyping, harvest quantity data, and localization information using a camera in a greenhouse. Commercial systems exist that support agriculture, but many unresolved issues remain regarding optimization of farming sustainability and productivity. Therefore, we intend to apply state-of-the-art information and communication technology (ICT) to tackle these agricultural issues and to investigate their limitations for developing advanced smart agricultural systems.
An architecture for QoS-aware service deployment in software-defined IoT networks

One of the major challenges in Internet of Things (IoT) will be management and orchestration of multi-technology/multi-vendor network environments. According to many stakeholders, the adoption of software defined networking (SDN) principles in 5G (5-th Generation) wireless networks will allow the integration of mobile networks and the internet of things and will make it possible to manage billions of connected smart objects through intelligent orchestration and provisioning systems. The special value of SDN lies its ability to support network virtualization and to automate deployment of new services on top of the virtualized network infrastructure. To pave the way towards this novel approach, we present a SDN-based architecture for virtualization of IoT networks, including an intent-based north-bound interface and a virtualized infrastructure manager, allowing virtualization of IoT resources. The architecture exploits SDN controller to program different IoT networks in order to provide to users the intended service at the requested level of quality. A prototype of the architecture is presented and numerical results related to round trip time, measured at the different levels of the architecture, are reported.
CheetahFlow: Towards low latency software-defined network

Software defined networking (SDN), which enables programmability, has the advantage of global visibility and high flexibility. However, when forwarding new flows in SDN, the interaction between the switch and the controller imposes extra latency such as round-trip time and routing path search time. Even though such latency is acceptable for elephant flows since it only takes limited ratio of total transmission time of elephant flows, it is an overkill to pay certain overheads for mice flows due to the their short transmission time. Moreover, the controller is frequently invoked by the mice flows since the number of mice flows accounts for a large portion of the total number of flows. Hence, the frequent controller invocation is mainly responsible for the controller performance degradation, and thus increasing the flow setup latency significantly. To solve this problem, we propose CheetahFlow, a novel scheme to predict frequent communication pairs via support vector machine and proactively setup wildcard rules to reduce flow setup latency. Particularly, in order to avoid congestion along a fixed path, elephant flows are detected and rerouted to the non-congestion path efficiently by applying blocking island paradigm. Extensive experiments show that CheetahFlow prominently reduces latency without any loss of flexibility of SDN.
Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs

We approach structured output prediction by optimizing a deep value network (DVN) to precisely estimate the task loss on different output configurations for a given input. Once the model is trained, we perform inference by gradient descent on the continuous relaxations of the output variables to find outputs with promising scores from the value network. When applied to image segmentation, the value network takes an image and a segmentation mask as inputs and predicts a scalar estimating the intersection over union between the input and ground truth masks. For multi-label classification, the DVN's objective is to correctly predict the F1 score for any potential label configuration. The DVN framework achieves the state-of-the-art results on multi-label prediction and image segmentation benchmarks.
Policy Considerations from a Nationwide IT-in-Education Initiative: Macedonia Connects

Though information technology (IT) is increasingly touted as an effective tool through which governments may enhance their development efforts, to date, many IT-related deployments have remained in a pilot stage, which does little to aid governments in understanding macro-level IT policy approaches to confronting development challenges. To address this shortcoming, this article presents a unique nationwide initiative: Macedonia Connects, a multipartner, scaled IT-for-education project wherein every school in the country was equipped with both computers and wireless Internet connections and through which the Internet was made available to citizens across the entire country. A number of policy-related aspects of the project are identified and discussed, including the predeployment training of teachers in IT adoption, the equality of technology and training provision across the entire country, the long-term focus on the part of all stakeholders in terms of outcomes, and the business model employed in the public–private partnership. Although it is too early to discuss long-term outcomes, there are many aspects of this case that will be applicable to governments and practitioners alike in future IT-for-development endeavors. Some of Macedonia's neighboring states have already indicated interest in following this model.
Parameter-free hierarchical image segmentation

Images typically have many levels of detail and the suitability of a segmentation depends on application requirements. Thus, it is imperative that the user/application be given the option to select the ‘optimal’ segmentation that captures the desired level of detail from a set of segmentations. This paper presents a hierarchical image segmentation algorithm that offers this option using the concept of minimum spanning trees. It converts an input image into a tree structure from which a hierarchy of segmentations is obtained through a process of merging. No parameters are used in this process and thus the proposed algorithm can be used on any segmentation dataset as is. The levels are calculated in one pass of the minimum spanning tree and as such, no iterative merging is required. This provides the user with a quick way of segment visualisation. Evaluation results on two popular segmentation datasets show that the algorithm provides competitive results in comparison to other segmentation algorithms.
Prioritizing Road Maintenance Activities using GIS Platform and Vb.net

