Miracle-dz/UniFAR-FTUs huggingface dataset:
Visualizing high-dimensional loss landscapes with Hessian directions

Analyzing the geometric properties of high-dimensional loss functions, such as local curvature and the existence of other optima around a certain point in loss space, can help provide a better understanding of the interplay between neural-network structure, implementation attributes, and learning performance. In this paper, we combine concepts from high-dimensional probability and differential geometry to study how curvature properties in lower-dimensional loss representations depend on those in the original loss space. We show that saddle points in the original space are rarely correctly identified as such in the expected lower-dimensional representations if random projections are used. The principal curvature in the expected lower-dimensional representation is proportional to the mean curvature in the original loss space. Hence, the mean curvature in the original loss space determines if saddle points appear, on average, as either minima, maxima, or almost flat regions. We use the connection between expected curvature in random projections and mean curvature in the original space (i.e. the normalized Hessian trace) to compute Hutchinson-type trace estimates without calculating Hessian-vector products as in the original Hutchinson method. Because random projections are not suitable for correctly identifying saddle information, we propose to study projections along the dominant Hessian directions that are associated with the largest and smallest principal curvatures. We connect our findings to the ongoing debate on loss landscape flatness and generalizability. Finally, for different common image classifiers and a function approximator, we show and compare random and Hessian projections of loss landscapes with up to approximately 7 × 10 6 parameters.
A Federated Learning-enabled Smart Street Light Monitoring Application: Benefits and Future Challenges

Data-enabled cities are recently accelerated and enhanced with automated learning for improved Smart Cities applications. In the context of an Internet of Things (IoT) ecosystem, the data communication is frequently costly, inefficient, not scalable and lacks security. Federated Learning (FL) plays a pivotal role in providing privacy-preserving and communication efficient Machine Learning (ML) frameworks. In this paper we evaluate the feasibility of FL in the context of a Smart Cities Street Light Monitoring application. FL is evaluated against benchmarks of centralised and (fully) personalised machine learning techniques for the classification task of the lampposts operation. Incorporating FL in such a scenario shows minimal performance reduction in terms of the classification task, but huge improvements in the communication cost and the privacy preserving. These outcomes strengthen FL's viability and potential for IoT applications.
Using Speech to Reduce Loss of Trust in Humanoid Social Robots

We present data from two online human-robot interaction experiments where 227 participants viewed videos of a humanoid robot exhibiting faulty or non-faulty behaviours while either remaining mute or speaking. The participants were asked to evaluate their perception of the robot's trustworthiness, as well as its likeability, animacy, and perceived intelligence. The results show that, while a non-faulty robot achieves the highest trust, an apparently faulty robot that can speak manages to almost completely mitigate the loss of trust that is otherwise seen with faulty behaviour. We theorize that this mitigation is correlated with the increase in perceived intelligence that is also seen when speech is present.
Rasmussen invariants of Whitehead doubles and other satellites

We prove formulae for the $\mathbb{F}_2$-Rasmussen invariant of satellite knots of patterns with wrapping number 2, using the multicurve technology for Khovanov and Bar-Natan homology developed by Kotelskiy, Watson, and the second author. A new concordance homomorphism, which is independent of the Rasmussen invariant, plays a central role in these formulae. We also explore whether similar formulae hold for the Ozsv\'ath-Szab\'o invariant $\tau$.
Multiferroic Ti$_3$C$_2$T$_x$ MXene with Tunable Ferroelectric-controlled High Performance Resistive Memory Devices

