Miracle-dz/UniFAR-FTUs huggingface dataset:
Efﬁcient coding of facial stimuli in primate inferotemporal cortex

Processing faces accurately and efﬁciently is a key capability of humans and other animals that engage in sophisticated social tasks. Recent studies reported a decoupled coding for faces in the primate inferotemporal cortex, with two separate neural populations coding for the geometric position of (uniformed) facial landmarks and for the image texture at ﬁxed landmark positions, respectively. Here, we formally assess the efﬁciency of this decoupled coding by appealing to the information-theoretic notion of description length, which quantiﬁes the amount of information that is saved when encoding novel facial images, with a given precision. We show that despite decoupled coding encodes two sets of (shape and texture) coordinates, it is more efﬁcient (i.e., yields more information compression) than the widely used eigenface method, which only requires encoding the original facial images. The advantage of decoupled coding over eigenface coding increases with image resolution and is especially prominent when coding variants of training set images that only differ in facial expressions. Moreover, we demonstrate that decoupled coding entails better performance in three different tasks: the representation of facial images, the (daydream) sampling of novel facial images, and the recognition of facial identities and gender. In summary, our study provides a ﬁrst principle perspective on the efﬁciency and accuracy of the decoupled coding of facial stimuli reported in the primate inferotemporal cortex.
Challenges and Complexities in Machine Learning based Credit Card Fraud Detection

Credit cards play an exploding role in modern economies. Its popularity and ubiquity have created a fertile ground for fraud, assisted by the cross boarder reach and instantaneous confirmation. While transactions are growing, the fraud percentages are also on the rise as well as the true cost of a dollar fraud. Volume of transactions, uniqueness of frauds and ingenuity of the fraudster are main challenges in detecting frauds. The advent of machine learning, artificial intelligence and big data has opened up new tools in the fight against frauds. Given past transactions, a machine learning algorithm has the ability to 'learn' infinitely complex characteristics in order to identify frauds in real-time, surpassing the best human investigators. However, the developments in fraud detection algorithms has been challenging and slow due the massively unbalanced nature of fraud data, absence of benchmarks and standard evaluation metrics to identify better performing classifiers, lack of sharing and disclosure of research findings and the difficulties in getting access to confidential transaction data for research. This work investigates the properties of typical massively imbalanced fraud data sets, their availability, suitability for research use while exploring the widely varying nature of fraud distributions. Furthermore, we show how human annotation errors compound with machine classification errors. We also carry out experiments to determine the effect of PCA obfuscation (as a means of disseminating sensitive transaction data for research and machine learning) on algorithmic performance of classifiers and show that while PCA does not significantly degrade performance, care should be taken to use the appropriate principle component size (dimensions) to avoid overfitting.
Impact of a rigid sphere onto an elastic membrane

We study the axisymmetric impact of a rigid sphere onto an elastic membrane theoretically and experimentally. We derive governing equations from first principles and impose natural kinematic and geometric constraints for the coupled motion of the sphere and the membrane during contact. The free-boundary problem of finding the contact surface, over which forces caused by the collision act, is solved by an iterative method. This results in a model that produces detailed predictions of the trajectory of the sphere, the deflection of the membrane, and the pressure distribution during contact. Our model predictions are validated against our direct experimental measurements. Moreover, we identify new phenomena regarding the behaviour of the coefficient of restitution for low impact velocities, the possibility of multiple contacts during a single rebound, and energy recovery on subsequent bounces. Insight obtained from this model problem in contact mechanics can inform ongoing efforts towards the development of predictive models for contact problems that arise naturally in multiple engineering applications.
Transversality and framed cobordism

