Miracle-dz/UniFAR-FTUs huggingface dataset:
A Model of Multisensory Integration and Its Influence on Hippocampal Spatial Cell Responses

Head direction (HD) cells, grid cells, and place cells, often dubbed spatial cells, are neural correlates of spatial navigation. We propose a computational model to study the influence of multisensory modalities, especially vision, and proprioception on responses of these cells. A virtual animal was made to navigate within a square box along a synthetic trajectory. Visual information was obtained via a cue card placed at a specific location in the environment, while proprioceptive information was derived from curvature-modulated limb oscillations associated with the gait of the virtual animal. A self-organizing layer was used to encode HD information from both sensory streams. The sensory integration (SI) of HD from both modalities was performed using a continuous attractor network with local connectivity, followed by oscillatory path integration and lateral anti-Hebbian network, where spatial cell responses were observed. The model captured experimental findings which investigated the role of visual manipulation (cue card removal and cue card rotation) on these spatial cells. The model showed a more stable formation of spatial representations via the visual pathway compared to the proprioceptive pathway, emphasizing the role of visual input as an anchor for HD, grid, and place responses. The model suggests the need for SI at the HD level for formation of such stable representations of space essential for effective navigation.
FEMCRA: Fine-Grained Elasticity Measurement for Cloud Resources Allocation

It is indispensable for a cloud platform to provide flexible elasticity service. However, the cloud users do not know whether elastic resource allocation of cloud platform matches their resource requirement or not, and the inappropriate purchase plan will have an impact on effect of elasticity service. Thus, a fine-grained and suitable purchase plan of cloud resources is inevitably needed. In this paper, we propose a fine-grained elasticity measurement method for cloud resources allocation towards upper-level cloud applications, called FEMCRA. It is a feasible measurement method from the perspective of testing cloud applications in advance to find a fine-grained and suitable elastic resource allocation scheme. We construct an integrated environment for simulating various elasticity level scenarios based on OpenStack, and data analysis application from CloudSuite is deployed to act as applications under testing. By executing that application under different elasticity rule-sets, which is a group of testing strategies that makes the cloud platform to be automatically scaled, we get the optimal elasticity level with least quantity of cloud resource, and the best purchase plan is correspondingly obtained which could save the expense for cloud users.
A Privacy-Preserving Voting Protocol on Blockchain

As blockchain technologies mature and ecosystems over blockchain evolve, peers on blockchain networks often face situations in which they need to conduct voting for decision-making; as happened in the case of the DAO hard fork event on Ethereum. However, a natively built-in voting mechanism is not available on any of the existing blockchain platforms. Thus, the decision making either is delegated to a few network members who make such decisions offline or is dependent on third party online voting services. In both cases, peers directly or indirectly rely on trusted parties or centralized systems. This is against the basic decentralization principle of blockchain and exposes the election to frauds. To facilitate decision-making in a decentralized and secure manner, we propose a native blockchain voting protocol for peers to vote over their existing blockchain network without the need of any trusted or third party. Our protocol preserves end-to-end privacy and possesses desirable properties such as detectability and correctability against cheating. A reference implementation of our protocol on Hyperledger Fabric that demonstrates the validity and practical applicability of our protocol is also provided.
Cellular hysteresis as a principle to maximize the efficacy of antibiotic therapy

