Miracle-dz/UniFAR-FTUs huggingface dataset:
NeutrEx: A 3D Quality Component Measure on Facial Expression Neutrality

Accurate face recognition systems are increasingly important in sensitive applications like border control or migration management. Therefore, it becomes crucial to quantify the quality of facial images to ensure that lowquality images are not affecting recognition accuracy. In this context, the current draft of ISO/IEC 29794-5 introduces the concept of component quality to estimate how single factors of variation affect recognition outcomes. In this study, we propose a quality measure (NeutrEx) based on the accumulated distances of a 3D face reconstruction to a neutral expression anchor. Our evaluations demonstrate the superiority of our proposed method compared to baseline approaches obtained by training Support Vector Machines on face embeddings extracted from a pre-trained Convolutional Neural Network for facial expression classification. Furthermore, we highlight the explainable nature of our NeutrEx measures by computing per-vertex distances to unveil the most impactful face regions and allow operators to give actionable feedback to subjects1.
Impact of geometry on chemical analysis exemplified for photoelectron spectroscopy of black silicon

For a smooth surface, the chemical composition can be readily evaluated by a variety of spectroscopy techniques; a prominent example is X-ray photoelectron spectroscopy (XPS), where the relative proportions of the elements are mainly determined by the intensity ratio of the element-specific photoelectrons. This deduction, however, is more intricate for a nanorough surface, such as black silicon, since the steep slopes of the geometry mimic local variations of the local emission angle. Here, we explicitly quantify this effect via an integral geometric analysis, by using so-called Minkowski tensors. Thus, we match the chemical information from XPS with topographical information from atomic force microscopy (AFM). Our method provides reliable estimates of layer thicknesses for nanorough surfaces. For our black silicon samples, we found that the oxide layer thickness is on average comparable to that of a native oxide layer. Our study highlights the impact of complex geometries at the nanoscale on the analysis of chemical properties with implications for a broad class of spectroscopy techniques.
Communicating Robot's Intentions while Assisting Users via Augmented Reality

This paper explores the challenges faced by assistive robots in effectively cooperating with humans, requiring them to anticipate human behavior, predict their actions' impact, and generate understandable robot actions. The study focuses on a use-case involving a user with limited mobility needing assistance with pouring a beverage, where tasks like unscrewing a cap or reaching for objects demand coordinated support from the robot. Yet, anticipating the robot's intentions can be challenging for the user, which can hinder effective collaboration. To address this issue, we propose an innovative solution that utilizes Augmented Reality (AR) to communicate the robot's intentions and expected movements to the user, fostering a seamless and intuitive interaction.
Unlocking Your Bike the Easy Way

Combination locks are widely used to secure bicycles. We consider a combination lock consisting of adjacent rotating dials with the first nonnegative integers printed on each of them. Assuming that we know the correct combination and we start from an incorrect combination, what is the minimal number of steps to arrive at the correct combination if in each step we are allowed to turn an arbitrary number of adjacent dials once in a common direction? We answer this question using elementary methods and show how this is related to a variation of (multivariate) functions.
bbOCR: An Open-source Multi-domain OCR Pipeline for Bengali Documents

Despite the existence of numerous Optical Character Recognition (OCR) tools, the lack of comprehensive open-source systems hampers the progress of document digitization in various low-resource languages, including Bengali. Low-resource languages, especially those with an alphasyllabary writing system, suffer from the lack of large-scale datasets for various document OCR components such as word-level OCR, document layout extraction, and distortion correction; which are available as individual modules in high-resource languages. In this paper, we introduce Bengali$. $AI-BRACU-OCR (bbOCR): an open-source scalable document OCR system that can reconstruct Bengali documents into a structured searchable digitized format that leverages a novel Bengali text recognition model and two novel synthetic datasets. We present extensive component-level and system-level evaluation: both use a novel diversified evaluation dataset and comprehensive evaluation metrics. Our extensive evaluation suggests that our proposed solution is preferable over the current state-of-the-art Bengali OCR systems. The source codes and datasets are available here: https://bengaliai.github. io/bbocr.
Contributions expérimentales originales en chambres réverbérantes à brassage de modes et en cavités surdimensionnées

