Miracle-dz/UniFAR-FTUs huggingface dataset:
Impaired spatial memory codes in a mouse model of Rett syndrome

The Mecp2+/- mouse model recapitulates many phenotypes of patients with Rett syndrome (RTT), including learning and memory deficits. It is unknown, however, how the disease state alters memory circuit functions in vivo in RTT mice. Here we recorded from hippocampal place cells, which are thought to encode spatial memories, in freely moving RTT mice and littermate controls. We found that place cells in RTT mice are impaired in their experience-dependent increase of spatial information. This impairment is accompanied by an enhanced baseline firing synchrony of place cells within ripple oscillations during rest, which consequently occludes the increase in synchrony after a novel experience. Behaviorally, contextual memory is normal at short but not long time scale in RTT mice. Our results suggest that hypersynchrony interferes with memory consolidation and leads to impaired spatial memory codes in RTT mice, providing a possible circuit mechanism for memory deficits in Rett Syndrome.
On Secure Transmission Design: An Information Leakage Perspective

Information leakage rate is an intuitive metric that reflects the level of security in a wireless communication system, however, there are few studies taking it into consideration. Existing work on information leakage rate has two major limitations due to the complicated expression for the leakage rate: 1) the analytical and numerical results give few insights into the trade-off between system throughput and information leakage rate; 2) and the corresponding optimal designs of transmission rates are not analytically tractable. To overcome such limitations and obtain an in-depth understanding of information leakage rate in secure wireless communications, we propose an approximation for the average information leakage rate in the fixed-rate transmission scheme. Different from the complicated expression for information leakage rate in the literature, our proposed approximation has a low-complexity expression, and hence, it is easy for further analysis. Based on our approximation, the corresponding approximate optimal transmission rates are obtained for two transmission schemes with different design objectives. Through analytical and numerical results, we find that for the system maximizing throughput subject to information leakage rate constraint, the throughput is an upward convex non-decreasing function of the security constraint and much too loose security constraint does not contribute to higher throughput; while for the system minimizing information leakage rate subject to throughput constraint, the average information leakage rate is a lower convex increasing function of the throughput constraint.
A Soft Robotic Wearable Wrist Device for Kinesthetic Haptic Feedback

Advances in soft robotics provide a unique approach for delivering haptic feedback to a user by a soft wearable device. Such devices can apply forces directly on the human joints, while still maintaining the safety and flexibility necessary for use in close proximity to the human body. To take advantage of these properties, we present a new haptic wrist device using pressure-driven soft actuators called reverse pneumatic artificial muscles (rPAMs) mounted on four sides of the wrist. These actuators are originally pre-strained and release compressive stress under pressure, applying a safe torque around the wrist joints while being compact and portable, representing the first soft haptic device capable of real-time feedback. To demonstrate the functional utility of this device, we created a virtual path-following task, wherein the user employs the motion of their wrist to control their embodied agent. We used the haptic wrist device to assist the user in following the path and study their performance with and without haptic feedback in multiple scenarios. Our results quantify the effect of wearable soft robotic haptic feedback on user performance. Specifically, we observed that our haptic feedback system improved the performance of users following complicated paths in a statistically significant manner, but did not show improvement for simple linear paths. Based on our findings, we anticipate broader applications of wearable soft robotic haptic devices toward intuitive user interactions with robots, computers, and other users.
Asynchronous Advantage Actor-Critic Agent for Starcraft II

Deep reinforcement learning, and especially the Asynchronous Advantage Actor-Critic algorithm, has been successfully used to achieve super-human performance in a variety of video games. Starcraft II is a new challenge for the reinforcement learning community with the release of pysc2 learning environment proposed by Google Deepmind and Blizzard Entertainment. Despite being a target for several AI developers, few have achieved human level performance. In this project we explain the complexities of this environment and discuss the results from our experiments on the environment. We have compared various architectures and have proved that transfer learning can be an effective paradigm in reinforcement learning research for complex scenarios requiring skill transfer.
Clustering of Driving Scenarios Using Connected Vehicle Datasets

Driving encounter classification and analysis can benefit autonomous vehicles to efficiently achieve a more smart decision. This paper presents an unsupervised learning framework to classify a wide range of driving encounters which compose of a pair of vehicles' GPS trajectories ordered by time. First, we develop five specific approaches, through integrating deep autoencoders with distance-based measures, to learn underlying representations of driving encounters in terms of both shape and distance, and we thoroughly compare and evaluate the performance of the five approaches. We then apply k-means clustering to categorize driving encounters into distinguishable groups based on the learned representations. Our proposed unsupervised learning framework for driving encounter classification is finally validated based on 2568 naturalistic driving encounters from connected vehicle datasets.
Adult Learning of Novel Words in a Non-native Language: Consonants, Vowels, and Tones