Multiferroic (MF) devices based on simultaneous ferroelectric and ferromagnetic phenomena are considered to be promising candidates for future bi-functional micro/nano-electronics. The multiferroic phenomena in two-dimensional materials is rarely reported in literature. We reported a simple approach to reveal frequency-dependent ferroelectricity and mutiferroicity in Ti 3 C 2 T x MXene film at room-temperature. To study the frequency and poling effect on ferroelectricity as well as multiferroicity, we performed electric polarization vs. electric field measurement at different external frequencies measured under zero and non-zero static magnetic fields. In order to further investigate this effect, the magneto-electric (ME) coupling was also performed to confirm the multiferroic nature of our synthesized Ti 3 C 2 T x MXene film. The ferroelectric hysteresis effect was attributed to the switching of electric domain walls under low frequencies that continue to respond to at much extent to the higher frequencies. The coupling between disordered electric dipoles with local spin moments could cause presence of strong magneto-electric coupling. Moreover, the bipolar resistive switching in trilayer memory devices also supports the ferroelectric behavior of HT- Ti 3 C 2 T x MXene film and showed uniform repeatability in switching behavior due to minimum dielectric loss inside ferroelectric HT-Ti 3 C 2 T x MXene along with improved on/off ratio in comparison to non-ferroelectric Ti 3 C 2 T x
CounTR: Transformer-based Generalised Visual Counting

In this paper, we consider the problem of generalised visual object counting, with the goal of developing a computational model for counting the number of objects from arbitrary semantic categories, using arbitrary number of"exemplars", i.e. zero-shot or few-shot counting. To this end, we make the following four contributions: (1) We introduce a novel transformer-based architecture for generalised visual object counting, termed as Counting Transformer (CounTR), which explicitly capture the similarity between image patches or with given"exemplars"with the attention mechanism;(2) We adopt a two-stage training regime, that first pre-trains the model with self-supervised learning, and followed by supervised fine-tuning;(3) We propose a simple, scalable pipeline for synthesizing training images with a large number of instances or that from different semantic categories, explicitly forcing the model to make use of the given"exemplars";(4) We conduct thorough ablation studies on the large-scale counting benchmark, e.g. FSC-147, and demonstrate state-of-the-art performance on both zero and few-shot settings.
Consistency between ordering and clustering methods for graphs

A relational dataset is often analyzed by optimally assigning a label to each element through clustering or ordering. While similar characterizations of a dataset would be achieved by both clustering and ordering methods, the former has been studied much more actively than the latter, particularly for the data represented as graphs. This study fills this gap by investigating methodological relationships between several clustering and ordering methods, focusing on spectral techniques. Furthermore, we evaluate the resulting performance of the clustering and ordering methods. To this end, we propose a measure called the label continuity error, which generically quantifies the degree of consistency between a sequence and partition for a set of elements. Based on synthetic and real-world datasets, we evaluate the extents to which an ordering method identifies a module structure and a clustering method identifies a banded structure.
Reshape: Adaptive Result-aware Skew Handling for Exploratory Analysis on Big Data

The process of data analysis, especially in GUI-based analytics systems, is highly exploratory. The user iteratively refines a workflow multiple times before arriving at the final workflow. In such an exploratory setting, it is valuable to the user if the initial results of the workflow are representative of the final answers so that the user can refine the workflow without waiting for the completion of its execution. Partitioning skew may lead to the production of misleading initial results during the execution. In this paper, we explore skew and its mitigation strategies from the perspective of the results shown to the user. We present a novel framework called Reshape that can adaptively handle partitioning skew in pipelined execution. Reshape employs a two-phase approach that transfers load in a fine-tuned manner to mitigate skew iteratively during execution, thus enabling it to handle changes in input-data distribution. Reshape has the ability to adaptively adjust skew-handling parameters, which reduces the technical burden on the users. Reshape supports a variety of operators such as HashJoin, Group-by, and Sort. We implemented Reshape on top of two big data engines, namely Amber and Flink, to demonstrate its generality and efficiency, and report an experimental evaluation using real and synthetic datasets.
Minimal Feature Analysis for Isolated Digit Recognition for varying encoding rates in noisy environments

