Miracle-dz/UniFAR-FTUs huggingface dataset:
Integration and multiplexing of positional and contextual information by the hippocampal network

The hippocampus is known to store cognitive representations, or maps, that encode both positional and contextual information, critical for episodic memories and functional behavior. How path integration and contextual cues are dynamically combined and processed by the hippocampus to maintain these representations accurate over time remains unclear. To answer this question, we propose a two-way data analysis and modeling approach to CA3 multi-electrode recordings of a moving rat submitted to rapid changes of contextual (light) cues, triggering back-and-forth instabitilies between two cognitive representations (Jezek et al, Nature 478, p 246 (2011)). We develop a dual neural activity decoder, capable of independently identifying the recalled cognitive map at high temporal resolution (comparable to theta cycle) and the position of the rodent given a map. Remarkably, position can be reconstructed at any time with an accuracy comparable to fixed-context periods, even during highly unstable periods. These findings provide evidence for the capability of the hippocampal neural activity to maintain an accurate encoding of spatial and contextual variables, while one of these variables undergoes rapid changes independently of the other. To explain this result we introduce an attractor neural network model for the hippocampal activity that process inputs from external cues and the path integrator. Our model allows us to make predictions on the frequency of the cognitive map instability, its duration, and the detailed nature of the place-cell population activity, which are validated by a further analysis of the data. Our work therefore sheds light on the mechanisms by which the hippocampal network achieves and updates multi-dimensional neural representations from various input streams. Author summary As an animal moves in space and receives external sensory inputs, it must dynamically maintain the representations of its position and environment at all times. How the hippocampus, the brain area crucial for spatial representations, achieves this task, and manages possible conflicts between different inputs remains unclear. We propose here a comprehensive attractor neural network-based model of the hippocampus and of its multiple input streams (including self-motion). We show that this model is capable of maintaining faithful representations of positional and contextual information, and resolves conflicts by adapting internal representations to match external cues. Model predictions are confirmed by the detailed analysis of hippocampal recordings of a rat submitted to quickly varying and conflicting contextual inputs.
A Cosine Similarity-Based Method to Infer Variability of Chromatin Accessibility at the Single-Cell Level

Cellular identity between generations of developing cells is propagated through the epigenome particularly via the accessible parts of the chromatin. It is now possible to measure chromatin accessibility at single-cell resolution using single-cell assay for transposase accessible chromatin (scATAC-seq), which can reveal the regulatory variation behind the phenotypic variation. However, single-cell chromatin accessibility data are sparse, binary, and high dimensional, leading to unique computational challenges. To overcome these difficulties, we developed PRISM a computational workflow and R package (https://github.com/stanleycai123/PRISM) that quantifies cell-to-cell chromatin accessibility variation while controlling for technical biases. Using data generated in our lab or publically available, we show that PRISM outperforms an existing algorithm, which relies on the aggregate of signal across a set of genomic regions. PRISM shows robustness to noise in low accessibility cells and reveals previously masked accessibility variation where accessible sites differ between cells but total number of accessible sites is constant. We also show that PRISM, but not an existing algorithm, finds suppressed heterogeneity of accessibility at CTCF binding sites. PRISM is a novel multidimensional scaling-based method using angular cosine distance metrics coupled with distance from the spatial centroid. PRISM takes differences in accessibility at each genomic region between single cells into account. This updated approach uncovers new biological results with profound implications on the cellular heterogeneity of chromatin architecture.
RaptorQ-Based Efficient Multimedia Transmission Over Cooperative Cellular Cognitive Radio Networks

Accommodating the fast-growing multimedia traffic demands at low cost is critical to ensure the future competitiveness of cellular networks. Cognitive radio (CR) emerges as a key technology to enhance radio spectrum utilization and provides an effective solution to meet critical spectrum demands for future mobile multimedia traffics. In this paper, we investigate reliable downlink video transmission in underlay cellular CR networks by means of cooperative diversity. We use RaptorQ code as the Application Layer Forward Error Correction scheme to combat packet loss arising from the primary user interference and other channel conditions such as channel fading. For implementing cooperative diversity, we select the best relay based on the ability of successful decoding using RaptorQ and the value of signal-to-interference-plus-noise ratio at the destination node. Moreover, for efficient primary user channel utilization, the selected relay performs merging of the encoding coefficients before forwarding to the destination. We evaluate the performance of the proposed framework under various time sharing scenarios between the direct and best indirect transmission links in terms of decoding overhead, probability of decoding failure, and peak signal-to-noise ratio of the received videos. Numerical results show that the proposed scheme outperforms the baseline transmission scheme and conventional relaying schemes using Amplify-and-Forward and Decode-and-Forward transmission techniques.
Efficient Two Stage Spectrum Sensing for Cognitive Radios