While words are distinguished primarily by consonants and vowels in many languages, tones are also used in the majority of the world's languages to cue lexical contrasts. However, studies on novel word learning have largely concentrated on consonants and vowels. To shed more light on the use of tonal information in novel word learning and its relationship with the development of phonological categories, the present study explored how adults' ability to learn minimal pair pseudowords in a tone language is modulated by their native phonological knowledge. Twenty-four adult speakers of three languages were tested: Cantonese, Mandarin, and French. Eye-tracking was used to record eye movements of these learners, while they were watching animated cartoons in Cantonese. On each trial, adults had to learn two new label-object associations, while the labels differed minimally by a consonant, a vowel, or a tone. Learning would therefore attest to participants' ability to use phonological information to distinguish the paired words. Results first revealed that adult learners in each language group performed better than chance in all conditions. Moreover, compared to native Cantonese adults, both Mandarin- and French-speaking adults performed worse on all three contrasts. In addition, French adults were worse on tones when compared to Mandarin adults. Lastly, no advantage for consonantal information in native lexical processing was found for Cantonese-speaking adults as predicted by the “division of labor” proposal, thus confirming crosslinguistic differences in consonant/vowel weight between speakers of tonal vs. non-tonal languages. These findings establish rapid novel word learning in a non-native language (long-term learning will have to be further assessed), modulated by native phonological knowledge. The implications of the findings of this adult study for further infant word learning studies are discussed.
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time

Is Android malware classification a solved problem? Published F1 scores of up to 0.99 appear to leave very little room for improvement. In this paper, we argue that results are commonly inflated due to two pervasive sources of experimental bias: "spatial bias" caused by distributions of training and testing data that are not representative of a real-world deployment; and "temporal bias" caused by incorrect time splits of training and testing sets, leading to impossible configurations. We propose a set of space and time constraints for experiment design that eliminates both sources of bias. We introduce a new metric that summarizes the expected robustness of a classifier in a real-world setting, and we present an algorithm to tune its performance. Finally, we demonstrate how this allows us to evaluate mitigation strategies for time decay such as active learning. We have implemented our solutions in TESSERACT, an open source evaluation framework for comparing malware classifiers in a realistic setting. We used TESSERACT to evaluate three Android malware classifiers from the literature on a dataset of 129K applications spanning over three years. Our evaluation confirms that earlier published results are biased, while also revealing counter-intuitive performance and showing that appropriate tuning can lead to significant improvements.
Is autumn the key for dengue epidemics in non endemic regions? The case of Argentina

Background Dengue is a major and rapidly increasing public health problem. In Argentina, the southern extreme of its distribution in the Americas, epidemic transmission takes place during the warm season. Since its re-emergence in 1998 two major outbreaks have occurred, the biggest during 2016. To identify the environmental factors that trigger epidemic events, we analyzed the occurrence and magnitude of dengue outbreaks in time and space at different scales in association with climatic, geographic and demographic variables and number of cases in endemic neighboring countries. Methods Information on dengue cases was obtained from dengue notifications reported in the National Health Surveillance System. The resulting database was analyzed by Generalized Linear Mixed Models (GLMM) under three methodological approaches to: identify in which years the most important outbreaks occurred in association with environmental variables and propose a risk estimation for future epidemics (temporal approach); characterize which variables explain the occurrence of local outbreaks through time (spatio-temporal approach); and select the environmental drivers of the geographical distribution of dengue positive districts during 2016 (spatial approach). Results Within the temporal approach, the number of dengue cases country-wide between 2009 and 2016 was positively associated with the number of dengue cases in bordering endemic countries and negatively with the days necessary for transmission (DNT) during the previous autumn in the central region of the country. Annual epidemic intensity in the period between 1999–2016 was associated with DNT during previous autumn and winter. Regarding the spatio-temporal approach, dengue cases within a district were also associated with mild conditions in the previous autumn along with the number of dengue cases in neighboring countries. As for the spatial approach, the best model for the occurrence of two or more dengue cases per district included autumn minimum temperature and human population as fixed factors, and the province as a grouping variable. Explanatory power of all models was high, in the range 57–95%. Discussion Given the epidemic nature of dengue in Argentina, virus pressure from endemic neighboring countries along with climatic conditions are crucial to explain disease dynamics. In the three methodological approaches, temperature conditions during autumn were best associated with dengue patterns. We propose that mild autumns represent an advantage for mosquito vector populations and that, in temperate regions, this advantage manifests as a larger egg bank from which the adult population will re-emerge in spring. This may constitute a valuable anticipating tool for high transmission risk events.
Molecular Typing of ST239-MRSA-III From Diverse Geographic Locations and the Evolution of the SCCmec III Element During Its Intercontinental Spread

