Miracle-dz/UniFAR-FTUs huggingface dataset:
hARMS: A Hardware Acceleration Architecture for Real-Time Event-Based Optical Flow

Event-based vision sensors produce asynchronous event streams with high temporal resolution based on changes in the visual scene. The properties of these sensors allow for accurate and fast calculation of optical flow as events are generated. Existing solutions for calculating optical flow from event data either fail to capture the true direction of motion due to the aperture problem, do not use the high temporal resolution of the sensor, or are too computationally expensive to be run in real time on embedded platforms. In this research, we first present a faster version of our previous algorithm, ARMS (Aperture Robust Multi-Scale flow). The new optimized software version (fARMS) significantly improves throughput on a traditional CPU. Further, we present hARMS, a hardware realization of the fARMS algorithm allowing for real-time computation of true flow on low-power, embedded platforms. The proposed hARMS architecture targets hybrid system-on-chip devices and was designed to maximize configurability and throughput. The hardware architecture and fARMS algorithm were developed with asynchronous neuromorphic processing in mind, abandoning the common use of an event frame and instead operating using only a small history of relevant events, allowing latency to scale independently of the sensor resolution. This change in processing paradigm improved the estimation of flow directions by up to 73% compared to the existing method and yielded a demonstrated hARMS throughput of up to 1.21 Mevent/s on the benchmark configuration selected. This throughput enables real-time performance and makes it the fastest known realization of aperture-robust, event-based optical flow to date.
Edge-Compatible Reinforcement Learning for Recommendations

Most reinforcement learning (RL) recommendation systems designed for edge computing must either synchronize during recommendation selection or depend on an unprincipled patchwork collection of algorithms. In this work, we build on asynchronous coagent policy gradient algorithms \citep{kostas2020asynchronous} to propose a principled solution to this problem. The class of algorithms that we propose can be distributed over the internet and run asynchronously and in real-time. When a given edge fails to respond to a request for data with sufficient speed, this is not a problem; the algorithm is designed to function and learn in the edge setting, and network issues are part of this setting. The result is a principled, theoretically grounded RL algorithm designed to be distributed in and learn in this asynchronous environment. In this work, we describe this algorithm and a proposed class of architectures in detail, and demonstrate that they work well in practice in the asynchronous setting, even as the network quality degrades.
Leaping Through Time with Gradient-based Adaptation for Recommendation

Modern recommender systems are required to adapt to the change in user preferences and item popularity. Such a problem is known as the temporal dynamics problem, and it is one of the main challenges in recommender system modeling. Different from the popular recurrent modeling approach, we propose a new solution named LeapRec to the temporal dynamic problem by using trajectory-based meta-learning to model time dependencies. LeapRec characterizes temporal dynamics by two complement components named global time leap (GTL) and ordered time leap (OTL). By design, GTL learns long-term patterns by finding the shortest learning path across unordered temporal data. Cooperatively, OTL learns short-term patterns by considering the sequential nature of the temporal data. Our experimental results show that LeapRec consistently outperforms the state-of-the-art methods on several datasets and recommendation metrics. Furthermore, we provide an empirical study of the interaction between GTL and OTL, showing the effects of long- and short-term modeling.
Optimal Partitions for Nonparametric Multivariate Entropy Estimation

Efficient and accurate estimation of multivariate empirical probability distributions is fundamental to the calculation of information-theoretic measures such as mutual information and transfer entropy. Common techniques include variations on histogram estimation which, whilst computationally efficient, are often unable to precisely capture the probability density of samples with high correlation, kurtosis or fine substructure, especially when sample sizes are small. Adaptive partitions, which adjust heuristically to the sample, can reduce the bias imparted from the geometry of the histogram itself, but these have commonly focused on the location, scale and granularity of the partition, the effects of which are limited for highly correlated distributions. In this paper, I reformulate the differential entropy estimator for the special case of an equiprobable histogram, using a k-d tree to partition the sample space into bins of equal probability mass. By doing so, I expose an implicit rotational orientation parameter, which is conjectured to be suboptimally specified in the typical marginal alignment. I propose that the optimal orientation minimises the variance of the bin volumes, and demonstrate that improved entropy estimates can be obtained by rotationally aligning the partition to the sample distribution accordingly. Such optimal partitions are observed to be more accurate than existing techniques in estimating entropies of correlated bivariate Gaussian distributions with known theoretical values, across varying sample sizes (99% CI).
Predicting User Code-Switching Level from Sociological and Psychological Profiles