La chambre reverberante a brassage de modes (CRBM) est une cavite metallique fermee surdimensionnee, equipee d’un brasseur de modes. Le brassage de modes rend le champ electromagnetique homogene et isotrope a l’interieur de la CRBM (dans son volume utile). Cette these illustre tout d’abord une nouvelle methode pour evaluer les performances d’un brasseur de modes (via le spectre Doppler). Ensuite, elle presente une nouvelle methode permettant de mesurer la surface equivalente radar (SER) d’un objet canonique en utilisant la technique de fenetrage temporel ou time-gating. Enfin, elle presente une nouvelle methode permettant de mesurer le diagramme de rayonnement d’une antenne dans une cavite surdimensionnee via la technique de time-gating.
R-C-P Method: An Autonomous Volume Calculation Method Using Image Processing and Machine Vision

Machine vision and image processing are often used with sensors for situation awareness in autonomous systems, from industrial robots to self-driving cars. The 3D depth sensors, such as LiDAR (Light Detection and Ranging), Radar, are great invention for autonomous systems. Due to the complexity of the setup, LiDAR may not be suitable for some operational environments, for example, a space environment. This study was motivated by a desire to get real-time volumetric and change information with multiple 2D cameras instead of a depth camera. Two cameras were used to measure the dimensions of a rectangular object in real-time. The R-C-P (row-column-pixel) method is developed using image processing and edge detection. In addition to the surface areas, the R-C-P method also detects discontinuous edges or volumes. Lastly, experimental work is presented for illustration of the R-C-P method, which provides the equations for calculating surface area dimensions. Using the equations with given distance information between the object and the camera, the vision system provides the dimensions of actual objects.
Never Explore Repeatedly in Multi-Agent Reinforcement Learning

In the realm of multi-agent reinforcement learning, intrinsic motivations have emerged as a pivotal tool for exploration. While the computation of many intrinsic rewards relies on estimating variational posteriors using neural network approximators, a notable challenge has surfaced due to the limited expressive capability of these neural statistics approximators. We pinpoint this challenge as the"revisitation"issue, where agents recurrently explore confined areas of the task space. To combat this, we propose a dynamic reward scaling approach. This method is crafted to stabilize the significant fluctuations in intrinsic rewards in previously explored areas and promote broader exploration, effectively curbing the revisitation phenomenon. Our experimental findings underscore the efficacy of our approach, showcasing enhanced performance in demanding environments like Google Research Football and StarCraft II micromanagement tasks, especially in sparse reward settings.
Decomposition-based Hierarchical Task Allocation and Planning for Multi-Robots under Hierarchical Temporal Logic Specifications

Past research into robotic planning with temporal logic specifications, notably Linear Temporal Logic (LTL), was largely based on singular formulas for individual or groups of robots. But with increasing task complexity, LTL formulas unavoidably grow lengthy, complicating interpretation and specification generation, and straining the computational capacities of the planners. A recent development has been the hierarchical representation of LTL [1] that contains multiple temporal logic specifications, providing a more interpretable framework. However, the proposed planning algorithm assumes the independence of robots within each specification, limiting their application to multi-robot coordination with complex temporal constraints. In this work, we formulated a decomposition-based hierarchical framework. At the high level, each specification is first decomposed into a set of atomic sub-tasks. We further infer the temporal relations among the sub-tasks of different specifications to construct a task network. Subsequently, a Mixed Integer Linear Program is utilized to assign sub-tasks to various robots. At the lower level, domain-specific controllers are employed to execute sub-tasks. Our approach was experimentally applied to domains of robotic navigation and manipulation. The outcomes of thorough simulations, which included comparative analyses, demonstrated the effectiveness of the proposed approach.
Neural Architectures Learning Fourier Transforms, Signal Processing and Much More

This report will explore and answer fundamental questions about taking Fourier Transforms and tying it with recent advances in AI and neural architecture. One interpretation of the Fourier Transform is decomposing a signal into its constituent components by projecting them onto complex exponentials. Variants exist, such as discrete cosine transform that does not operate on the complex domain and projects an input signal to only cosine functions oscillating at different frequencies. However, this is a fundamental limitation, and it needs to be more suboptimal. The first one is that all kernels are sinusoidal: What if we could have some kernels adapted or learned according to the problem? What if we can use neural architectures for this? We show how one can learn these kernels from scratch for audio signal processing applications. We find that the neural architecture not only learns sinusoidal kernel shapes but discovers all kinds of incredible signal-processing properties. E.g., windowing functions, onset detectors, high pass filters, low pass filters, modulations, etc. Further, upon analysis of the filters, we find that the neural architecture has a comb filter-like structure on top of the learned kernels. Comb filters that allow harmonic frequencies to pass through are one of the core building blocks/types of filters similar to high-pass, low-pass, and band-pass filters of various traditional signal processing algorithms. Further, we can also use the convolution operation with a signal to be learned from scratch, and we will explore papers in the literature that uses this with that robust Transformer architectures. Further, we would also explore making the learned kernel's content adaptive, i.e., learning different kernels for different inputs.
Domain Reduction Strategy for Non Line of Sight Imaging