ST239-MRSA-III is probably the oldest truly pandemic MRSA strain, circulating in many countries since the 1970s. It is still frequently isolated in some parts of the world although it has been replaced by other MRSA strains in, e.g., most of Europe. Previous genotyping work (Harris et al., 2010; Castillo-Ramírez et al., 2012) suggested a split in geographically defined clades. In the present study, a collection of 184 ST239-MRSA-III isolates, mainly from countries not covered by the previous studies were characterized using two DNA microarrays (i) targeting an extensive range of typing markers, virulence and resistance genes and (ii) a SCCmec subtyping array. Thirty additional isolates underwent whole-genome sequencing (WGS) and, together with published WGS data for 215 ST239-MRSA-III isolates, were analyzed using in-silico analysis for comparison with the microarray data and with special regard to variation within SCCmec elements. This permitted the assignment of isolates and sequences to 39 different SCCmec III subtypes, and to three major and several minor clades. One clade, characterized by the integration of a transposon into nsaB and by the loss of fnbB and splE was detected among isolates from Turkey, Romania and other Eastern European countries, Russia, Pakistan, and (mainly Northern) China. Another clade, harboring sasX/sesI is widespread in South-East Asia including China/Hong Kong, and surprisingly also in Trinidad & Tobago. A third, related, but sasX/sesI-negative clade occurs not only in Latin America but also in Russia and in the Middle East from where it apparently originated and from where it also was transferred to Ireland. Minor clades exist or existed in Western Europe and Greece, in Portugal, in Australia and New Zealand as well as in the Middle East. Isolates from countries where this strain is not epidemic (such as Germany) frequently are associated with foreign travel and/or hospitalization abroad. The wide dissemination of this strain and the fact that it was able to cause a hospital-borne pandemic that lasted nearly 50 years emphasizes the need for stringent infection prevention and control and admission screening.
Capacity Analysis for Full Duplex Self-Backhauled Small Cells

Full duplex (FD) communication enables simultaneous transmission and reception on the same frequency band. Although it has the potential of doubling the throughput on isolated links, in reality higher interference and asymmetric traffic demands in the uplink and downlink could significantly reduce the gains of FD operations. In this paper, we consider the application of FD operation in self-backhauled small cells, where multiple FD capable small cell base stations (SBS) are wirelessly backhauled by a FD capable macro-cell BS (MBS). To increase the capacity of the backhaul link, the MBS is equipped with multiple antennas to enable space division multiple access (SDMA). A scheduling method using the back-pressure algorithm and geometric programming is proposed for link selection and interference mitigation. Simulation results show that with FD SDMA backhaul links, the proposed scheduler almost doubles throughput even under asymmetric traffic demand and varied network conditions.
Independent Sets in Vertex-Arrival Streams

We consider the classic maximal and maximum independent set problems in three models of graph streams: In the edge-arrival model we see a stream of edges which collectively define a graph, this model has been well-studied for a variety of problems. We first show that the space complexity for a one-pass streaming algorithm to find a maximal independent set is quadratic (i.e. we must store all edges). We further show that the problem does not become much easier if we only require approximate maximality. In the "explicit" vertex stream model, the input stream is a sequence of vertices making up the graph, where every vertex arrives along with its incident edges that connect to previously arrived vertices. Various graph problems require substantially less space to solve in this setting than for edge-arrival streams. We show that every one-pass $c$-approximation algorithm for maximum independent set (MIS) on explicit vertex streams requires space $\Omega(\frac{n^2}{c^7})$, where $n$ is the number of vertices of the input graph, and it is already known that space $\tilde{\Theta}(\frac{n^2}{c^2})$ is necessary and sufficient in the edge arrival model (Halldorsson et al. 2012). The MIS problem is thus not significantly easier to solve under the explicit vertex arrival order assumption. Our result is proved via a reduction to a new multi-party communication problem closely related to pointer jumping. In the "implicit" vertex stream model, the input stream consists of a sequence of objects, one per vertex. The algorithm is equipped with a function that can map a pair of objects to the presence or absence of an edge, thus defining the graph. This model captures, for example, geometric intersection graphs such as unit disc graphs. Our final set of results consists of several improved upper and lower bounds for ball intersection graphs, in both explicit and implicit streams.
Improving Automatic Skin Lesion Segmentation using Adversarial Learning based Data Augmentation

