Miracle-dz/UniFAR-FTUs huggingface dataset:
Parametric Distributionally Robust Optimisation Models for Budgeted Multi-period Newsvendor Problems

In this paper, we consider a static, multi-period newsvendor model under a budget constraint. In the case where the true demand distribution is known, we develop a heuristic algorithm to solve the problem. By comparing this algorithm with off-the-shelf solvers, we show that it generates near-optimal solutions in a short time. We then consider a scenario in which limited information on the demand distribution is available. It is assumed, however, that the true demand distribution lies within some given family of distributions and that samples can be obtained from it. We consider the cases of normal and Poisson demands. For each case, we show that using maximum likelihood estimates in place of the true parameters can lead to poor estimates of the true cost associated with an order quantity. Hence, we make use of likelihood inference to develop confidence sets for the true parameters. These are used as ambiguity sets in a distributionally robust model, where we enforce that the worst-case distribution lies in the same family as the true distribution. We solve these models by discretising the ambiguity set and reformulating them as piecewise linear models. We show that these models quickly become large as the ambiguity set grows, resulting in long computation times. To overcome this, we propose a heuristic cutting surface algorithm that exploits theoretical properties of the objective function to reduce the size of the ambiguity set. We illustrate that our cutting surface algorithm solves orders of magnitude faster than the piecewise linear model, while generating very near-optimal solutions.
Privacy-Preserving Load Forecasting via Personalized Model Obfuscation

The widespread adoption of smart meters provides access to detailed and localized load consumption data, suitable for training building-level load forecasting models. To mitigate privacy concerns stemming from model-induced data leakage, federated learning (FL) has been proposed. This paper addresses the performance challenges of short-term load forecasting models trained with FL on heterogeneous data, emphasizing privacy preservation through model obfuscation. Our proposed algorithm, Privacy Preserving Federated Learning (PPFL), incorporates personalization layers for localized training at each smart meter. Additionally, we employ a differentially private mechanism to safeguard against data leakage from shared layers. Simulations on the NREL ComStock dataset corroborate the effectiveness of our approach.
Domain Adaptive Imitation Learning with Visual Observation

In this paper, we consider domain-adaptive imitation learning with visual observation, where an agent in a target domain learns to perform a task by observing expert demonstrations in a source domain. Domain adaptive imitation learning arises in practical scenarios where a robot, receiving visual sensory data, needs to mimic movements by visually observing other robots from different angles or observing robots of different shapes. To overcome the domain shift in cross-domain imitation learning with visual observation, we propose a novel framework for extracting domain-independent behavioral features from input observations that can be used to train the learner, based on dual feature extraction and image reconstruction. Empirical results demonstrate that our approach outperforms previous algorithms for imitation learning from visual observation with domain shift.
Polarization compensation methods for quantum communication networks

The information-theoretic unconditional security offered by quantum key distribution has spurred the development of larger quantum communication networks. However, as these networks grow so does the strong need to reduce complexity and overheads. Polarization based entanglement distribution networks are a promising approach due to their scalability and lack of trusted nodes. Nevertheless, they are only viable if the birefringence of all optical distribution fibres in the network is compensated to preserve the polarization based quantum state. The brute force approach would require a few hundred fibre polarization controllers for even a moderately sized network. Instead, we propose and investigate four different methods of polarization compensation. We compare them based on complexity, effort, level of disruption to network operations and performance. We have demonstrated four polarization compensation methods that can be used in quantum networks. The promising results of their practical implementation have confirmed that they can be a powerful tool for future large-scale QKD networks. The process of polarization compensation with minimization of QBER can be done while the network is active and utilizing only photons from the entangled photon source, which is an important comparative advantage. Implementation of motorized controllers with the QBER method could further reduce the time required for the polarization compensation process and automate it. We did not find an efficient automation solution for the QBER based method other than a simple grid search algorithm and consequently did not implement it. Future work could investigate Machine Learning algorithms to help compensate birefringence in the optical fibres based on the QBER value. Importantly, our experiment shows that each logical link within the entanglement distribution layer of the quantum network can be polarization compensated independently and we do not need to compensate each wavelength over each physical link in the network.
A Review of the In-Network Computing and Its Role in the Edge-Cloud Continuum

Future networks are anticipated to enable exciting applications and industrial services ranging from Multisensory Extended Reality to Holographic and Haptic communication. These services are accompanied by high bandwidth requirements and/or require low latency and low reliability, which leads to the need for scarce and expensive resources. Cloud and edge computing offer different functionalities to these applications that require communication, computing, and caching (3C) resources working collectively. Hence, a paradigm shift is necessary to enable the joint management of the 3Cs in the edge-cloud continuum. We argue that In-Network Computing (INC) is the missing element that completes the edge-cloud continuum. This paper provides a detailed analysis of the driving use-cases, explores the synergy between INC and 3C, and emphasizes the crucial role of INC. A discussion on the opportunities and challenges posed by INC is held from various perspectives, including hardware implementation, architectural design, and regulatory and commercial aspects.
Enhancing IoT Security via Automatic Network Traffic Analysis: The Transition from Machine Learning to Deep Learning