This research work is about recent development made in speech recognition. In this research work, analysis of isolated digit recognition in the presence of different bit rates and at different noise levels has been performed. This research work has been carried using audacity and HTK toolkit. Hidden Markov Model (HMM) is the recognition model which was used to perform this experiment. The feature extraction techniques used are Mel Frequency Cepstrum coefficient (MFCC), Linear Predictive Coding (LPC), perceptual linear predictive (PLP), mel spectrum (MELSPEC), filter bank (FBANK). There were three types of different noise levels which have been considered for testing of data. These include random noise, fan noise and random noise in real time environment. This was done to analyse the best environment which can used for real time applications. Further, five different types of commonly used bit rates at different sampling rates were considered to find out the most optimum bit rate.
Cooperative coevolutionary hybrid NSGA-II with Linkage Measurement Minimization for Large-scale Multi-objective optimization

In this paper, we propose a variable grouping method based on cooperative coevolution for large-scale multi-objective problems (LSMOPs), named Linkage Measurement Minimization (LMM). And for the sub-problem optimization stage, a hybrid NSGA-II with a Gaussian sampling operator based on an estimated convergence point is proposed. In the variable grouping stage, according to our previous research, we treat the variable grouping problem as a combinatorial optimization problem, and the linkage measurement function is designed based on linkage identification by the nonlinearity check on real code (LINC-R). We extend this variable grouping method to LSMOPs. In the sub-problem optimization stage, we hypothesize that there is a higher probability of existing better solutions around the Pareto Front (PF). Based on this hypothesis, we estimate a convergence point at every generation of optimization and perform Gaussian sampling around the convergence point. The samples with good objective value will participate in the optimization as elites. Numerical experiments show that our variable grouping method is better than some popular variable grouping methods, and hybrid NSGA-II has broad prospects for multi-objective problem optimization.
Grounded Affordance from Exocentric View

Affordance grounding aims to locate objects'"action possibilities"regions, which is an essential step toward embodied intelligence. Due to the diversity of interactive affordance, the uniqueness of different individuals leads to diverse interactions, which makes it difficult to establish an explicit link between object parts and affordance labels. Human has the ability that transforms the various exocentric interactions into invariant egocentric affordance to counter the impact of interactive diversity. To empower an agent with such ability, this paper proposes a task of affordance grounding from exocentric view, i.e., given exocentric human-object interaction and egocentric object images, learning the affordance knowledge of the object and transferring it to the egocentric image using only the affordance label as supervision. However, there is some"interaction bias"between personas, mainly regarding different regions and different views. To this end, we devise a cross-view affordance knowledge transfer framework that extracts affordance-specific features from exocentric interactions and transfers them to the egocentric view. Specifically, the perception of affordance regions is enhanced by preserving affordance co-relations. In addition, an affordance grounding dataset named AGD20K is constructed by collecting and labeling over 20K images from $36$ affordance categories. Experimental results demonstrate that our method outperforms the representative models regarding objective metrics and visual quality. Code is released at https://github.com/lhc1224/Cross-view-affordance-grounding.
Degenerate fourfold Massey products over arbitrary fields

We prove that, for all fields $F$ of characteristic different from $2$ and all $a,b,c\in F^\times$, the mod $2$ Massey product $\langle a,b,c,a \rangle$ vanishes as soon as it is defined. For every field $F_0$, we construct a field $F$ containing $F_0$ and $a,b,c,d\in F^\times$ such that $\langle a,b,c \rangle$ and $\langle b,c,d \rangle$ vanish but $\langle a,b,c,d \rangle$ is not defined. As a consequence, we answer a question of Positselski by constructing the first examples of fields containing all roots of unity and such that the mod $2$ cochain DGA of the absolute Galois group is not formal.
Learning Binary and Sparse Permutation-Invariant Representations for Fast and Memory Efficient Whole Slide Image Search