Ren\'e Thom's remarkable and far-reaching concept of transversality has found numerous powerful applications. Most importantly, it allowed Thom to develop cobordism theory, which led to a piercing insight into the topology of smooth manifolds, when he generalized Lev Pontryagin's earlier concept of framed cobordism. For his profound findings, in 1958 he was awarded the Fields Medal. The present paper provides a down-to-earth approach to transversality theory which only assumes basic knowledge about smooth manifolds. Following an idea of Andrew Putman, we then use transversality to give a novel and detailed proof of an isomorphism theorem of Pontryagin about homotopy groups of spheres whose original proof is generally believed to be rather hard to access.
Cryptographic Key Management Workshop Summary - June 8-9, 2009

On June 8 and 9, 2009, NIST held a Cryptographic Key Management (CKM) Workshop at its Gaithersburg, Maryland, campus that attracted approximately 80 people attending the workshop in person, with another 75 participating through video conferencing, and an additional 36 participating via audio teleconferencing. A total of 36 speakers, including technical experts, security standards leaders, and experienced managers gave presentations on various aspects of CKM during the workshop. Two presentations were made remotely via audio teleconferencing facilities. This summary provides the highlights of workshop presentations organized both by major CKM topics and also by presenter. Electronic access to the slides used for the presentations will be available for at least one year following the Workshop at http://csrc.nist. gov/groups/ST/key_mgmt. Additional information regarding the CKM project will be made available through the NIST website at http://csrc.nist. gov/groups/ST/key_mgmt.
Detect Hate Speech in Unseen Domains using Multi-Task Learning: A Case Study of Political Public Figures

Automatic identification of hateful and abusive content is vital in combating the spread of harmful online content and its damaging effects. Most existing works evaluate models by examining the generalization error on train-test splits on hate speech datasets. These datasets often differ in their definitions and labeling criteria, leading to poor model performance when predicting across new domains and datasets. In this work, we propose a new Multi-task Learning (MTL) pipeline that utilizes MTL to train simultaneously across multiple hate speech datasets to construct a more encompassing classification model. We simulate evaluation on new previously unseen datasets by adopting a leave-one-out scheme in which we omit a target dataset from training and jointly train on the other datasets. Our results consistently outperform a large sample of existing work. We show strong results when examining generalization error in train-test splits and substantial improvements when predicting on previously unseen datasets. Furthermore, we assem-ble a novel dataset, dubbed PubFigs, focusing on the problematic speech of American Public Political Figures. We automatically detect problematic speech in the 305 , 235 tweets in PubFigs, and we uncover insights into the posting behaviors of public figures.
Few-shot Table-to-text Generation with Prefix-Controlled Generator

Neural table-to-text generation approaches are data-hungry, limiting their adaption for low-resource real-world applications. Previous works mostly resort to Pre-trained Language Models (PLMs) to generate fluent summaries of a table. However, they often contain hallucinated contents due to the uncontrolled nature of PLMs. Moreover, the topological differences between tables and sequences are rarely studied. Last but not least, fine-tuning on PLMs with a handful of instances may lead to over-fitting and catastrophic forgetting. To alleviate these problems, we propose a prompt-based approach, Prefix-Controlled Generator (i.e., PCG), for few-shot table-to-text generation. We prepend a task-specific prefix for a PLM to make the table structure better fit the pre-trained input. In addition, we generate an input-specific prefix to control the factual contents and word order of the generated text. Both automatic and human evaluations on different domains (humans, books and songs) of the Wikibio dataset prove the effectiveness of our approach.
More is Merrier: Relax the Non-Collusion Assumption in Multi-Server PIR

A long line of research on secure computation has confirmed that anything that can be computed, can be computed securely using a set of non-colluding parties. Indeed, this non-collusion assumption makes a number of problems solvable, as well as reduces overheads and bypasses computational hardness results, and it is pervasive across different privacy-enhancing technologies. However, it remains highly susceptible to covert, undetectable collusion among computing parties. This work stems from an observation that if the number of available computing parties is much higher than the number of parties required to perform a secure computation task, collusion attempts in privacy-preserving computations could be deterred. We focus on the prominent privacy-preserving computation task of multi-server $1$-private information retrieval (PIR) that inherently assumes no pair-wise collusion. For PIR application scenarios, such as those for blockchain light clients, where the available servers can be plentiful, a single server's deviating action is not tremendously beneficial to itself. We can make deviations undesired via small amounts of rewards and penalties, thus significantly raising the bar for collusion resistance. We design and implement a collusion mitigation mechanism on a public bulletin board with payment execution functions, considering only rational and malicious parties with no honest non-colluding servers. Privacy protection is offered for an extended period after the query executions.
Aging prediction using deep generative model toward the development of preventive medicine