This work provides a comparative analysis illustrating how Deep Learning (DL) surpasses Machine Learning (ML) in addressing tasks within Internet of Things (IoT), such as attack classification and device-type identification. Our approach involves training and evaluating a DL model using a range of diverse IoT-related datasets, allowing us to gain valuable insights into how adaptable and practical these models can be when confronted with various IoT configurations. We initially convert the unstructured network traffic data from IoT networks, stored in PCAP files, into images by processing the packet data. This conversion process adapts the data to meet the criteria of DL classification methods. The experiments showcase the ability of DL to surpass the constraints tied to manually engineered features, achieving superior results in attack detection and maintaining comparable outcomes in device-type identification. Additionally, a notable feature extraction time difference becomes evident in the experiments: traditional methods require around 29 milliseconds per data packet, while DL accomplishes the same task in just 2.9 milliseconds. The significant time gap, DL's superior performance, and the recognized limitations of manually engineered features, presents a compelling call to action within the IoT community. This encourages us to shift from exploring new IoT features for each dataset to addressing the challenges of integrating DL into IoT, making it a more efficient solution for real-world IoT scenarios.
Machine Learning for Actionable Warning Identification: A Comprehensive Survey

Actionable Warning Identification (AWI) plays a crucial role in improving the usability of static code analyzers. With recent advances in Machine Learning (ML), various approaches have been proposed to incorporate ML techniques into AWI. These ML-based AWI approaches, benefiting from ML's strong ability to learn subtle and previously unseen patterns from historical data, have demonstrated superior performance. However, a comprehensive overview of these approaches is missing, which could hinder researchers/practitioners from understanding the current process and discovering potential for future improvement in the ML-based AWI community. In this paper, we systematically review the state-of-the-art ML-based AWI approaches. First, we employ a meticulous survey methodology and gather 50 primary studies from 2000/01/01 to 2023/09/01. Then, we outline the typical ML-based AWI workflow, including warning dataset preparation, preprocessing, AWI model construction, and evaluation stages. In such a workflow, we categorize ML-based AWI approaches based on the warning output format. Besides, we analyze the techniques used in each stage, along with their strengths, weaknesses, and distribution. Finally, we provide practical research directions for future ML-based AWI approaches, focusing on aspects like data improvement (e.g., enhancing the warning labeling strategy) and model exploration (e.g., exploring large language models for AWI).
Partition-based K-space Synthesis for Multi-contrast Parallel Imaging

Multi-contrast magnetic resonance imaging is a significant and essential medical imaging technique.However, multi-contrast imaging has longer acquisition time and is easy to cause motion artifacts. In particular, the acquisition time for a T2-weighted image is prolonged due to its longer repetition time (TR). On the contrary, T1-weighted image has a shorter TR. Therefore,utilizing complementary information across T1 and T2-weighted image is a way to decrease the overall imaging time. Previous T1-assisted T2 reconstruction methods have mostly focused on image domain using whole-based image fusion approaches. The image domain reconstruction method has the defects of high computational complexity and limited flexibility. To address this issue, we propose a novel multi-contrast imaging method called partition-based k-space synthesis (PKS) which can achieve super reconstruction quality of T2-weighted image by feature fusion. Concretely, we first decompose fully-sampled T1 k-space data and under-sampled T2 k-space data into two sub-data, separately. Then two new objects are constructed by combining the two sub-T1/T2 data. After that, the two new objects as the whole data to realize the reconstruction of T2-weighted image. Finally, the objective T2 is synthesized by extracting the sub-T2 data of each part. Experimental results showed that our combined technique can achieve comparable or better results than using traditional k-space parallel imaging(SAKE) that processes each contrast independently.
Who is leading in AI? An analysis of industry AI research

AI research is increasingly industry-driven, making it crucial to understand company contributions to this field. We compare leading AI companies by research publications, citations, size of training runs, and contributions to algorithmic innovations. Our analysis reveals the substantial role played by Google, OpenAI and Meta. We find that these three companies have been responsible for some of the largest training runs, developed a large fraction of the algorithmic innovations that underpin large language models, and led in various metrics of citation impact. In contrast, leading Chinese companies such as Tencent and Baidu had a lower impact on many of these metrics compared to US counterparts. We observe many industry labs are pursuing large training runs, and that training runs from relative newcomers -- such as OpenAI and Anthropic -- have matched or surpassed those of long-standing incumbents such as Google. The data reveals a diverse ecosystem of companies steering AI progress, though US labs such as Google, OpenAI and Meta lead across critical metrics.
Learning Unorthogonalized Matrices for Rotation Estimation