Multilingual speakers tend to alternate between languages within a conversation, a phenomenon referred to as “code-switching” (CS). CS is a complex phenomenon that not only encompasses linguistic challenges, but also contains a great deal of complexity in terms of its dynamic behaviour across speakers. This dynamic behaviour has been studied by sociologists and psychologists, identifying factors affecting CS. In this paper, we provide an empirical user study on Arabic-English CS, where we show the correlation between users' CS frequency and character traits. We use machine learning (ML) to validate the findings, informing and confirming existing theories. The predictive models were able to predict users' CS frequency with an accuracy higher than 55%, where travel experiences and personality traits played the biggest role in the modeling process.
MinkLoc3D-SI: 3D LiDAR place recognition with sparse convolutions, spherical coordinates, and intensity

The 3D LiDAR place recognition aims to estimate a coarse localization in a previously seen environment based on a single scan from a rotating 3D LiDAR sensor. The existing solutions to this problem include hand-crafted point cloud descriptors (e.g., ScanContext, M2DP, LiDAR IRIS) and deep learning-based solutions (e.g., PointNetVLAD, PCAN, LPD-Net, DAGC, MinkLoc3D), which are often only evaluated on accumulated 2D scans from the Oxford RobotCat dataset. We introduce MinkLoc3D-SI, a sparse convolution-based solution that utilizes spherical coordinates of 3D points and processes the intensity of 3D LiDAR measurements, improving the performance when a single 3D LiDAR scan is used. Our method integrates the improvements typical for hand-crafted descriptors (like ScanContext) with the most efficient 3D sparse convolutions (MinkLoc3D). Our experiments show improved results on single scans from 3D LiDARs (Usyd Campus dataset) and great generalization ability (KITTI dataset). Using intensity information on accumulated 2D scans (RobotCar Intensity dataset) improves the performance, even though spherical representation doesn't produce a noticeable improvement. As a result, MinkLoc3D-SI is suited for single scans obtained from a 3D LiDAR, making it applicable in autonomous vehicles.
DiVA: A Scalable, Interactive and Customizable Visual Analytics Platform for Information Diffusion on Large Networks

With an increasing outreach of digital platforms in our lives, researchers have taken a keen interest in studying different facets of social interactions. Analyzing the spread of information (aka diffusion) has brought forth multiple research areas such as modelling user engagement, determining emerging topics, forecasting the virality of online posts and predicting information cascades. Despite such ever-increasing interest, there remains a vacuum among easy-to-use interfaces for large-scale visualization of diffusion models. In this article, we introduce DiVA—Diffusion Visualization and Analysis, a tool that provides a scalable web interface and extendable APIs to analyze various diffusion trends on networks. DiVA uniquely offers support for simultaneous comparison of two competing diffusion models and even the comparison with the ground-truth results, which help develop a coherent understanding of real-world scenarios. Along with performing an exhaustive feature comparison and system evaluation of DiVA against publicly-available web interfaces for information diffusion, we conducted a user study to understand the strengths and limitations of DiVA. We noticed that evaluators had a seamless user experience, especially when analyzing diffusion on large networks.
Enumeration of three-quadrant walks via invariants: some diagonally symmetric models

Abstract In the past $20$ years, the enumeration of plane lattice walks confined to a convex cone—normalized into the first quadrant—has received a lot of attention, stimulated the development of several original approaches, and led to a rich collection of results. Most of these results deal with the nature of the associated generating function: for which models is it algebraic, D-finite, D-algebraic? By model, what we mean is a finite collection of allowed steps. More recently, similar questions have been raised for nonconvex cones, typically the three-quadrant cone $\mathcal {C} = \{ (i,j) : i \geq 0 \text { or } j \geq 0 \}$ . They turn out to be more difficult than their quadrant counterparts. In this paper, we investigate a collection of eight models in $\mathcal {C}$ , which can be seen as the first level of difficulty beyond quadrant problems. This collection consists of diagonally symmetric models in $\{-1, 0,1\}^2\setminus \{(-1,1), (1,-1)\}$ . Three of them are known not to be D-algebraic. We show that the remaining five can be solved in a uniform fashion using Tutte’s notion of invariants, which has already proved useful for some quadrant models. Three models are found to be algebraic, one is (only) D-finite, and the last one is (only) D-algebraic. We also solve in the same fashion the diagonal model $\{ \nearrow , \nwarrow , \swarrow , \searrow \}$ , which is D-finite. The three algebraic models are those of the Kreweras trilogy, $\mathcal S=\{\nearrow , \leftarrow , \downarrow \}$ , $\mathcal S^*=\{\rightarrow , \uparrow , \swarrow \}$ , and $\mathcal S\cup \mathcal S^*$ . Our solutions take similar forms for all six models. Roughly speaking, the square of the generating function of three-quadrant walks with steps in $\mathcal S$ is an explicit rational function in the quadrant generating function with steps in $\mathscr S:= \{(j-i,j): (i,j) \in \mathcal S\}$ . We derive various exact or asymptotic corollaries, including an explicit algebraic description of a positive harmonic function in $\mathcal C$ for the (reverses of the) five models that are at least D-finite.
Do Data-based Curricula Work?