From birth to death, we all experience surprisingly ubiquitous changes over time due to aging. If we can predict aging in the digital domain, that is, the digital twin of the human body, we would be able to detect lesions in their very early stages, thereby enhancing the quality of life and extending the life span. We observed that none of the previously developed digital twins of the adult human body explicitly trained longitudinal conversion rules between volumetric medical images with deep generative models, potentially resulting in poor prediction performance of, for example, ventricular volumes. Here, we establish a new digital twin of an adult human body that adopts longitudinally acquired head computed tomography (CT) images for training, enabling prediction of future volumetric head CT images from a single present volumetric head CT image. We, for the ﬁrst time, adopt one of the three-dimensional ﬂow-based deep generative models to realize this sequential three-dimensional digital twin. We show that our digital twin outperforms the latest methods of prediction of ventricular volumes in relatively short terms.
A Survey and Framework of Cooperative Perception: From Heterogeneous Singleton to Hierarchical Cooperation

Perceiving the environment is one of the most fundamental keys to enabling Cooperative Driving Automation (CDA), which is regarded as the revolutionary solution to addressing the safety, mobility, and sustainability issues of contemporary transportation systems. Although an unprecedented evolution is now happening in the area of computer vision for object perception, state-of-the-art perception methods are still struggling with sophisticated real-world traffic environments due to the inevitably physical occlusion and limited receptive field of single-vehicle systems. Based on multiple spatially separated perception nodes, Cooperative Perception (CP) is born to unlock the bottleneck of perception for driving automation. In this paper, we comprehensively review and analyze the research progress on CP and, to the best of our knowledge, this is the first time to propose a unified CP framework. Architectures and taxonomy of CP systems based on different types of sensors are reviewed to show a high-level description of the workflow and different structures for CP systems. Node structure, sensor modality, and fusion schemes are reviewed and analyzed with comprehensive literature to provide detailed explanations of specific methods. A Hierarchical CP framework is proposed, followed by a review of existing Datasets and Simulators to sketch an overall landscape of CP. Discussion highlights the current opportunities, open challenges, and anticipated future trends.
Mckean-Vlasov stochastic differential equations with oblique reflection on non-smooth time dependent domains

: In this paper, we consider a class of Mckean-Vlasov stochastic diﬀerential equation with oblique reﬂection over an non-smooth time dependent domain. We establish the existence and uniqueness results of this class, address the propagation of chaos and prove a Fredlin-Wentzell type large deviations principle (LDP). One of the main diﬃculties is raised by the setting of non-smooth time dependent domain. To prove the LDP, a suﬃcient condition for the weak convergence method, which is suitable for Mckean-Vlasov stochastic diﬀerential equation, plays an important role.
AI and 6G Into the Metaverse: Fundamentals, Challenges and Future Research Trends