The growth of wireless communication in recent years has resulted to spectrum scarcity and increase in the number mobile users. However, the major concern in wireless communication is spectrum scarcity on fixed spectrum. Technologies such as cognitive radios (CR) have emerged to address the issue of spectrum scarcity in wireless communication. The principle which CR is using to address spectrum scarcity is by utilizing opportunistic spectrum access method. In this method CR allows the secondary users (SU) to opportunistically utilize spectrum by sensing the absence or presence of the primary users (PU) on the spectrum that it will assign to the SU. Thus, before CR allows the SU to utilize the spectrum it must first carry out the process of spectrum sensing (SS). This process of SS can either be done by exerting single stage or two stage SS techniques. Nevertheless, this process is not an easy task, due to several factors such as noise, cochannel interferences and attenuation which have a negative effect on the accuracy, reliability and efficiency of CR. Thus, in this work literature review on single stage and two stage (dual stage) SS techniques was carried out to determine how the efficiency, accuracy and sensing time on SS can be improved.
Fault-Tolerant Cooperative Tracking Control of Nonlinear Affine Systems Using Integral Sliding Mode Control

Ahstract- The cooperative tracking problem for a class of nonlinear affine systems are addressed. Actuator faults and external disturbance/model uncertainty are allowed such that we aim to design distributed controllers that can tolerate them while realize cooperative tracking. A general setup is considered, that is, actuator faults can take such forms as actuator degradation and total failure. Then, to guarantee the feasibility of fault-tolerant control, we assume that the actuator has sufficiently many healthy components which enables us to apply integral sliding mode control (ISMC) to develop the distributed controllers that can fulfill the control target and the external disturbance can simultaneously be successfully rejected. We also provide the simulation results to verify the validity of our theoretical findings.
A Simple Control Technique for Distributed Generations in Grid-Connected and Islanded Modes

This paper presents a control technique for the seamless transfer of AC microgrid between grid-connected and islanded modes of operation. When a fault occurs in the utility grid, microgrid should be disconnected from the utility grid to protect its components and continuously supply the local loads in the microgrid. In the opposite case, if the utility grid is in normal condition, the microgrid should be connected to the utility grid. To fulfilling these control objectives, the proposed control technique handles two transfer modes: grid-connected to islanded mode and vice versa. Each distributed generation (DG) unit is controlled by the voltage control technique in islanded mode whereas the DG is controlled by feed-forward based control technique in grid-connected mode so as to ensure the seamless transition. Furthermore, parallel converters have been controlled by droop control technique in order to deliver desired power to the local loads in the islanded mode. The general operation principle is analyzed and demonstrated by means of simulation.
Sum-Throughput Maximization in Wireless Sensor Networks With Radio Frequency Energy Harvesting and Backscatter Communication

This paper formulates and solves optimization problems whose objective is to maximize the sum-throughput of wireless sensor networks with radio frequency (RF) energy harvesting (EH) and backscatter communication. The paper proposes and analyzes three protocols for maximizing the sum-throughput; namely, the time-division with downlink data decoding protocol, hybrid power splitting/data decoding protocol, and backscatter-enabled combination protocol. The time-division protocol optimizes a wireless powered communication network (WPCN) with a two-way communication link between the sensors and a hybrid access point. The hybrid protocol optimizes a WPCN with a power splitting downlink data decoding phase. The backscattered-enabled combination protocol optimizes a WPCN with a power splitting downlink data decoding phase and backscatter communication enabled sensors. Numerical results show that the hybrid and combination protocols deliver up to 14.5% and 30.5% increase in sum-throughput, respectively, compared with the reference time-switching RF EH protocol in a dynamic environment. The findings are significant for the future widespread adoption of WPCN systems powered by RF energy sources in the real world by increasing the amount of harvested energy and system achievable data rate.
Cluster Characterization of 3-D MIMO Propagation Channel in an Urban Macrocellular Environment

Multidimensional characterization of outdoor urban macrocellular propagation channels is essential for the analysis and design of next-generation (5G and beyond) cellular massive MIMO (multiple-input-multiple-output) systems. Since most massive MIMO arrays will extend in two or three dimensions, an understanding of 3-D parameters (i.e., azimuth and elevation) of the multipath components (MPCs) is required. This paper presents an extensive measurement campaign for 3-D outdoor propagation channels in an urban macrocellular environment. Measurements were performed with a 20 MHz wideband polarimetric MIMO channel sounder centered at 2.53 GHz and MPCs were extracted using RIMAX—an iterative maximum likelihood algorithm. The physical propagation mechanisms of the observed discrete MPCs are explained in terms of waveguiding, over-the-rooftop propagation, and scattering by far-away objects. MPCs exhibit clustering in the temporal and spatial domains; both intra- and inter-cluster parameters and their relevant statistics are provided. We also extract diffuse MPCs, show that they can comprise a moderate portion of the overall energy, and provide a statistical characterization.
Single or Multiple Frames Content Delivery for Next-Generation Networks?