This paper presents a novel optimization-based method for non-line-of-sight (NLOS) imaging that aims to reconstruct hidden scenes under various setups. Our method is built upon the observation that photons returning from each point in hidden volumes can be independently computed if the interactions between hidden surfaces are trivially ignored. We model the generalized light propagation function to accurately represent the transients as a linear combination of these functions. Moreover, our proposed method includes a domain reduction procedure to exclude empty areas of the hidden volumes from the set of propagation functions, thereby improving computational efficiency of the optimization. We demonstrate the effectiveness of the method in various NLOS scenarios, including non-planar relay wall, sparse scanning patterns, confocal and non-confocal, and surface geometry reconstruction. Experiments conducted on both synthetic and real-world data clearly support the superiority and the efficiency of the proposed method in general NLOS scenarios.
Deep Metric Loss for Multimodal Learning

Multimodal learning often outperforms its unimodal counterparts by exploiting unimodal contributions and cross-modal interactions. However, focusing only on integrating multimodal features into a unified comprehensive representation overlooks the unimodal characteristics. In real data, the contributions of modalities can vary from instance to instance, and they often reinforce or conflict with each other. In this study, we introduce a novel \text{MultiModal} loss paradigm for multimodal learning, which subgroups instances according to their unimodal contributions. \text{MultiModal} loss can prevent inefficient learning caused by overfitting and efficiently optimize multimodal models. On synthetic data, \text{MultiModal} loss demonstrates improved classification performance by subgrouping difficult instances within certain modalities. On four real multimodal datasets, our loss is empirically shown to improve the performance of recent models. Ablation studies verify the effectiveness of our loss. Additionally, we show that our loss generates a reliable prediction score for each modality, which is essential for subgrouping. Our \text{MultiModal} loss is a novel loss function to subgroup instances according to the contribution of modalities in multimodal learning and is applicable to a variety of multimodal models with unimodal decisions. Our code is available at https://github.com/SehwanMoon/MultiModalLoss.
Broken Toric Varieties and Cell-Compatible Sheaves

We study the cohomology of broken toric varieties via the derived push-forward of the constant sheaf to a complex of polytopes, proving a Deligne-type decomposition theorem, degeneration of the associated Leray-Serre spectral sequence, and showing that the Leray filtration on their cohomology is equal to twice the weight filtration. Furthermore, we give an explicit formula for the Betti numbers of some broken toric varieties whose associated complex of polytopes is the $n$-skeleton of a higher dimensional polytope, encompassing some important examples.
HODN: Disentangling Human-Object Feature for HOI Detection

The task of Human-Object Interaction (HOI) detection is to detect humans and their interactions with surrounding objects, where transformer-based methods show dominant advances currently. However, these methods ignore the relationship among humans, objects, and interactions: 1) human features are more contributive than object ones to interaction prediction; 2) interactive information disturbs the detection of objects but helps human detection. In this article, we propose a Human and Object Disentangling Network (HODN) to model the HOI relationships explicitly, where humans and objects are first detected by two disentangling decoders independently and then processed by an interaction decoder. Considering that human features are more contributive to interaction, we propose a Human-Guide Linking method to make sure the interaction decoder focuses on the human-centric regions with human features as the positional embeddings. To handle the opposite influences of interactions on humans and objects, we propose a Stop-Gradient Mechanism to stop interaction gradients from optimizing the object detection but to allow them to optimize the human detection. Our proposed method achieves competitive performance on both the V-COCO and the HICO-Det datasets. It can be combined with existing methods easily for state-of-the-art results.
Advancing Intra-operative Precision: Dynamic Data-Driven Non-Rigid Registration for Enhanced Brain Tumor Resection in Image-Guided Neurosurgery

During neurosurgery, medical images of the brain are used to locate tumors and critical structures, but brain tissue shifts make pre-operative images unreliable for accurate removal of tumors. Intra-operative imaging can track these deformations but is not a substitute for pre-operative data. To address this, we use Dynamic Data-Driven Non-Rigid Registration (NRR), a complex and time-consuming image processing operation that adjusts the pre-operative image data to account for intra-operative brain shift. Our review explores a specific NRR method for registering brain MRI during image-guided neurosurgery and examines various strategies for improving the accuracy and speed of the NRR method. We demonstrate that our implementation enables NRR results to be delivered within clinical time constraints while leveraging Distributed Computing and Machine Learning to enhance registration accuracy by identifying optimal parameters for the NRR method. Additionally, we highlight challenges associated with its use in the operating room.
Learning in Dynamic Systems and Its Application to Adaptive PID Control

