Miracle-dz/UniFAR-FTUs huggingface dataset:
Pedestrian Detection: Domain Generalization, CNNs, Transformers and Beyond

Pedestrian detection is the cornerstone of many vision based applications, starting from object tracking to video surveillance and more recently, autonomous driving. With the rapid development of deep learning in object detection, pedestrian detection has achieved very good performance in traditional single-dataset training and evaluation setting. However, in this study on generalizable pedestrian detectors, we show that, current pedestrian detectors poorly handle even small domain shifts in cross-dataset evaluation. We attribute the limited generalization to two main factors, the method and the current sources of data. Regarding the method, we illustrate that biasness present in the design choices (e.g anchor settings) of current pedestrian detectors are the main contributing factor to the limited generalization. Most modern pedestrian detectors are tailored towards target dataset, where they do achieve high performance in traditional single training and testing pipeline, but suffer a degrade in performance when evaluated through cross-dataset evaluation. Consequently, a general object detector performs better in cross-dataset evaluation compared with state of the art pedestrian detectors, due to its generic design. As for the data, we show that the autonomous driving benchmarks are monotonous in nature, that is, they are not diverse in scenarios and dense in pedestrians. Therefore, benchmarks curated by crawling the web (which contain diverse and dense scenarios), are an efficient source of pre-training for providing a more robust representation. Accordingly, we propose a progressive fine-tuning strategy which improves generalization. Code and models can accessed at https://github.com/hasanirtiza/Pedestron.
Investigating the Origin of Observed Central Dips in Radial Metallicity Profiles

Radial metallicity trends provide a key indicator of physical processes such as star formation and radial gas migration within a galaxy. Large IFU surveys allow for detailed studies of these radial variations, with recent observations detecting central dips in the metallicity, which may trace the impact of various evolutionary processes. However, the origin of these dips has not been conclusively determined, with suggestions that they may be diagnostic dependent. In this paper, we use the SDSS-IV MaNGA survey to investigate whether the observed dips represent genuine decreases in the central metallicity, or if they could be an artefact of the diagnostic used. Using a sub-sample of 758 local star-forming galaxies at low inclinations, we investigate in detail the impact of using different strong line diagnostics on the shapes of the returned profiles, and the prevalence of dips. We find no clear evidence of the dips being caused by changing values of the ionisation parameter within galaxies. To investigate physical causes, we explore both global and spatially-resolved parameters, finding that galaxies exhibiting central dips in the O3N2 metallicity profile have on average lower HαEW values out to R/Re ∼ 1.5, and higher values of DN (4000) in the central regions. We additionally find a higher prevalence of dips in galaxies with high stellar mass, and lower values of global specific star formation rate, suggesting a possible link to central quenching. Nevertheless, these results are dependent on the diagnostic used, suggesting caution should be taken when interpreting observed features in galaxy metallicity gradients.
Low-Complexity Codebook Design for SCMA-Based Visible Light Communication

Sparse code multiple access (SCMA), as a code-domain non-orthogonal multiple access (NOMA) scheme, has received considerable research attention for enabling massive connectivity in future wireless communication systems. In this paper, we present a novel codebook (CB) design for SCMA based visible light communication (VLC) system, which suffers from shot noise. In particular, we introduce an iterative algorithm for designing and optimizing CB by considering the impact of shot noise at the VLC receiver. Based on the proposed CB, we derive and analyze the theoretical bit error rate (BER) expression for the resultant SCMA-VLC system. The simulation results show that our proposed CBs outperform CBs in the existing literature for different loading factors with much less complexity. Further, the derived analytical BER expression well aligns with simulated results, especially in high signal power regions.
Plenary lecture 6: neuro-control of vibration for a maglev vehicle traveling at high speeds

This paper is intended to present a neuro-PI (proportional-integral) controller for tuning the dynamic response of a running maglev vehicle at high speeds. The maglev vehicle is simulated as a rigid car body supported by a rigid magnetic bogie-set with a uniformly distributed spring-dashpot system, in which the electromagnetic force is controlled by an on-board PI controller. Considering the motion-dependent nature of electromagnetic force working in a maglev system, this study presents an iterative approach to compute the dynamic response of the running maglev vehicle system based on the Newmark method. The proposed neuro-PI controller is trained using back propagation neural network in such a way that its PI gains are correlated to the generated dataset of moving speeds and the maximum vertical accelerations of maglev oscillators including the midpoint and two ends of the rigid car body. Numerical simulations demonstrate that a trained neuro-PI controller has the ability to control the acceleration amplitude for a running maglev vehicle within an allowable region of restricted acceleration.
The State of Aerial Surveillance: A Survey