One of the most important factors for the sustainable development of any country is the quality and efficiency of its transportation system. The principled and accurate maintenance of roads, in addition to having a major impact on budget savings, improves the quality and service levels of the transportation system. For this reason, road management and maintenance are the main pillars of the transportation system in any country. Nowadays, due to the increased cost of maintaining roads and the lack of funding in this area, traditional ways of managing and maintaining roads, which are more based on the experience of the experts themselves, are no longer affordable. Hence, more recent, and more systematic methods have become more popular among relevant authorities. Afghanistan is a country facing problems such as budget deficits, lack of professional experts and advanced technology in road maintenance sector. This paper presents an example of using the GIS platform and vb.net to prioritize the road maintenance and rehabilitation activities based on identified criteria. A case study conducted in an academic environment and road maintenance and rehabilitation activities prioritized. The results show that the positive criterion has the greatest impact on the ranking of road maintenance activities. The characteristic of this process is to help the decision makers to plan road maintenance requirements to effectively and efficiently allocate funds for future planning.
Genome Sequence of a Byssochlamys sp. Strain Isolated from Fouled B20 Biodiesel

ABSTRACT Byssochlamys sp. strain AF001 is a filamentous fungus isolated from fouled B20 biodiesel. Its growth on B20 biodiesel results in the degradation and fouling of the fuel and higher rates of corrosion in affected storage tanks. The genome of Byssochlamys sp. AF001 is 35.9 Mbp and is composed of 10 scaffolds, with a G+C content of 45.89%.
LIFE-HISTORY AND ECOLOGICAL CORRELATES OF GEOGRAPHIC VARIATION IN EGG AND CLUTCH MASS AMONG PASSERINE SPECIES

Abstract Broad geographic patterns in egg and clutch mass are poorly described, and potential causes of variation remain largely unexamined. We describe interspecific variation in avian egg and clutch mass within and among diverse geographic regions and explore hypotheses related to allometry, clutch size, nest predation, adult mortality, and parental care as correlates and possible explanations of variation. We studied 74 species of Passeriformes at four latitudes on three continents: the north temperate United States, tropical Venezuela, subtropical Argentina, and south temperate South Africa. Egg and clutch mass increased with adult body mass in all locations, but differed among locations for the same body mass, demonstrating that egg and clutch mass have evolved to some extent independent of body mass among regions. A major portion of egg mass variation was explained by an inverse relationship with clutch size within and among regions, as predicted by life-history theory. However, clutch size did not explain all geographic differences in egg mass; eggs were smallest in South Africa despite small clutch sizes. These small eggs might be explained by high nest predation rates in South Africa; life-history theory predicts reduced reproductive effort under high risk of offspring mortality. This prediction was supported for clutch mass, which was inversely related to nest predation but not for egg mass. Nevertheless, clutch mass variation was not fully explained by nest predation, possibly reflecting interacting effects of adult mortality. Tests of the possible effects of nest predation on egg mass were compromised by limited power and by counterposing direct and indirect effects. Finally, components of parental investment, defined as effort per offspring, might be expected to positively coevolve. Indeed, egg mass, but not clutch mass, was greater in species that shared incubation by males and females compared with species in which only females incubate eggs. However, egg and clutch mass were not related to effort of parental care as measured by incubation attentiveness. Ecological and life-history correlates of egg and clutch mass variation found here follow from theory, but possible evolutionary causes deserve further study.
P-selectin is a nanotherapeutic delivery target in the tumor microenvironment

P-selectin–targeted nanoparticles deliver chemotherapy to tumors, particularly when combined with radiation. Special delivery P-selectin is a molecule expressed on active blood vessels, such as those found in tumors. To take advantage of this observation, Shamay et al. designed nanoparticles containing fucoidan, a polysaccharide that specifically binds to P-selectin, and demonstrated that these can deliver a variety of chemotherapeutic drugs to tumors. In addition, the authors demonstrated that radiation treatment stimulates expression of P-selectin in tumors that do not normally express it, successfully expanding the range of tumor types that can be targeted by fucoidan nanoparticle treatment. Disseminated tumors are poorly accessible to nanoscale drug delivery systems because of the vascular barrier, which attenuates extravasation at the tumor site. We investigated P-selectin, a molecule expressed on activated vasculature that facilitates metastasis by arresting tumor cells at the endothelium, for its potential to target metastases by arresting nanomedicines at the tumor endothelium. We found that P-selectin is expressed on cancer cells in many human tumors. To develop a targeted drug delivery platform, we used a fucosylated polysaccharide with nanomolar affinity to P-selectin. The nanoparticles targeted the tumor microenvironment to localize chemotherapeutics and a targeted MEK (mitogen-activated protein kinase kinase) inhibitor at tumor sites in both primary and metastatic models, resulting in superior antitumor efficacy. In tumors devoid of P-selectin, we found that ionizing radiation guided the nanoparticles to the disease site by inducing P-selectin expression. Radiation concomitantly produced an abscopal-like phenomenon wherein P-selectin appeared in unirradiated tumor vasculature, suggesting a potential strategy to target disparate drug classes to almost any tumor.
SIW slot antenna at Ka-band for soil moisture radiometer system