Deep learning using neural networks has revolutionized machine learning and put artificial intelligence into everyday life. In order to introduce self-learning to dynamic systems other than neural networks, we extend the Brandt-Lin learning algorithm of neural networks to a large class of dynamic systems. This extension is possible because the Brandt-Lin algorithm does not require a dedicated step to back-propagate the errors in neural networks. To this end, we first generalize signal-flow graphs so that they can be used to model nonlinear systems as well as linear systems. We then derive the extended Brandt-Lin algorithm that can be used to adapt the weights of branches in generalized signal-flow graphs. We show the applications of the new algorithm by applying it to adaptive PID control. In particular, we derive a new adaptation law for PID controllers. We verify the effectiveness of the method using simulations for linear and nonlinear plants, stable as well as unstable plants.
Contrastive Learning-based Imputation-Prediction Networks for In-hospital Mortality Risk Modeling using EHRs

Predicting the risk of in-hospital mortality from electronic health records (EHRs) has received considerable attention. Such predictions will provide early warning of a patient's health condition to healthcare professionals so that timely interventions can be taken. This prediction task is challenging since EHR data are intrinsically irregular, with not only many missing values but also varying time intervals between medical records. Existing approaches focus on exploiting the variable correlations in patient medical records to impute missing values and establishing time-decay mechanisms to deal with such irregularity. This paper presents a novel contrastive learning-based imputation-prediction network for predicting in-hospital mortality risks using EHR data. Our approach introduces graph analysis-based patient stratification modeling in the imputation process to group similar patients. This allows information of similar patients only to be used, in addition to personal contextual information, for missing value imputation. Moreover, our approach can integrate contrastive learning into the proposed network architecture to enhance patient representation learning and predictive performance on the classification task. Experiments on two real-world EHR datasets show that our approach outperforms the state-of-the-art approaches in both imputation and prediction tasks.
Activation Addition: Steering Language Models Without Optimization

Reliably controlling the behavior of large language models is a pressing open problem. Existing methods include supervised finetuning, reinforcement learning from human feedback, prompt engineering and guided decoding. We instead investigate activation engineering: modifying activations at inference-time to predictably alter model behavior. We bias the forward pass with a 'steering vector' implicitly specified through natural language. Past work learned these steering vectors; our Activation Addition (ActAdd) method instead computes them by taking the activation differences which result from pairs of prompts. We demonstrate ActAdd on GPT-2 on OpenWebText and ConceptNet, and replicate the effect on Llama-13B and GPT-J-6B. Our approach yields inference-time control over high-level properties of output&preserves performance on off-target topics. The method requires far less compute and implementation effort than finetuning and RLHF, allows for natural language specification by users, and its overhead scales naturally with model size.
How do soil properties affect alpine treelines? General principles in a global perspective and novel findings from Rolwaling Himal, Nepal

Little is known about how soil properties control tree growth at its upper limit. This paper reviews the state of knowledge and discusses the results specifically related to ecozones, to the scale-dependent importance of single factors, and to new findings from a near-natural treeline ecotone in Rolwaling Himal, Nepal. This paper identifies gaps in literature and shows where new research is needed, both conceptual and geographical. The review shows that at a global scale and throughout diverse ecozones, growing season soil temperature is considered a key factor for tree growth. Soil temperatures differ greatly at a local scale, and are mainly determined by local climatic, edaphic, and topographic conditions. Our result of 7.6 ± 0.6°C for growing season mean soil temperature at treeline in Rolwaling is 1.2 K higher compared to the postulated 6.4 ± 0.7°C for alpine treelines. We suggest a broadening of the ±0.7°C error term to cover the wide range at a local scale. The role of major soil nutrients and soil moisture for treeline shift has been underestimated by far. In Rolwaling, significantly decreasing nutrient availability (N, K, Mg) in soils and foliage with elevation might explain why treeline shift and global warming are decoupled. Further, soil moisture deficits early in the year impede seedling and sapling establishment, which could be an important mechanism that controls treeline position. These findings question previous results which argue that alpine treelines are unaffected by soil nutrient availability and soil moisture. We assume that specific combinations of soil properties as well as single soil properties limit tree growth even below climatic treelines.