Estimating 3D rotations is a common procedure for 3D computer vision. The accuracy depends heavily on the rotation representation. One form of representation -- rotation matrices -- is popular due to its continuity, especially for pose estimation tasks. The learning process usually incorporates orthogonalization to ensure orthonormal matrices. Our work reveals, through gradient analysis, that common orthogonalization procedures based on the Gram-Schmidt process and singular value decomposition will slow down training efficiency. To this end, we advocate removing orthogonalization from the learning process and learning unorthogonalized `Pseudo' Rotation Matrices (PRoM). An optimization analysis shows that PRoM converges faster and to a better solution. By replacing the orthogonalization incorporated representation with our proposed PRoM in various rotation-related tasks, we achieve state-of-the-art results on large-scale benchmarks for human pose estimation.
Modeling of Plasmonic and Polaritonic Effects in Photocurrent Nanoscopy

We present a basic framework for modeling collective mode effects in photocurrent measurements performed on two-dimensional materials using nano-optical scanned probes. We consider photothermal, photovoltaic, and bolometric contributions to the photocurrent. We show that any one of these can dominate depending on frequency, temperature, applied bias, and sample geometry. Our model is able to account for periodic spatial oscillations (fringes) of the photocurrent observed near sample edges or inhomogeneities. For the case of a non-absorbing substrate, we find a direct relation between the spectra measured by the photocurrent nanoscopy and its parental scanning technique, near-field optical microscopy.
CompuCell3D Model of Cell Migration Reproduces Chemotaxis

Chemotaxis combines three processes: directional sensing, polarity reorientation and migration. Directed migration plays an important role in immune response, metastasis, wound healing and development. To describe chemotaxis, we extend a previously published computational model of a 3D single cell, that presents three compartments (lamellipodium, nucleus and cytoplasm), whose migration on a flat surface quantitatively describes experiments. The simulation is built in the framework of CompuCell3D, an environment based on the Cellular Potts Model. In our extension, we treat chemotaxis as a compound process rather than a response to a potential force. We propose robust protocols to measure cell persistence, drift speed, terminal speed, chemotactic efficiency, taxis time, and we analyse cell migration dynamics in the cell reference frame from position and polarization recordings through time. Our metrics can be applied to experimental results and allow quantitative comparison between simulations and experiments. We found that our simulated cells exhibit a trade-off between polarization stability and chemotactic efficiency. Specifically, we found that cells with lower protrusion forces and smaller lamellipodia exhibit an increased ability to undergo chemotaxis. We also noticed no significant change in cell movement due to external chemical gradient when analysing cell displacement in the cell reference frame. Our results demonstrate the importance of measuring cell polarity throughout the entire cell trajectory, and treating velocity quantities carefully when cell movement is diffusive at short time intervals. The simulation we developed is adequate to the development of new measurement protocols, and it helps paving the way to more complex multicellular simulations to model collective migration and their interaction with external fields, which are under development on this date.
Spectroastrometry and Imaging Science with Photonic Lanterns on Extremely Large Telescopes

Photonic lanterns (PLs) are tapered waveguides that gradually transition from a multi-mode fiber geometry to a bundle of single-mode fibers. In astronomical applications, PLs can efficiently couple multi-mode telescope light into a multi-mode fiber entrance and convert it into multiple single-mode beams. The output beams are highly stable and suitable for feeding into high-resolution spectrographs or photonic chip beam combiners. For instance, by using relative intensities in the output cores as a function of wavelength, PLs can enable spectroastrometry. In addition, by interfering beams in the output cores with a beam combiner in the backend, PLs can be used for high-throughput interferometric imaging. When used on an Extremely Large Telescope (ELT), with its increased sensitivity and angular resolution, the imaging and spectroastrometric capabilities of PLs will be extended to higher contrast and smaller angular scales. We study the potential spectroastrometry and imaging science cases of PLs on ELTs, including study of exomoons, broad-line regions of quasars, and inner circumstellar disks.
Learning domain-invariant classifiers for infant cry sounds

The issue of domain shift remains a problematic phenomenon in most real-world datasets and clinical audio is no exception. In this work, we study the nature of domain shift in a clinical database of infant cry sounds acquired across different geographies. We find that though the pitches of infant cries are similarly distributed regardless of the place of birth, other characteristics introduce peculiar biases into the data. We explore methodologies for mitigating the impact of domain shift in a model for identifying neurological injury from cry sounds. We adapt unsupervised domain adaptation methods from computer vision which learn an audio representation that is domain-invariant to hospitals and is task discriminative. We also propose a new approach, target noise injection (TNI), for unsupervised domain adaptation which requires neither labels nor training data from the target domain. Our best-performing model significantly improves target accuracy by 7.2%, without negatively affecting the source domain.
ChebNet: Efficient and Stable Constructions of Deep Neural Networks with Rectified Power Units via Chebyshev Approximations