This paper addresses the four enabling technologies, namely multi-user sparse code multiple access (SCMA), content caching, energy harvesting, and physical layer security for proposing an energy and spectral efficient resource allocation algorithm for the access and backhaul links in heterogeneous cellular networks. Although each of the above mentioned issues could be a topic of research, in a real situation, we would face a complicated scenario where they should be considered jointly, and hence, our target is to consider these technologies jointly in a unified framework. Moreover, we propose two novel content delivery scenarios: 1) single frame content delivery (SFCD), and 2) multiple frames content delivery (MFCD), where the time duration of serving user requests is divided into several frames. In the first scenario, the requested content by each user is served over one frame. However, in the second scenario, the requested content by each user can be delivered over several frames. We formulate the resource allocation for the proposed scenarios as optimization problems where our main aim is to maximize the energy efficiency of access links subject to the transmit power and rate constraints of access and backhaul links, caching and energy harvesting constraints, and SCMA codebook allocation limitations. Due to the practical limitations, we assume that the channel state information values between eavesdroppers and base stations are uncertain and design the network for the worst case scenario. Since the corresponding optimization problems are mixed integer non-linear and nonconvex programming, NP-hard, and intractable, we propose an iterative algorithm based on the well-known alternate and successive convex approximation methods. In addition, the proposed algorithms are studied from the computational complexity, convergence, and performance perspectives. Moreover, the proposed caching scheme outperforms the existing traditional caching schemes like random caching and most popular caching. We also study the effect of joint and disjoint considerations of enabling technologies for the performance of next-generation networks. On the one hand, our proposed caching strategy increases slightly the computational complexity by 3%, 1.1%, and 2% compared to no caching, SFCD, and disjoint solution, respectively. On the other hand, the proposed caching scheme achieves a performance gain of 43%, 9.4%, and 51.3% compared to the three state-of-the-art schemes.
Genomic landscape and mutational impacts of recurrently mutated genes in cancers

Cancer genes tend to be highly mutated under positive selection. Better understanding the recurrently mutated genes (RMGs) in cancer is critical for explicating the mechanisms of tumorigenesis and providing vital clues for therapy. Although some studies have investigated functional impacts of RMGs in specific cancer types, a comprehensive analysis of RMGs and their mutational impacts across cancers is still needed.
Sub-band Robust GNSS Signal Processing for Jamming Mitigation

Jammers, illegal devices, which broadcast powerful signals in the Global Navigation Satellite System (GNSS) frequency bands, can significantly degrade the performance of GNSS receivers. A possible approach to mitigate the impact of jamming is the implementation of Pulse Blanking (PB) that is an effective mitigation technique for pulsed interference. Clean samples are however required for receiver processing. In order to achieve this condition, filtering can be used: the jamming signal usually sweeps large frequency bands. When filtering is applied, the jamming signal periodically enters and exits the filter bandwidth resulting in pulsed interference. For wide-band GNSS signals, a Filter Bank (FB) can be adopted: the input signal is split into several sub-bands and PB is applied independently on each sub-band. The jamming signal impacts only one sub-band at the time resulting in a sequence of pulses in the different sub-band channels. In this paper, sub-band signal processing is combined with robust Zero-Memory Non-Linearities (ZMNLs) and a general scheme for interference mitigation is proposed. In particular, PB is replaced by ZMNLs that introduce robustness to the processing of GNSS signals. Sub-band decomposition is performed using a uniform Discrete Fourier Transform (DFT) FB, which is efficiently implemented using polyphase decomposition. Different FB configurations and different ZMNLs are considered. Hardware simulations and real GNSS data are used to demonstrate the effectiveness of the mitigation schemes proposed.
Polylogarithmic Approximation Algorithms for Weighted-ℱ-deletion Problems

For a family of graphs ℱ, the Weighted ℱ Vertex Deletion problem, is defined as follows: given an n-vertex undirected graph G and a weight function w: V(G)࢐ ℝ, find a minimum weight subset S⊆ V(G) such that G-S belongs to ℱ. We devise a recursive scheme to obtain O(logO(1) n)-approximation algorithms for such problems, building upon the classical technique of finding balanced separators. We obtain the first O(logO(1) n)-approximation algorithms for the following problems. • Let F be a finite set of graphs containing a planar graph, and ℱ=G(F) be the maximal family of graphs such that every graph H∈ G(F) excludes all graphs in F as minors. The vertex deletion problem corresponding to ℱ=G(F) is the Weighted Planar F-Minor-Free Deletion (WP F-MFD) problem. We give a randomized and a deterministic approximation algorithms for WP F-MFD with ratios O(log1.5 n) and O(log2 n), respectively. Prior to our work, a randomized constant factor approximation algorithm for the unweighted version was known [FOCS 2012]. After our work, a deterministic constant factor approximation algorithm for the unweighted version was also obtained [SODA 2019]. • We give an O(log2 n)-factor approximation algorithm for Weighted Chordal Vertex Deletion, the vertex deletion problem to the family of chordal graphs. On the way to this algorithm, we also obtain a constant factor approximation algorithm for Multicut on chordal graphs. • We give an O(log3 n)-factor approximation algorithm for Weighted Distance Hereditary Vertex Deletion. We believe that our recursive scheme can be applied to obtain O(logO(1) n)-approximation algorithms for many other problems as well.
Identifying Student Difficulty in a Digital Learning Environment