Considering their gigapixel sizes, the representation of whole slide images (WSIs) for classification and retrieval systems is a non-trivial task. Patch processing and multi-Instance Learning (MIL) are common approaches to analyze WSIs. However, in end-to-end training, these methods require high GPU memory consumption due to the simultaneous processing of multiple sets of patches. Furthermore, compact WSI representations through binary and/or sparse representations are urgently needed for real-time image retrieval within large medical archives. To address these challenges, we propose a novel framework for learning compact WSI representations utilizing deep conditional generative modeling and the Fisher Vector Theory. The training of our method is instance-based, achieving better memory and computational efficiency during the training. To achieve efficient large-scale WSI search, we introduce new loss functions, namely gradient sparsity and gradient quantization losses, for learning sparse and binary permutation-invariant WSI representations called Conditioned Sparse Fisher Vector (C-Deep-SFV), and Conditioned Binary Fisher Vector (C-Deep-BFV). The learned WSI representations are validated on the largest public WSI archive, The Cancer Genomic Atlas (TCGA) and also Liver-Kidney-Stomach (LKS) dataset. For WSI search, the proposed method outperforms Yottixel and Gaussian Mixture Model (GMM)-based Fisher Vector both in terms of retrieval accuracy and speed. For WSI classification, we achieve competitive performance against state-of-art on lung cancer data from TCGA and the public benchmark LKS dataset.
Detection and Classification of Brain tumors Using Deep Convolutional Neural Networks

Abnormal development of tissues in the body as a result of swelling and morbid enlargement is known as a tumor. They are mainly classified as Benign and Malignant. Tumour in the brain is fatal as it may be cancerous, so it can feed on healthy cells nearby and keep increasing in size. This may affect the soft tissues, nerve cells, and small blood vessels in the brain. Hence there is a need to detect and classify them during the early stages with utmost precision. There are different sizes and locations of brain tumors which makes it difficult to understand their nature. The process of detection and classification of brain tumors can prove to be an onerous task even with advanced MRI (Magnetic Resonance Imaging) techniques due to the similarities between the healthy cells nearby and the tumor. In this paper, we have used Keras and Tensorflow to implement state-of-the-art Convolutional Neural Network (CNN) architectures, like EfficientNetB0, ResNet50, Xception, MobileNetV2, and VGG16, using Transfer Learning to detect and classify three types of brain tumors namely - Glioma, Meningioma, and Pituitary. The dataset we used consisted of 3264 2-D magnetic resonance images and 4 classes. Due to the small size of the dataset, various data augmentation techniques were used to increase the size of the dataset. Our proposed methodology not only consists of data augmentation, but also various image denoising techniques, skull stripping, cropping, and bias correction. In our proposed work EfficientNetB0 architecture performed the best giving an accuracy of 97.61%. The aim of this paper is to differentiate between normal and abnormal pixels and also classify them with better accuracy.
Personalized Inter-Task Contrastive Learning for CTR&CVR Joint Estimation

In most of advertising and recommendation systems, multi-task learning (MTL) paradigm is widely employed to model diverse user behaviors ( e.g., click, view, and purchase). Existing MTL models typically use task-shared networks with shared parameters or a routing mechanism to learn the commonalities between tasks while applying task-speciﬁc networks to learn the unique characteristics of each task. However, the potential relevance within task-speciﬁc networks is ignored, which is intuitively crucial for overall performance. In light of the fact that relevance is both task-complex and instance-speciﬁc, we present a novel learning paradigm to address these issues. In this paper, we pro-pose P ersonalized I nter-task CO ntrastive Learning ( PICO ) framework, which can effectively model the inter-task relationship and is utilized to jointly estimate the click-through rate (CTR) and post-click conversion rate (CVR) in advertising systems. PICO utilizes contrastive learning to integrate inter-task knowledge implicitly from the task representations in task-speciﬁc networks. In addition, we introduce an auxiliary network to capture the inter-task relevance at instance-level and transform it into personalized temperature parameters for contrastive learning. With this method, ﬁne-grained knowledge can be transferred to improve MTL performance without incurring additional inference costs. Both ofﬂine and online experiments show that PICO outperforms previous multi-task models signiﬁcantly.
Out-of-Model Adjustments of Variable Annuities