The rapid emergence of airborne platforms and imaging sensors are enabling new forms of aerial surveillance due to their unprecedented advantages in scale, mobility, deployment and covert observation capabilities. This paper provides a comprehensive overview of human-centric aerial surveillance tasks from a computer vision and pattern recognition perspective. It aims to provide readers with an in-depth systematic review and technical analysis of the current state of aerial surveillance tasks using drones, UAVs and other airborne platforms. The main object of interest is humans, where single or multiple subjects are to be detected, identified, tracked, re-identified and have their behavior analyzed. More specifically, for each of these four tasks, we first discuss unique challenges in performing these tasks in an aerial setting compared to a ground-based setting. We then review and analyze the aerial datasets publicly available for each task, and delve deep into the approaches in the aerial literature and investigate how they presently address the aerial challenges. We conclude the paper with discussion on the missing gaps and open research questions to inform future research avenues.
Provable Clustering of a Union of Linear Manifolds Using Optimal Directions

This paper focuses on the Matrix Factorization based Clustering (MFC) method which is one of the few closed form algorithms for the subspace clustering problem. Despite being simple, closed-form, and computation-efficient, MFC can outperform the other sophisticated subspace clustering methods in many challenging scenarios. We reveal the connection between MFC and the Innovation Pursuit (iPursuit) algorithm which was shown to be able to outperform the other spectral clustering based methods with a notable margin especially when the span of clusters are close. A novel theoretical study is presented which sheds light on the key performance factors of both algorithms (MFC/iPursuit) and it is shown that both algorithms can be robust to notable intersections between the span of clusters. Importantly, in contrast to the theoretical guarantees of other algorithms which emphasized on the distance between the subspaces as the key performance factor and without making the innovation assumption, it is shown that the performance of MFC/iPursuit mainly depends on the distance between the innovative components of the clusters.
The Relevance of Ram Pressure Stripping for the Evolution of Blue Cluster Galaxies as Seen at Optical Wavelengths

Ram pressure stripping is one of the most efficient mechanisms able to affect the gas reservoir in cluster galaxies, and in the last decades many studies have characterized the properties of stripped galaxies. A definite census of the importance of this process in local clusters is still missing, though. Here, we characterize the fraction of galaxies showing signs of stripping at optical wavelengths, using the data of 66 clusters from the WINGS and OMEGAWINGS surveys. We focus on the infalling galaxy population, and hence only consider blue, bright (B < 18.2), late-type, spectroscopically confirmed cluster members within two virial radii. In addition to “traditional” stripping candidates (SC)—i.e., galaxies showing unilateral debris and tails—we also consider unwinding galaxies (UG) as potentially stripped galaxies. Recent work has indeed unveiled a connection between unwinding features and ram pressure stripping, and even though only integral field studies can inform on how often these features are indeed due to ram pressure, it is important to include them in the global census. We performed a visual inspection of B-band images, and here we release a catalog of 143 UG. SC and UG each represent ∼15%–20% of the inspected sample. If we make the assumption that they both are undergoing ram pressure stripping, we can conclude that, at any given time in the low-z universe, about 35% of the infalling cluster population show signs of stripping in their morphology at optical wavelengths. These fractions depend on color, mass, and morphology, and little on clustercentric distance. Making some rough assumptions regarding the duration of the tail visibility and the time that cluster galaxies can maintain blue colors, we infer that almost all bright blue late-type cluster galaxies undergo a stripping phase during their life, boosting the importance of ram pressure stripping in cluster galaxy evolution.
NeuroPack: An Algorithm-Level Python-Based Simulator for Memristor-Empowered Neuro-Inspired Computing

Emerging two-terminal nanoscale memory devices, known as memristors, have demonstrated great potential for implementing energy-efficient neuro-inspired computing architectures over the past decade. As a result, a wide range of technologies have been developed that, in turn, are described via distinct empirical models. This diversity of technologies requires the establishment of versatile tools that can enable designers to translate memristors’ attributes in novel neuro-inspired topologies. In this study, we present NeuroPack, a modular, algorithm-level Python-based simulation platform that can support studies of memristor neuro-inspired architectures for performing online learning or offline classification. The NeuroPack environment is designed with versatility being central, allowing the user to choose from a variety of neuron models, learning rules, and memristor models. Its hierarchical structure empowers NeuroPack to predict any memristor state changes and the corresponding neural network behavior across a variety of design decisions and user parameter options. The use of NeuroPack is demonstrated herein via an application example of performing handwritten digit classification with the MNIST dataset and an existing empirical model for metal-oxide memristors.
Privacy-aware Distributed Day-ahead Dispatch of Multiple Distribution Grids hosting Stochastic Resources: A Multi-Grid Dispatch Framework