Significance Rapid evolution is central to the current antibiotic crisis. Sustainable treatments must thus take account of the bacteria’s potential for adaptation. We identified cellular hysteresis as a principle to constrain bacterial evolution. Cellular hysteresis is a persistent change in bacterial physiology, reminiscent of cellular memory, which is induced by one antibiotic and enhances susceptibility toward another antibiotic. Cellular hysteresis increases bacterial extinction in fast sequential treatments and reduces selection of resistance by favoring responses specific to the induced physiological effects. Fast changes between antibiotics are key, because they create the continuously high selection conditions that are difficult to counter by bacteria. Our study highlights how an understanding of evolutionary processes can help to outsmart human pathogens. Antibiotic resistance has become one of the most dramatic threats to global health. While novel treatment options are urgently required, most attempts focus on finding new antibiotic substances. However, their development is costly, and their efficacy is often compromised within short time periods due to the enormous potential of microorganisms for rapid adaptation. Here, we developed a strategy that uses the currently available antibiotics. Our strategy exploits cellular hysteresis, which is the long-lasting, transgenerational change in cellular physiology that is induced by one antibiotic and sensitizes bacteria to another subsequently administered antibiotic. Using evolution experiments, mathematical modeling, genomics, and functional genetic analysis, we demonstrate that sequential treatment protocols with high levels of cellular hysteresis constrain the evolving bacteria by (i) increasing extinction frequencies, (ii) reducing adaptation rates, and (iii) limiting emergence of multidrug resistance. Cellular hysteresis is most effective in fast sequential protocols, in which antibiotics are changed within 12 h or 24 h, in contrast to the less frequent changes in cycling protocols commonly implemented in hospitals. We found that cellular hysteresis imposes specific selective pressure on the bacteria that disfavors resistance mutations. Instead, if bacterial populations survive, hysteresis is countered in two distinct ways, either through a process related to antibiotic tolerance or a mechanism controlled by the previously uncharacterized two-component regulator CpxS. We conclude that cellular hysteresis can be harnessed to optimize antibiotic therapy, to achieve both enhanced bacterial elimination and reduced resistance evolution.
Modulation of Oxidative Stress and Hyperglycemia by Rheum spiciformis in Alloxan Induced Diabetic Rats and Characterization of Isolated Compound

Abstract This study evaluates the ameliorative potential of Rheum spiciformis methanolic (RS-MeOH) extract in reducing oxidative stress and hyperglycemia in albino rats along with characterization of possible therapeutic compound(s). Groups treated with 50 and 100 mg/kg bw plant extract (RS-MeOH ) decrease blood glucose levels from 359.9±8.2 to 209.5±8.5 mg/dl (50 mg/kg bw) and 354.7±13.3 to 162.5±7.4 mg/dl (100 mg/kg bw) on the 0th and 14th day (P<0.001) respectively. This reduction in blood glucose was significant as compared to glibenclamide (20 mg/dl) which reduced glucose levels from 297.7±11.39 to 132.9±8.74 mg/dl on 0th and 14th day respectively. Biochemical parameters triglycerdies, cholesterol, low density lipoprotein (LDL) and creatinine were also reduced in a dose dependent manner. Liver marker enzymes were positively modulated by administration of RS-MeOH (P<0.001). Antioxidant enzyme profile showed an enhanced/better pattern after the administration of RS-MeOH extracts for reduced glutathione, reduced glutathione (GR), glutathione peroxidase (GPx), glutathione–S-transferase (GST) and superoxide dismutase (SOD) in both liver and pancreas. Moreover pancreatic histopathology reports revealed β-cell restorative effects with RS-MeOH, thereby potentiating its role in improving blood glucose levels. RS-MeOH purification and isolation studies involving GC-MS and NMR techniques revealed presence of emodin type compounds in RS-MeOH. Overall Rheum spiciformis showed ameliorative action on oxidative stress and hyperglycemia, however further studies to explore the mechanism of action of possible therapeutic compound in invivo clinical trials will prove beneficial for the advancement of new oral antidiabetic drug.
Closed-Book Training to Improve Summarization Encoder Memory

A good neural sequence-to-sequence summarization model should have a strong encoder that can distill and memorize the important information from long input texts so that the decoder can generate salient summaries based on the encoder’s memory. In this paper, we aim to improve the memorization capabilities of the encoder of a pointer-generator model by adding an additional ‘closed-book’ decoder without attention and pointer mechanisms. Such a decoder forces the encoder to be more selective in the information encoded in its memory state because the decoder can’t rely on the extra information provided by the attention and possibly copy modules, and hence improves the entire model. On the CNN/Daily Mail dataset, our 2-decoder model outperforms the baseline significantly in terms of ROUGE and METEOR metrics, for both cross-entropy and reinforced setups (and on human evaluation). Moreover, our model also achieves higher scores in a test-only DUC-2002 generalizability setup. We further present a memory ability test, two saliency metrics, as well as several sanity-check ablations (based on fixed-encoder, gradient-flow cut, and model capacity) to prove that the encoder of our 2-decoder model does in fact learn stronger memory representations than the baseline encoder.
Xrp1 genetically interacts with the ALS-associated FUS orthologue caz and mediates its toxicity