Current state-of-the-art NLP systems use large neural networks that require extensive computational resources for training. Inspired by human knowledge acquisition, researchers have proposed curriculum learning - sequencing tasks (task-based curricula) or ordering and sampling the datasets (data-based curricula) that facilitate training. This work investigates the benefits of data-based curriculum learning for large language models such as BERT and T5. We experiment with various curricula based on complexity measures and different sampling strategies. Extensive experiments on several NLP tasks show that curricula based on various complexity measures rarely have any benefits, while random sampling performs either as well or better than curricula.
Mean-Square-Error-Based Secondary Source Placement in Sound Field Synthesis with Prior Information on Desired Field

A method of optimizing secondary source placement in sound field synthesis is proposed. Such an optimization method will be useful when the allowable placement region and available number of loudspeakers are limited. We formulate a mean-square-error-based cost function, incorporating the statistical properties of possible desired sound fields, for general linear-least-squares-based sound field synthesis methods, including pressure matching and (weighted) mode matching, whereas most of the current methods are applicable only to the pressure-matching method. An efficient greedy algorithm for minimizing the proposed cost function is also derived. Numerical experiments indicated that a high reproduction accuracy can be achieved by the placement optimized by the proposed method compared with the empirically used regular placement.
Towards an Ontology for Scenario Definition for the Assessment of Automated Vehicles: An Object-Oriented Framework

The development of new assessment methods for the performance of automated vehicles is essential to enable the deployment of automated driving technologies, due to the complex operational domain of automated vehicles. One contributing method is scenario-based assessment in which test cases are derived from real-world road traffic scenarios obtained from driving data. Given the complexity of the reality that is being modeled in these scenarios, it is a challenge to define a structure for capturing these scenarios. An intensional definition that provides a set of characteristics that are deemed to be both necessary and sufficient to qualify as a scenario assures that the scenarios constructed are both complete and intercomparable. In this article, we develop a comprehensive and operable definition of the notion of scenario while considering existing definitions in the literature. This is achieved by proposing an object-oriented framework in which scenarios and their building blocks are defined as classes of objects having attributes, methods, and relationships with other objects. The object-oriented approach promotes clarity, modularity, reusability, and encapsulation of the objects. We provide definitions and justifications of each of the terms. Furthermore, the framework is used to translate the terms in a coding language that is publicly available.
On Meshfree Collocation to Compute the Probability of Default under a Regime-Switching Synchronous-Jump Tempered Stable L\'{e}vy Model

In the paper [Hainaut, D. and Colwell, D.B. , {\rm A structural model for credit risk with switching processes and synchronous jumps}, The European Journal of Finance 22(11) (2016): 1040-1062], the authors exploit a synchronous-jump regime-switching model to compute the default probability of a publicly traded company. Here, we first generalize the proposed L\'{e}vy model to more general setting of tempered stable processes recently introduced into the finance literature. Based on the singularity of the resulting partial integro-differential operator, we propose a general framework based on strictly positive-definite functions to de-singularize the operator. We then analyze an efficient meshfree collocation method based on radial basis functions to approximate the solution of the corresponding system of partial integro-differential equations arising from the structural credit risk model. We show that under some regularity assumptions, our proposed method naturally de-sinularizes the problem in the tempered stable case. Numerical results of applying the method on some standard examples from the literature confirms the accuracy of our theoretical results and numerical algorithm.
Backstepping approach to controller designs of nonlinear systems based on L/sub G/V approach

In the backstepping approach, it would be significantly important from a view point of robustification to avoid the cancellation of system nonlinearities. For such ones, we take the two main versions: lean backstepping and L/sub G/V backstepping. In this paper, we discuss a possible fusion of lean backstepping and L/sub G/V backstepping, which may produce a systematic procedure to make a resultant controller relatively simple. Lastly, a design example is demonstrated to show the effectiveness of our fusion approach.
Optimization of Core Network Router for Telecommunication Exchange

The operation of Core Router is to restrict Network Broadcast to the LAN, to act as default gateway, to move data between different networks and to advertise loop free Path. The technology of Wifi, WiMAX (Worldwide Interoperability for Microwave Access), GPRS (General Packet Radio Services), EDGE (Enhanced Data Rates For GSM Evolution), EV-DO (Evolution Data Optimized) which are used for remote data access. A Router must be able to support multiple telecommunications interfaces of the highest speed in use in the core Internet and must be able to forward IP packets at full speed on all of them. It must also support the routing protocols being used in the core. In telephone system, Core Routers installed on the network are used as carriers to carry data from traffic sources to sinks. The optimization formulation based on Telecommunication network to obtain an optimal decision for the Core Router on whether to accept packets from a traffic source. This decision is made to maximize the reward of data delivery while the quality-of-service performance is guaranteed. From the performance evaluation, a Core Router with network optimization can achieve the highest reward while the maximum packet-blocking probability requirements met. The Core Router is a cell-site access platform specifically designed to optimize, aggregate and transport mixed-generation Radio Access Network (RAN) traffic. It is used at a cell site as part of an IP-RAN or Cell Site DCN solution. An IP RAN solution in which the Core Router extends IP connectivity to the cell site and Base Transceiver Station (BTS), through a Fast Ethernet interface to the BTS, the router provides bandwidth-efficient IP transport of voice and data bearer traffic, as well as maintenance, control and signaling traffic over the IP using traditional circuits.
Probabilistic Forecasts for Aviation Traffic at FAA's Commercial Terminals