Segmentation of skin lesions is considered as an important step in computer aided diagnosis (CAD) for automated melanoma diagnosis. In recent years, segmentation methods based on fully convolutional networks (FCN) have achieved great success in general images. This success is primarily due to the leveraging of large labelled datasets to learn features that correspond to the shallow appearance as well as the deep semantics of the images. However, the dependence on large dataset does not translate well into medical images. To improve the FCN performance for skin lesion segmentations, researchers attempted to use specific cost functions or add post-processing algorithms to refine the coarse boundaries of the FCN results. However, the performance of these methods is heavily reliant on the tuning of many parameters and post-processing techniques. In this paper, we leverage the state-of-the-art image feature learning method of generative adversarial network (GAN) for its inherent ability to produce consistent and realistic image features by using deep neural networks and adversarial learning concept. We improve upon GAN such that skin lesion features can be learned at different level of complexities, in a controlled manner. The outputs from our method is then augmented to the existing FCN training data, thus increasing the overall feature diversity. We evaluated our method on the ISIC 2018 skin lesion segmentation challenge dataset and showed that it was more accurate and robust when compared to the existing skin lesion segmentation methods.
Waterbird counts on large water bodies: comparing ground and aerial methods during different ice conditions

The aerial and ground methods of counting birds in a coastal area during different ice conditions were compared. Ice coverage of water was an important factor affecting the results of the two methods. When the water was ice-free, more birds were counted from the ground, whereas during ice conditions, higher numbers were obtained from the air. The first group of waterbirds with the smallest difference between the two methods (average 6%) contained seven species: Mute Swan Cygnus olor, Whooper Swan Cygnus cygnus, Greater Scaup Aythya marila, Tufted Duck Aythya fuligula, Common Goldeneye Bucephala clangula, Smew Mergellus albellus and Goosander Mergus merganser; these were treated as the core group. The second group with a moderate difference (average 20%) included another six species: Mallard Anas platyrhynchos, Eurasian Wigeon Mareca penelope, Common Pochard Aythya ferina, Great Crested Grebe Podiceps cristatus and Eurasian Coot Fulica atra. The third group with a large difference (average 85%) included five species, all of the Anatini tribe: Gadwall Mareca strepera, Northern Pintail Anas acuta, Northern Shoveler Spatula clypeata, Eurasian Teal Anas crecca and Garganey Spatula querquedula. During ice conditions, smaller numbers of most species were counted from the ground. The exception here was Mallard, more of which were counted from the ground, but the difference between two methods was relatively small in this species (7.5%). Under ice-free conditions, both methods can be used interchangeably for the most numerous birds occupying open water (core group) without any significant impact on the results. When water areas are frozen over, air counts are preferable as the results are more reliable. The cost analysis shows that a survey carried out by volunteer observers (reimbursement of travel expenses only) from the land is 58% cheaper, but if the observers are paid, then an aerial survey is 40% more economical.
Cross‐cultural differences in object recognition: Comparing asylum seekers from Sub‐Saharan Africa and a matched Western European control group

Summary Nowadays, more and more people report about their memories in cross‐cultural contexts. In international criminal settings and asylum procedures, object recognition tests can provide valuable information, for example, about weapons used during a crime or landmarks from the claimed region of origin. This study was the first to compare object recognition performance by asylum seekers from Sub‐Saharan Africa to a matched Western European control group. African participants performed worse than European participants on perceptual tests involving transformations from two‐ to three‐dimensional representations, but both groups performed equally well on an object recognition test that involved transformation from three‐ to two‐dimensional representations. However, African participants were significantly more likely to respond “yes” on the recognition test (i.e., an acquiescence response style) than European participants. Our findings elucidate cultural differences in responding on an object recognition test. Judges, juries, and immigration officials would be wise to take these differences into account when evaluating recognition performance in cross‐cultural contexts.