Since Facebook was renamed Meta, a lot of attention, debate, and exploration have intensified about what the Metaverse is, how it works, and the possible ways to exploit it. It is anticipated that Metaverse will be a continuum of rapidly emerging technologies, usecases, capabilities, and experiences that will make it up for the next evolution of the Internet. Several researchers have already surveyed the literature on artificial intelligence (AI) and wireless communications in realizing the Metaverse. However, due to the rapid emergence and continuous evolution of technologies, there is a need for a comprehensive and in-depth survey of the role of AI, 6G, and the nexus of both in realizing the immersive experiences of Metaverse. Therefore, in this survey, we first introduce the background and ongoing progress in augmented reality (AR), virtual reality (VR), mixed reality (MR) and spatial computing, followed by the technical aspects of AI and 6G. Then, we survey the role of AI in the Metaverse by reviewing the state-of-the-art in deep learning, computer vision, and Edge AI to extract the requirements of 6G in Metaverse. Next, we investigate the promising services of B5G/6G towards Metaverse, followed by identifying the role of AI in 6G networks and 6G networks for AI in support of Metaverse applications, and the need for sustainability in Metaverse. Finally, we enlist the existing and potential applications, usecases, and projects to highlight the importance of progress in the Metaverse. Moreover, in order to provide potential research directions to researchers, we underline the challenges, research gaps, and lessons learned identified from the literature review of the aforementioned technologies.
The Robustness of Tether Friction in Non-Idealized Terrains

Reduced traction limits the ability of mobile robotic systems to resist or apply large external loads, such as tugging a massive payload. One simple and versatile solution is to wrap a tether around naturally occurring objects to leverage the capstan effect and create exponentially-amplified holding forces. Experiments show that an idealized capstan model explains force amplification experienced on common irregular outdoor objects – trees, rocks, posts. Robust to variable environmental conditions, this exponential amplification method can harness single or multiple capstan objects, either in series or in parallel with a team of robots. This adaptability allows for a range of potential configurations especially useful for when objects cannot be fully encircled or gripped. This versatility is demonstrated with teleoperated mobile platforms to (1) control the lowering and arrest of a payload, (2) to achieve planar control of a payload, and (3) to act as an anchor point for a more massive platform to winch towards. We show the simple addition of a tether, wrapped around shallow stones in sand, amplifies holding force of a low-traction platform by up to 774x.
Core Collapse Supernova Gravitational Wave Emission for Progenitors of 9.6, 15, and 25 Solar Masses

We present gravitational wave emission predictions based on three core collapse supernova simulations corresponding to three different progenitor masses. The masses span a large range, between 9.6 and 25 Solar masses, are all initially non-rotating, and are of two metallicities: zero and Solar. We compute both the temporal evolution of the gravitational wave strains for both the plus and the cross polarizations, as well as their spectral decomposition and characteristic strains.
Convex integer optimization with Frank-Wolfe methods

Mixed-integer nonlinear optimization is a broad class of problems that feature combinatorial structures and nonlinearities. Typical exact methods combine a branch-and-bound scheme with relaxation and separation subroutines. We investigate the properties and advantages of error-adaptive ﬁrst-order methods based on the Frank-Wolfe algorithm for this setting, requiring only a gradient oracle for the objective function and linear optimization over the feasible set. In particular, we will study the algorithmic consequences of optimizing with a branch-and-bound approach where the subproblem is solved over the convex hull of the mixed-integer feasible set thanks to linear oracle calls, compared to solving the subproblems over the continuous relaxation of the same set. This novel approach computes feasible solutions while working on a single representation of the polyhedral constraints, leveraging the full extent of Mixed-Integer Programming (MIP) solvers without an outer approximation scheme.
Time of Impact Dataset for Continuous Collision Detection and a Scalable Conservative Algorithm

We introduce a large-scale benchmark for broad- and narrow-phase continuous collision detection (CCD) over linearized trajectories with exact time of impacts and use it to evaluate the accuracy, correctness, and efficiency of 13 state-of-the-art CCD algorithms. Our analysis shows that several methods exhibit problems either in efficiency or accuracy. To overcome these limitations, we introduce an algorithm for CCD designed to be scalable on modern parallel architectures and provably correct when implemented using floating point arithmetic. We integrate our algorithm within the Incremental Potential Contact solver [Li et al . 2021] and evaluate its impact on various simulation scenarios. Our approach includes a broad-phase CCD to quickly filter out primitives having disjoint bounding boxes and a narrow-phase CCD that establishes whether the remaining primitive pairs indeed collide. Our broad-phase algorithm is efficient and scalable thanks to the experimental observation that sweeping along a coordinate axis performs surprisingly well on modern parallel architectures. For narrow-phase CCD, we re-design the recently proposed interval-based algorithm of Wang et al. [2021] to work on massively parallel hardware. To foster the adoption and development of future linear CCD algorithms, and to evaluate their correctness, scalability, and overall performance, we release the dataset with analytic ground truth, the implementation of all the algorithms tested, and our testing framework.
Observation of magnetic fields in laser-produced plasma using the Zeeman effect. Memorandum report