Forecasting air travel in uncertain times is a challenging task. The effects of uncertainties accompanied with usual errors emanating from estimation of demand prevent deterministic forecasts from representing an uncertain future. The framework that is presented takes into account uncertainty for which the nature and sources are unknown. A parametric distribution fitting the past growth rates captures the probabilities associated with the outcomes representing the future uncertainty at the airports. This process combined with Monte Carlo simulation is used to generate probabilistic forecasts at the top 50 commercial airports in the United States. The methodology is illustrated by using Hartsfield–Jackson Atlanta International Airport in Georgia. Probabilistic forecasts provide an important tool for investment and personnel planning in uncertain times.
Inductor design using amorphous metal C-cores

As standards are developed that limit harmonic current levels in off-line equipment, the need grows for solutions that do not increase the cost, size, and weight of power-factor-correction components. A method for correcting power factor that helps reduce cost, size, and weight involves using amorphous metal C-cores as the inductive element of power-factor-correction devices (e.g. a continuous-mode boost pre-regulator). Inductive components made with amorphous metals are smaller and lighter than those made with traditional materials. In this article we discuss power factor, the establishment of IEC 555 for limiting harmonic currents, and the benefits of using amorphous metal C-cores over traditional materials. A step-by-step procedure is then presented for designing the inductive element of a continuous-mode boost pre-regulator using amorphous metal C-cores for limiting harmonic currents.
Disease-modifying immune-modulatory effects of the N-163 strain of Aureobasidium pullulans-produced 1,3-1,6 Beta glucans in young boys with Duchenne muscular dystrophy: Results of an open-label, prospective, randomized, comparative clinical study

Background: Duchenne muscular dystrophy (DMD) is an inherited neuromuscular disorder causing progressive muscle weakness and premature death. Steroids remain the mainstream approach for supportive care but have side effects; other targeted therapies and gene therapies are also being developed. As there is limited evidence on the use of disease modifying nutritional supplement adjuncts in DMD, this pilot trial is to evaluate the effects of supplementation of Aureobasidium pullulans-derived 1,3 1,6 beta glucan from the N163 strain in young patients with DMD. Methods: Twenty-seven patients with Duchenne muscular dystrophy (DMD), nine in the control arm (undergoing conventional therapies) participated. The patients were divided into groups: those not administered steroids (Steroid negative) (n = 5), those administered steroids (Steroid positive) (n = 4), and 18 in the treatment arm (N163 beta glucan supplement along with conventional therapies; N-163 Steroid negative and N-163 Steroid positive); they participated in the study for 45 days. Assessments of muscle function, disease status, and levels of IL6, IL13, TGF beta;, creatinine kinase (CK), titin, TNF Alpha;, haptoglobin, and dystrophin in the blood and myoglobin in the urine were performed at baseline and at the end of the study. Results: IL6 showed a significant decrease in the N163 Steroid negative group, from a baseline value of 7.2 pg/ml to 2.7 pg/ml. IL13 decreased in both treatment groups, from 157.76 pg/ml to 114.08 pg/ml (N-163 Steroid negative) and from 289.56 pg/ml to 255.56 pg/ml (N-163 Steroid positive). TGF beta levels showed a significant decrease in the N163 Steroid negative group, from a baseline value of 3302 ng/ml to 1325.66 ng/ml post intervention. Dystrophin levels increased by up to 32% in both Steroid positive and negative groups. Medical research council (MRC) grading showed muscle strength improvement in 12 out of 18 patients (67%) in the treatment group and four out of nine (44%) subjects in the control group. Conclusion: Supplementation with the N163 beta glucan food supplement produced disease-modifying beneficial effects: a significant decrease in inflammation and fibrosis markers, increase in dystrophin and improvement in muscle strength in DMD subjects over 45 days, thus making this a potential adjunct treatment for DMD after validation. A longer duration of follow up and further research on the mechanism of action and commonalities with other diseases provoked by hyperactive inflammation and/or fibrosis may pave the way for their extended applications in other dystrophinopathies and neuroinflammatory diseases.