Cabeza (caz) is the single Drosophila melanogaster orthologue of the human FET proteins FUS, TAF15, and EWSR1, which have been implicated in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia. In this study, we identified Xrp1, a nuclear chromatin-binding protein, as a key modifier of caz mutant phenotypes. Xrp1 expression was strongly up-regulated in caz mutants, and Xrp1 heterozygosity rescued their motor defects and life span. Interestingly, selective neuronal Xrp1 knockdown was sufficient to rescue, and neuronal Xrp1 overexpression phenocopied caz mutant phenotypes. The caz/Xrp1 genetic interaction depended on the functionality of the AT-hook DNA-binding domain in Xrp1, and the majority of Xrp1-interacting proteins are involved in gene expression regulation. Consistently, caz mutants displayed gene expression dysregulation, which was mitigated by Xrp1 heterozygosity. Finally, Xrp1 knockdown substantially rescued the motor deficits and life span of flies expressing ALS mutant FUS in motor neurons, implicating gene expression dysregulation in ALS-FUS pathogenesis.
A Tabula Rasa Approach to Sporadic Location Privacy

Attacks and defenses in the location privacy literature largely consider that users' data available for training wholly characterizes their mobility patterns. Thus, they hardwire this information in their models. We show that, in practice, training information cannot capture users' behavior with perfect certainty, and hence state-of-the-art defenses overestimate the level of privacy they provide. To tackle this problem, we propose a new model for user mobility in sporadic scenarios, that we call the blank-slate model. This model acknowledges the uncertainty of designers about real user behavior: it starts as a"blank page", and learns the actual user behavior as she queries the service provider. We use the blank-slate model to develop both an attack, the Profile Estimation Based Attack (PEBA), and a design technique, called output-feedback, that can be used to create LPPMs that progressively learn the user behavior. Using real data, we empirically show that our proposals outperform state-of-the-art mechanisms designed based on previous hardwired models. Our learning LPPMs do not require bootstrapping with training data and are easy to compute, so they could be easily deployed in current location privacy solutions.
Classification of Taxonomical Relationship by Word Embedding

In recent years, while Internet has brought various technological evolutions, users have been required to collect, select and integrate information according to a purpose. Based on this background, ontology that systemizes knowledge of the target world has been received a lot of attention. As a method of automatically constructing a super-sub relation which is a one of the important concept of ontology, there is a method of using a Lexico-syntactic pattern and a word dictionary. However, there are problems that cannot be classified correctly because it does not consider semantic relation of words so that cannot deal with words not existed in the dictionary. Therefore, a method to classify super-sub relation using a wedge product of word vectors is proposed to solve the problem. As a result, it has been confirmed that the effectiveness of the research to get higher precision/recall than that of the baseline method.
Categorizating 3D Fetal Ultrasound Image Database in First Trimester Pregnancy based on Mid-Sagittal Plane Assessments