The self-generated magnetic field produced when a pulsed Nd:glass laser is focused onto a carbon foil target is measured for the first time using the Zeeman effect. Emission from the CV 1s2s3S sub 1-1s2p3 P sub 2 (2270.9A) was used for the measurement. At a laser irradiance of approx 5 X 10 to the 12th power W/cm2, the distribution to a ring pattern, which caused higher density gradients. A Faraday rotation measurement at approx 200 kG agreed with the Zeeman effect measurement. Opacity effects in the plasma enhanced the sensitivity of the Zeeman measurement.
Short birth interval and associated factors among women who gave birth in the last three years in Dembecha district, Northwest Ethiopia

Background Maternal and child mortality remains a major public health problem in Ethiopia. Improving short birth interval practice is a main strategy to reduce neonatal mortality, maternal mortality, adverse pregnancy outcomes, high fertility rate, and enhance economic development efforts. However, there has been limited study done regarding short birth intervals in the study area. Therefore, this study aimed to assess short birth intervals and associated factors among women who gave birth in the last three years in Dembecha district, Northwest Ethiopia, 2019. Method A community-based cross-sectional study was conducted among 880 reproductive-age multipara mothers using a stratified cluster sampling technique. The data were collected by face-to-face interviews through pretested and semi-structured questionnaires. Bivariable and multivariable logistic regression model was fitted. Variables with a p-value ≤0.05 were considered statistically significant factors. Adjusted odds ratio with a 95% confidence interval was used to report the association between predictors and the outcome variable. Results The prevalence of short birth interval was found to be 43.4% (95% CI: 40.2, 46.9). Husband education (able to read and write) [AOR:2.81,(95% CI:1.04,7.85)], wealth index (lowest quartile) [AOR:3.75,(95% CI:2.35,5.97), residence (urban) [AOR:3.20,(95% CI:1.62,6.33)],age at first marriage (15–17 years old) [AOR: 1.65,(95% CI:1.15, 2.26),and non-use of contraceptive [AOR: 8.78, (95% CI: 6.18, 12.47) were statistically significant variables. Conclusion The study revealed that the prevalence of short birth intervals among multipara women is found to be high. Husband education, wealth index, urban residence, age at first marriage, and non-use of contraceptives were variables significantly associated with short birth intervals. Hence, to overcome the problem the focus should be on increasing family planning utilization, avoiding early marriage, strengthening paternal education, and improving family income.
A partnership between the lipid scramblase XK and the lipid transfer protein VPS13A at the plasma membrane

Significance Chorea-acanthocytosis and McLeod syndrome, due to mutations in VPS13A and XK, respectively, share similar manifestations: jerking movements due to degeneration of the caudate nucleus and star-shaped erythrocytes. Often, proteins whose mutations result in similar phenotypes function together. Both VPS13A and XK are thought to control bilayer lipids dynamics: net lipid transfer between adjacent cytosolic membrane leaflets (VPS13A) and lipid scrambling to equilibrate lipids between bilayer leaflets (XK). Accordingly, the two proteins were shown to interact, but the reported subcellular localizations of VPS13A seemed incompatible with a partnership with XK, a plasma membrane protein. Here we show that the two proteins can interact at ER-plasma membrane contacts and provide evidence that their partnership undergoes regulation by an intramolecular switch in XK.