—This work presents a framework to compute the aggregated day-ahead dispatch plans of multiple and interconnected distribution grids operating at different voltage levels. Speciﬁcally, the proposed framework optimizes the dispatch plan of an upstream medium voltage (MV) grid accounting for the ﬂexibility offered by downstream low voltage (LV) grids and the knowledge of the uncertainties of the stochastic resources. The framework considers grid, i.e., operational limits on the nodal voltages, lines, and transformer capacity using a linearized grid model, and controllable resources’ constraints. The problem is formulated as a stochastic-optimization scheme considering uncertainty on stochastic power generation and demands and the voltage imposed by the upstream grid. The problem is solved by a distributed optimization method relying on Alternating Direction Method of Multipliers (ADMM) that splits the main problem into one aggregator problem solved at the MV-grid level and several local problems solved at the MV-connected-controllable-resources and LV-grid levels. The use of distributed optimization enables privacy-aware dispatch computation where the centralized aggregator is agnostic of the parameters of the participating resources and downstream grids. The framework is validated for interconnected CIGRE medium-and low-voltage networks hosting heterogeneous stochastic and controllable resources.
Arithmetic Network Coding for Secret Sum Computation

We consider a network coding problem where the destination wants to recover the sum of the signals (Gaussian random variables or random finite field elements) at all the source nodes, but the sum must be kept secret from an eavesdropper that can wiretap on a subset of edges. This setting arises naturally in sensor networks, where the secrecy of the sum of the signals (e.g. weights, gradients) may be desired. While the case for finite field can be solved, the case for Gaussian random variables is surprisingly difficult. We give a simple conjecture on the necessary and sufficient condition under which such secret computation is possible for the Gaussian case, and prove the conjecture when the number of wiretapped edges is at most 2.
Motion compensated multi-wavelength digital holography

A holography imaging system includes a first laser, a second laser, a transmitter optical system, a receiver optical system, and a detector array. The first laser has a constant frequency, and the second laser has a non-constant frequency. The transmitter optical system can illuminate a target simultaneously using portions of the first and second laser signals. The receiver optical system can focus a returned light onto the detector array. A first and second illumination point sources can direct portions of the first and second laser signals onto the detector array. The first and second illumination point sources are located in-plane with a pupil of the receiver optical system. The system can detect simultaneously holograms formed on the detector array based on the returned light and the portions of the first and second laser signals directed by the first and second illumination point sources.
A Survey of Passive Sensing in the Workplace

As new technology inches into every aspect of our lives, there is no place more likely to dramatically change in the future than the workplace. New passive sensing technology is emerging capable of assessing human behavior with the goal of promoting better cognitive and physical capabilities at work. In this article, we survey recent research on the use of passive sensing in the workplace to assess wellbeing and productivity of the workforce. We also discuss open problems and future directions related to passive sensing in the future workplace.
A fall alert system with prior-fall activity identification

Falling, especially in the elderly, is a critical issue to care for and surveil. There have been many studies focusing on fall detection. However, from our survey, there is still no research indicating the prior-fall activities, which we believe that they have a strong correlation with the intensity of the fall. The purpose of this research is to develop a fall alert system that also identifies prior-fall activities. First, we want to find a suitable location to attach a sensor to the body. We created multiple-spot on-body devices to collect various activity data. We used that dataset to train 5 different classification models. We selected the XGBoost classification model for detecting a prior-fall activity and the chest location for use in fall detection from a comparison of the detection accuracy. We then tested 3 existing fall detection threshold algorithms to detect fall and fall to their knees first, and selected the 3-phase threshold algorithm of Chaitep and Chawachat [3] in our system. From the experiment, we found that the fall detection accuracy is 88.91%, the fall to their knees first detection accuracy is 91.25%, 2 and the average accuracy of detection of prior-fall activities is 86.25%. Although we use an activity dataset of young to middle-aged adults (18-49 years), we are confident that this system can be developed to monitor activities before the fall, especially in the elderly, so that caretakers can better manage the situation.
A Rigorous Proof of the Capacity of Time-Varying MIMO Rayleigh Fading Channels

We investigate the problem of message transmission over time-varying single-user multiple-input multiple-output (MIMO) Rayleigh fading channels with average power constraint and with complete channel state information available at the receiver side (CSIR). To describe the channel variations over the time, we consider a ﬁrst-order Gauss-Markov model. We completely solve the problem by giving a single-letter characterization of the channel capacity in closed form and by providing a rigorous proof of it
Validating the University of Delaware's precipitation and temperature database for northern South America