This paper discusses the development of TutorAlert, a natural language processing system similar to those used in sentiment analysis, but applied to the data generated by students in a digital online learning environment in order to detect confused or frustrated students. A number of machine learning algorithms were tested in the development process, including Support Vector Machines (SVM), Naive Bayes, and Random Forest classifiers. As well, an array of natural language preparation techniques were employed to determine the optimum preprocessing configuration to produce relevant results. We found that detecting potential student frustration or confusion was most successful using a Sequential Minimal Optimization algorithm (SMO), along with the Stanford Part-Of-Speech Tagger (POS Tagger), the iterated version of the Lovins stemmer, and a custom dictionary to help determine relevance probability. This model produced a promising initial F1 score of 0.79 and an accuracy of 0.83. Further, agreement values of 88% were achieved during inter-rater reliability testing between the classifier and human judges.
Modeling Unknown Class Centers for Metric Learning on Person Re-Identification

Metric learning is one of the major methods for person re-identification. Most existing metric learning methods for person re-identification first generate the pairwise constraints where the sample pairs with the same labels consist of the positive set and the ones with the different labels comprise the negative set. And then, different kinds of methods are formulated to pull the pairs in positive set together and penalize the pairs in negative set such that a good metric is learned. However, such a process has two drawbacks: 1) the size of negative set is often far more than the negative set for which the learning process is largely dominated by the large amount of negative sample pairs and 2) it often experiences tedious optimization procedures to compute pairwise distances which would be computationally intractable in real scenarios, especially for large-scale data sets. To address the above issues, we propose a new simple and effective metric learning method, which gets rid of the pairwise constraints. We take the unknown class center’s information into consideration to model the relationships between different classes directly, and a new objective function is formed. In addition, the proposed objective function can be solved by taking advantage of simple matrix multiplications and hence can avoid computationally complex optimization schemes. The proposed algorithm termed largest center-specific margin metric learning is shown to be computationally efficient and can be applied to large-scale person re-identification. Extensive experiments carried out on two challenging large-scale databases (CUHK03 and Market1501) demonstrate that the proposed algorithm performs favorably against the state-of-the-art approaches.
Exploring Hashtags Collaboratively to Facilitate Learning and Knowledge Construction

In the era of mobile learning and digital native students, concepts like seamless learning, situated learning and rhizomatic learning gain importance as the current technology is able to support them by developing new kinds of applications formobiledevices. Atthesametime, socialnetworkplatforms like Twitter are also being studied as a way to boost communication supporting collaborative knowledge construction since they are available everywhere and students are familiar with their use. This paper highlights an experience in which a mobile application called FeedbackApp was developed and used for these purposes. The main characteristic of this application is that it uses various ways to explore the information generated while searching and sharing information and discussing the learning topics, using concept maps, geographical maps and text lists. A preliminary experience shows that this application might encourage students to be more participative in discussions and exchange of ideas.
Documenting young children's technology use: Observations in the home

While there has been much interest in children's use of different technologies, research is often done with school-age children in their classrooms. This exploratory research study looks at fifteen preschool children (aged three to five) in Queensland, Australia and their use of different technologies in their own homes. This paper examines data from a checklist of technologies available in the home and video recording data of children's interactions with online technologies and other people captured by parents, which were analyzed using a modified ‘seating sweeps’ (Given & Leckie 2003) approach to gain a detailed, descriptive analysis of the home environment. A range of technologies are available to children, with the television and DVD player being most common in the home. Unlike desktop and laptop computers, which were restricted to adult use in half of homes, mobile computing devices (e.g., tablets and smartphones) were quite prevalent and generally available for children's use. In almost all cases children used devices designed for adults and often used them in common spaces in the home, such as the home office (38%) or living room (36%). Many children (45%) engaged independently with technology, able to accomplish activities and learn on their own. This study contributes to a growing body of literature about how young children connect to technology and the growing digital world around them. Examining children's interaction with technology and in the home environment allows researchers to better understand the role of technology in children's lives.