This paper presents the design of a compact SIW slot antenna at Ka-band (37 GHz) for Polarimetrie Radiometer System (PMR) for soil moisture measurement. The designed antenna is fabricated and the measured results are compared with the simulation. High Q-factor and high gain are achieved with the proposed design. Different slot excitations and the impact on slot length and gain have been discussed. Complete phased array antenna system model have been developed and beam scanning from −20° to 20° has performed. The proposed antenna shows robust performance which makes this design to be suitable for such radiometer application.
A new spherical hashing method in a low-dimensional isotropic space

By using a hypersphere to group the spatially coherent data points into the same bit, spherical hashing (SPH) can achieve a good performance in approximate nearest neighbor (ANN) search. However, when the data dimensionality rises, the data becomes sparse and the hypersphere needs to increase its radius to maintain the same coverage, which makes the data points in the hypersphere less coherent. To alleviate the effect brought from the high dimensionality of the data, a new hypersphere-based hashing method is proposed. By constructing a low-dimensional isotropic space where the variance of projection along each component is equal, both the similarity and the distribution of the original data can be preserved in this space. And then, the hashing functions are learnt by SPH in this space. The experiments on SIFT1M and GIST1M datasets show that the performance of SPH can be improved by our method and is superior to other state-of-the-art hashing methods in terms of recall and mAP performance.
Skill-Biased Labor Market Reforms and International Competitiveness

Abstract This paper proposes a multi-industry trade model with integrated capital and goods markets. Labor market imperfections in line with Mortensen and Pissarides (Job Creation and Job Destruction in the Theory of Unemployment, 1994) give rise to unemployment and a channel for the government to influence markets through institutional changes. Labor market interventions feedback into the product market through changes in a country’s competitiveness. Moreover, the distinction between high- and low-skill workers facilitates the analysis of skillbiased institutional changes that have stronger impact on certain skill groups. The comparative static exercise in this paper shows that high-skilled benefit from low-skill biased labor market reforms through higher wages. Lower labor costs reduce unemployment of the low-skilled and increases the reforming country’s competitiveness. One-sided labor market interventions have feedback effects through adjustments at the extensive margin, which affect all workers at home and abroad irrespective of their level of skill. Governments in the non-reforming countries may react to this loss in competitiveness by initiating cooperative labor market reforms instead.
Synergistic effects of fishing-induced demographic changes and climate variation on fish population dynamics

The synergistic effects of fishing, climate and internal dynamics on population fluctuations are poorly understood due to the complexity of these interac- tions. In this paper, we combine time series analysis and simulations to investigate the long-term dynamics of an overexploited population in the Mediterranean Sea, and its link with both fishing-induced demographic changes and hydroclimatic variability. We show that the cyclicity of the catch per unit of effort (CPUE) of Euro- pean hake Merluccius merluccius (EH) vanished in the 1980s, while the correlation between the CPUE and a local environmental index increased. Using simulations, we then show that the cyclicity observed in the EH bio- mass before the 1980s can have an internal origin, while that its disappearance could be due to the fishing- induced erosion of the age structure. Our results suggest that fishing can trigger a switch from internally gen- erated to externally forced population fluctuations, the latter being characterised by an increasing dependency of the population on recruitment and ultimately on envi- ronmental variability. Hydroclimatic modifications oc- curring in the Mediterranean in the early 1980s could have enhanced these changes by leading to a mismatch between early life stages of EH and favorable environ- mental conditions. Our conclusions underline the key effect of the interaction between exploitation and climate on the dynamics of EH and its important consequences for management and conservation.
A convolutional attentional neural network for sentiment classification

Neural network models with attention mechanism have shown their efficiencies on various tasks. However, there is little research work on attention mechanism for text classification and existing attention model for text classification lacks of cognitive intuition and mathematical explanation. In this paper, we propose a new architecture of neural network based on the attention model for text classification. In particular, we show that the convolutional neural network (CNN) is a reasonable model for extracting attentions from text sequences in mathematics. We then propose a novel attention model base on CNN and introduce a new network architecture which combines recurrent neural network with our CNN-based attention model. Experimental results on five datasets show that our proposed models can accurately capture the salient parts of sentences to improve the performance of text classification.
A new underwater image enhancing method via color correction and illumination adjustment

Since the light will be absorbed and scattered when travels in water, underwater imaging exists three major difficulties, including color cast, under-exposure, and fuzz. The solutions to overcome those issues are important for the exploration of the ocean. In this paper, we propose a new algorithm for improving the quality of underwater images. The algorithm is composed of two components: color correction and illumination adjustment. First, we use an efficient color enhancement method to solve the color cast. Then, based on Retinex model, we make the illumination adjustment, mainly extracting the illumination map and implementing gamma correction on it successively. Experimental results show that visual performance of our method outperforms that of other methods, and processing complexity is relatively simpler.