This paper studies the model risk of the Black-Scholes (BS) model in pricing and risk-managing variable annuities motivated by its wide usage in the insurance industry. Specifically, we derive a model-free decomposition of the no-arbitrage price of the variable annuity into the BS model price in conjunction with three out-of-model adjustment terms. This sheds light on all risk drivers behind the product, that is, spot price, realized volatility, future smile, and sub-optimal withdrawal. We further investigate the efficacy of the BS-based hedging strategy given the market diverges from the model assumptions. We disclose that the spot price risk can always be eliminated by the strategy and the hedger's cumulative P\&L exhibits gradual slippage and instantaneous leakage. We finally show that the pricing, risk and hedging models can be separated from each other in managing the risks of variable annuities.
Identification multi-longueurs d'onde des sources X faibles des amas globulaires

Les observations XMM-Newton et Chandra en X de trois amas globulaires galactiques (NGC 2808, NGC 4372 et M 22) sont presentees et analysees. Elles sont completees par des observations dans le visible, l'ultraviolet, l'infrarouge et le domaine radio afin d'identifier les sources X liees a ces amas globulaires. Les observations X ont permis de detecter 16 sources liees a NGC 2808 et 18 sources liees a M 22 qui sont principalement des binaires serrees. Cette population elevee de binaires aurait un role important a jouer dans le ralentissement de l'effondrement du cœur de ces amas globulaires. Une binaire X de faible masse en quiescence contenant une etoile a neutrons a ete detectee dans NGC 2808 et aucune source de ce type n'est detectee dans M 22 et NGC 4372. Ces resultats sont en accord avec la correlation deja observee entre le nombre de ces objets et le taux de rencontre d'etoiles dans le cœur des amas globulaires. Un deficit possible de sources X dans NGC 2808 semble indiquer que le taux de rencontre n'est pas le seul parametre intervenant dans la production des sources X et la metallicite de l'amas pourrait aussi avoir une influence. A partir des rapports X/UV et d'observations spectroscopiques, nous proposons differentes approches afin de resoudre le probleme du deficit d'eruptions de variables cataclysmiques deja observe dans les amas globulaires. Ces approches consistent a determiner si les variables cataclysmiques des amas globulaires sont en majorite magnetiques ou si elles ont un taux d'accretion faible. Enfin, les observations permettent d'apporter une limite superieure d'environ 400 masses solaires si un trou noir de masse intermediaire se trouvait dans NGC 2808.
ATTRITION: Attacking Static Hardware Trojan Detection Techniques Using Reinforcement Learning

Stealthy hardware Trojans (HTs) inserted during the fabrication of integrated circuits can bypass the security of critical infrastructures. Although researchers have proposed many techniques to detect HTs, several critical limitations exist, including: (i) a low success rate of HT detection, (ii) high algorithmic complexity, and (iii) a large number of test patterns. Furthermore, as we show in this work the most pertinent drawback of prior (including state-of-the-art) detection techniques stems from an incorrect evaluation methodology, i.e., they assume that an adversary inserts HTs randomly. Such inappropriate adversarial assumptions enable detection techniques to claim high HT detection accuracy, leading to a "false sense of security. " To the best of our knowledge, despite more than a decade of research on detecting HTs inserted during fabrication, there have been no concerted efforts to perform a systematic evaluation of HT detection techniques. In this paper, we play the role of a realistic adversary and question the efficacy of HT detection techniques by developing an automated, scalable, and practical attack framework, ATTRITION, using reinforcement learning (RL). ATTRITION evades eight detection techniques (published in premier security venues, well-cited in academia, etc.) across two HT detection categories, showcasing its agnostic behavior. ATTRITION achieves average attack success rates of 47x and 211x compared to randomly inserted HTs against state-of-the-art logic testing and side channel techniques. To demonstrate ATTRITION's ability in evading detection techniques, we evaluate different designs ranging from the widely-used academic suites (ISCAS-85, ISCAS-89) to larger designs such as the open-source MIPS and mor1kx processors to AES and a GPS module. Additionally, we showcase the impact of ATTRITION generated HTs through two case studies (privilege escalation and kill switch) on mor1kx processor. We envision that our work, along with our released HT benchmarks and models fosters the development of better HT detection techniques.