In a previous study [B. Li, S. Tang and H. Yu, Commun. Comput. Phy. 27(2):379-411, 2020], it is shown that deep neural networks built with rectified power units (RePU) as activation functions can give better approximation for sufficient smooth functions than those built with rectified linear units, by converting polynomial approximations using power series into deep neural networks with optimal complexity and no approximation error. However, in practice, power series approximations are not easy to obtain due to the associated stability issue. In this paper, we propose a new and more stable way to construct RePU deep neural networks based on Chebyshev polynomial approximations. By using a hierarchical structure of Chebyshev polynomial approximation in frequency domain, we obtain efficient and stable deep neural network construction, which we call ChebNet. The approximation of smooth functions by ChebNets is no worse than the approximation by deep RePU nets using power series. On the same time, ChebNets are much more stable. Numerical results show that the constructed ChebNets can be further fine-tuned to obtain much better results than those obtained by tuning deep RePU nets constructed by power series approach. As spectral accuracy is hard to obtain by direct training of deep neural networks, ChebNets provide a practical way to obtain spectral accuracy, it is expected to be useful in real applications that require efficient approximations of smooth functions.
Photo-acoustic spectroscopy using a quantum cascade laser (QCL) for analysis of ammonia in water solutions

Ammonia (NH$_3$) toxicity, stemming from nitrification, can adversely affect aquatic life and influence the taste and odor of drinking water. This underscores the necessity for highly responsive and accurate sensors to continuously monitor NH$_3$ levels in water, especially in complex environments where reliable sensors have been lacking until this point. Herein, we detail the development of a sensor comprising a compact and selective analyzer with low gas consumption and a timely response, based on photoacoustic spectroscopy. This, combined with an automated liquid sampling system, enables the precise detection of ammonia traces in water. The sensor system incorporates a state-of-the art quantum cascade laser as the excitation source emitting at 9 \textmu m in resonance with the absorption line of NH$_3$ located at 1103.46 cm$^{-1}$. Our instrument demonstrated detection sensitivity at low ppm level for total ammonia nitrogen with response times less than 60 seconds. For the sampling system, an ammonia stripping solution was designed resulting in a prompt full measurement cycle (6.35 mins). A further evaluation of the sensor within a pilot study showed good reliability and agreement with the reference method for real water samples, confirming the potential of our NH$_3$ analyzer for water-quality monitoring applications.
Star colouring and locally constrained graph homomorphisms

Dvo\v{r}\'ak, Mohar and \v{S}\'amal (J. Graph Theory, 2013) proved that for every 3-regular graph $G$, the line graph of $G$ is 4-star colourable if and only if $G$ admits a locally bijective homomorphism to the cube $Q_3$. We generalise this result as follows: for $p\geq 2$, a $K_{1,p+1}$-free $2p$-regular graph $G$ admits a $(p + 2)$-star colouring if and only if $G$ admits a locally bijective homomorphism to a fixed $2p$-regular graph named $G_{2p}$. We also prove the following: (i) for $p\geq 2$, a $2p$-regular graph $G$ admits a $(p + 2)$-star colouring if and only if $G$ has an orientation $\vec{G}$ that admits an out-neighbourhood bijective homomorphism to a fixed orientation $\vec{G_{2p}}$ of $G2p$; (ii) for every 3-regular graph $G$, the line graph of $G$ is 4-star colourable if and only if $G$ is bipartite and distance-two 4-colourable; and (iii) it is NP-complete to check whether a planar 4-regular 3-connected graph is 4-star colourable.
Algorithm-based diagnostic application for diabetic retinopathy detection

Diabetic retinopathy (DR) is a growing health problem worldwide and is a leading cause of visual impairment and blindness, especially among working people aged 20-65. Its incidence is increasing along with the number of diabetes cases, and it is more common in developed countries than in developing countries. Recent research in the field of diabetic retinopathy diagnosis is using advanced technologies, such as analysis of images obtained by ophthalmoscopy. Automatic methods for analyzing eye images based on neural networks, deep learning and image analysis algorithms can improve the efficiency of diagnosis. This paper describes an automatic DR diagnosis method that includes processing and analysis of ophthalmoscopic images of the eye. It uses morphological algorithms to identify the optic disc and lesions characteristic of DR, such as microaneurysms, hemorrhages and exudates. Automated DR diagnosis has the potential to improve the efficiency of early detection of this disease and contribute to reducing the number of cases of diabetes-related visual impairment. The final step was to create an application with a graphical user interface that allowed retinal images taken at cooperating ophthalmology offices to be uploaded to the server. These images were then analyzed using a developed algorithm to make a diagnosis.