Mid-Sagittal Plane (MSP) detection is crucial for the biometry assessments in ultrasound examinations. Screening on the correct MSP has been proven as the key condition for acquiring good quality of specified biometry measurements. In this paper, we proposed to categorize the 3D fetal ultrasound volume images based on the results of MSP detection. Based on MSP-detection results, our main focus here is to find the distinct descriptions or factors for database categorization. It is essential to realize how robust and effective the MSP-detection algorithm achieves with these factors. The database, including 381 fetal ultrasound image volumes have been collected from 141 different normal pregnant women, has been collected for more than three years in NCKU Hospital. The five factors adopted in categorizing the database include levels of image blurring, levels of weak edges, fetal adhesion, fetal posture and fetal size. The proposed MSP detection algorithm has been applied on 268 cases from the whole database (excluding the worst levels), and found the correct rate achieving 85.1 %. Then, the correct rate increases up to 90.0% by using the cases with the best conditions of all factors. Furthermore, the degree of influence for these factors in MSP detection has been discussed. At first, the results show that the image with highly weak edges (level 3) results in poor detections. Secondly, the poor fetal posture makes the highest effects on MSP detection (with 32% incorrect rate). It may be caused by having deep adhesions with the endometrium so that the fetal head boundary could not be fitted well. In fine-quality images, the adhesion factor reveals more determinative than the rough-quality factors. Thirdly, two factors of adhesion and weak edges achieved similar effects (not significant in statistics), with 23% and 25.7% incorrect rates, respectively. The less-influential factors are the fetus size and image blurring, achieving up to 14% and 16% incorrect rates, respectively.
Performance of Optical Spatial Modulation in Indoor Multipath Channel

In this paper, the performance of optical spatial modulation (OSM) in the indoor multipath optical wireless channel is investigated. Multipath propagation of the transmitted signal results in the temporal dispersion which causes intersymbol interference (ISI) especially in high-speed communications. The OSM schemes have been investigated in line-of-sight (LOS) channels. However, given the recent trend in Gigabits/s communication, there is a need to investigate the impact of the neglected higher-order reflections. Two variants of OSM are explored as case studies: optical space shift keying and spatial pulse position modulation. The multipath-induced ISI is modeled to account for the spreading of the transmitted signal, and the analytical upper bound on the symbol error rate of both OSM schemes in the multipath channel is derived. The derived analytical bounds are validated by closely matching simulation results. Furthermore, using the spatial distributions of the multipath-induced power penalty, the LOS channel response and the delay spread across the room, we demonstrate how the interaction between these parameters impacts error performance. Multipath-induced ISI has a significant adverse impact on the performance of OSM schemes, particularly on the detection of the activated transmitter as the incurred ISI can alter the channel gain of the received symbol.
Effectiveness of the Dual Dispatch to Cardiac Arrest Policy in Houston, Texas.

CONTEXT Houston policy is to dual dispatch medically trained firefighters, in addition to emergency medical services (EMS) units to out-of-hospital cardiac arrest (OHCA) cases. While believed to improve public health outcomes, no research exists supporting the policy that when firefighters respond before a better-equipped EMS unit, they increase the probability of survival. OBJECTIVE To inform EMS policy decisions regarding the effectiveness of dual dispatch by determining the impact of medically trained firefighter dispatch on return of spontaneous circulation (ROSC), a measure of survivability, in OHCA 911 calls while controlling for the subsequent arrival of an EMS unit. DESIGN This retrospective study uses logistic regression to determine the association between ROSC and response time for fire apparatus first responders controlling for arrival of the EMS unit. SETTING Out-of-hospital cardiac arrest cases in Houston between May 2008 and April 2013 when dual dispatch was used. PARTICIPANTS A total of 6961 OHCA cases with the complete data needed for the analysis. MAIN OUTCOME MEASURES Logistic regression of the dependence of OHCA survival using the indicator ROSC, as related to the fire first responder response times controlling for subsequent arrival of the EMS. RESULTS Fire apparatus arrived first in 46.7% of cases, a median value of 1.5 minutes before an EMS unit. Controlling for subsequent arrival time of EMS has no effect on ROSC achieved by the fire first responder. If the firefighters had not responded, the resulting 1.5-minute increase in response time equates to a decrease in probability of attaining ROSC of 20.1% for cases regardless of presenting heart rhythm and a 47.7% decrease for ventricular fibrillation cases in which bystander cardiopulmonary resuscitation was initiated. CONCLUSIONS The firefighter first responder not only improved response time but also greatly increased survivability independent of the arrival time of the better-equipped EMS unit, validating the public health benefit of the dual dispatch policy in Houston.
ARSENAL: Architecture for Secure Non-Volatile Memories