Vast sections of the planet face either a dearth of ground-based weather stations or are hampered by the poor quality of those in service. In response, researchers are forced to turn to climate field databases, as they constitute a source of reliable information for local studies. Insofar as the Amazon region, these databases prove to be valuable given their open-access platform and the fact that this expansive region possesses few quality stations (coupled with insufficient temporal coverage). However, before basing research on such archives, this information should be compared against in situ station measurements. Then, the present study assesses the validity of temperature and precipitation information furnished by University of Delaware’s database (UD-ATP) by means of a comparison with the open-access information available from Climate Explorer project (CLIMEXP). Results show that UD-ATP database offers better precipitation data representation, especially on Brazil, which is perhaps the effect of higher-quality and larger-quantity observed data.
Traffic event description based on Twitter data using Unsupervised Learning Methods for Indian road conditions

Short abstract: Non-recurrent and unpredictable traffic events directly influence road traffic conditions. There is a need for dynamic monitoring and prediction of these unpredictable events to improve road network management. The problem with the existing traditional methods (flow or speed studies) is that the coverage of many Indian roads is very sparse and reproducible methods to identify and describe the events are not available. Addition of some other form of data is essential to help with this problem. This could be real-time speed monitoring data like Google Maps, Waze, etc. or social data like Twitter, Facebook, etc. In this paper, an unsupervised learning model is used to perform effective tweet classification for enhancing Indian traffic data. The model uses word-embeddings to calculate semantic similarity and achieves a test score of 94.7%.
Modification of the Laminar-to-Turbulent Transition on a Flat Plate Using DBD Plasma Actuator

Active flow control by plasma actuators, usually ca lled ElectroHydroDynamic actuators, is currently under investigation in order to modify and control external flows. One of these actuators consists in using a surface dielectric ba rrier discharge by creating a non-thermal plasma at the dielectric surface. The plasma induce s a low-velocity airflow, the “ionic wind”, which adds momentum close to the wall. In this study, the ability of such an actuator to delay or promote the transition of a boundary layer developed along a flat plate is studied experimentally.
Dynamical Effects of Electromagnetic Flux on Chialvo Neuron Map: Nodal and Network Behaviors

We consider the dynamical effects of electromagnetic flux on the discrete Chialvo neuron model. It is shown that the model can exhibit rich dynamical behaviors such as multistability, firing patterns, antimonotonicity, closed invariant curves, various routes to chaos, and fingered chaotic attractors. The system enters a chaos regime via period-doubling cascades, reverse period-doubling route, antimonotonicity, and via a closed invariant curve to chaos. The results were confirmed using the techniques of bifurcation diagrams, Lyapunov exponent diagram, phase portraits, basins of attraction, and numerical continuation of bifurcations. Different global bifurcations are also shown to exist via numerical continuation. After understanding a single neuron model, a network of Chialvo neurons is explored. A ring-star network of Chialvo neurons is considered and different dynamical regimes such as synchronous, asynchronous, and chimera states are revealed. Different continuous and piecewise continuous wavy patterns were also found during the simulations for negative coupling strengths.
Projection: A Mixed-Initiative Research Process

Communication of dense information between humans and machines is relatively low bandwidth. Many modern search and recommender systems operate as machine learning black boxes, giving little insight as to how they represent information or why they take certain actions. We present Projection, a mixed-initiative interface that aims to increase the bandwidth of communication between humans and machines throughout the research process. The interface supports adding context to searches and visualizing information in multiple dimensions with techniques such as hierarchical clustering and spatial projections. Potential customers have shown interest in the application integrating their research outlining and search processes, enabling them to structure their searches in hierarchies, and helping them visualize related spaces of knowledge.
PocketNN: Integer-only Training and Inference of Neural Networks via Direct Feedback Alignment and Pocket Activations in Pure C++

Standard deep learning algorithms are implemented using floating-point real numbers. This presents an obstacle for implementing them on low-end devices which may not have dedicated floating-point units (FPUs). As a result, researchers in tinyML have considered machine learning algorithms that can train and run a deep neural network (DNN) on a low-end device using integer operations only. In this paper we propose PocketNN, a light and self-contained proof-of-concept framework in pure C++ for the training and inference of DNNs using only integers. Unlike other approaches, PocketNN directly operates on integers without requiring any explicit quantization algorithms or customized fixed-point formats. This was made possible by pocket activations, which are a family of activation functions devised for integer-only DNNs, and an emerging DNN training algorithm called direct feedback alignment (DFA). Unlike the standard backpropagation (BP), DFA trains each layer independently, thus avoiding integer overflow which is a key problem when using BP with integer-only operations. We used PocketNN to train some DNNs on two well-known datasets, MNIST and Fashion-MNIST. Our experiments show that the DNNs trained with our PocketNN achieved 96.98% and 87.7% accuracies on MNIST and Fashion-MNIST datasets, respectively. The accuracies are very close to the equivalent DNNs trained using BP with floating-point real number operations, such that accuracy degradations were just 1.02%p and 2.09%p, respectively. Finally, our PocketNN has high compatibility and portability for low-end devices as it is open source and implemented in pure C++ without any dependencies.