Whereas data persistence in non-volatile memories (NVMs) enables instant data recovery (IDR) in the face of power/system failures, it also exposes NVMs to data confidentiality and integrity attacks. Counter mode encryption and Merkle Tree authentication are established measures to thwart data confidentiality and integrity attacks, respectively, in NVMs. However, these security mechanisms require high overhead atomic security meta-data updates on every write-back in order to support IDR in NVMs. This increases memory traffic and negatively impacts system performance and memory lifetime. <italic>Architecture for Secure Non-Volatile Memories (ARSENAL)</italic> is an IDR-preserving, low cost, high performance security solution that protects NVM systems against data confidentiality and integrity attacks. ARSENAL synergistically integrates (i) <italic>Smart Writes for Faster Transactions (SWIFT) </italic>, a novel technique to reduce the performance overhead of atomic security meta-data updates on every write-back, with (ii) <italic>Terminal BMT Updates (TBU)</italic>, a novel BMT-consistency-preserving technique, to facilitate IDR in the face of power/system failures. Our evaluations show that on average, ARSENAL improves system performance (measured in IPC) by 2.26<inline-formula><tex-math notation="LaTeX">$\times$</tex-math><alternatives> <inline-graphic xlink:href="swami-ieq1-2863281.gif"/></alternatives></inline-formula> (4<inline-formula> <tex-math notation="LaTeX">$\times$</tex-math><alternatives><inline-graphic xlink:href="swami-ieq2-2863281.gif"/> </alternatives></inline-formula>), reduces memory traffic overhead by 1.47<inline-formula><tex-math notation="LaTeX"> $\times$</tex-math><alternatives><inline-graphic xlink:href="swami-ieq3-2863281.gif"/></alternatives></inline-formula> (1.88<inline-formula><tex-math notation="LaTeX">$\times$</tex-math><alternatives> <inline-graphic xlink:href="swami-ieq4-2863281.gif"/></alternatives></inline-formula>), and improves memory lifetime by 2<inline-formula><tex-math notation="LaTeX">$\times$</tex-math><alternatives> <inline-graphic xlink:href="swami-ieq5-2863281.gif"/></alternatives></inline-formula> (3.5<inline-formula> <tex-math notation="LaTeX">$\times$</tex-math><alternatives><inline-graphic xlink:href="swami-ieq6-2863281.gif"/> </alternatives></inline-formula>) in comparison to conventional IDR-preserving 64-bit (128-bit) encryption+authentication.
Simulating impacts of rapid forest loss on population size, connectivity and genetic diversity of Sunda clouded leopards (Neofelis diardi) in Borneo

Habitat loss is the greatest threat to biodiversity in Borneo, and to anticipate and combat its effects it is important to predict the pattern of loss and its consequences. Borneo is a region of extremely high biodiversity from which forest is being lost faster than in any other. The little-known Sunda clouded leopard (Neofelis diardi) is the top predator in Borneo and is likely to depend critically on habitat connectivity that is currently being rapidly lost to deforestation. We modeled the effects of landscape fragmentation on population size, genetic diversity and population connectivity for the Sunda clouded leopard across the entirety of Borneo. We modelled the impacts of land use change between the years 2000, 2010 and projected forwards to 2020. We found substantial reductions across all metrics between 2000 and 2010: the proportion of landscape connected by dispersal fell by approximately 12.5% and the largest patch size declined by around 15.1%, leading to a predicted 11.4% decline in clouded leopard numbers. We also predict that these trends will accelerate greatly towards 2020, with the percentage of the landscape being connected by dispersal falling by about 57.8%, the largest patch size falling by around 62.8% and the predicted clouded leopard population falling by 62.5% between 2010 and 2020. We predicted that these large declines in clouded leopard population size and connectivity will also substantially reduce the genetic diversity of the remaining clouded leopard population.