Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple Kernel Clustering with Dual Noise Minimization

Clustering is a representative unsupervised method widely applied in multi-modal and multi-view scenarios. Multiple kernel clustering (MKC) aims to group data by integrating complementary information from base kernels. As a representative, late fusion MKC first decomposes the kernels into orthogonal partition matrices, then learns a consensus one from them, achieving promising performance recently. However, these methods fail to consider the noise inside the partition matrix, preventing further improvement of clustering performance. We discover that the noise can be disassembled into separable dual parts, i.e. N-noise and C-noise (Null space noise and Column space noise). In this paper, we rigorously define dual noise and propose a novel parameter-free MKC algorithm by minimizing them. To solve the resultant optimization problem, we design an efficient two-step iterative strategy. To our best knowledge, it is the first time to investigate dual noise within the partition in the kernel space. We observe that dual noise will pollute the block diagonal structures and incur the degeneration of clustering performance, and C-noise exhibits stronger destruction than N-noise. Owing to our efficient mechanism to minimize dual noise, the proposed algorithm surpasses the recent methods by large margins.
Optimal segmentation of unbounded functions

- We study a variational problem connected to image segmentation in computer vision. More precisely, assuming that Q is open n Llo~ ( S~ ) and 1 &#x3E; 0, we prove that the functional achieves its minimum on pairs (C, u ) with C closed and u constant on each connected component of Moreover, we show that the family of connected components of is locally finite in Q, for any minimizer (K, w) of F.
Load Balancing in Cloud Systems Using Dynamic Chained Failover Algorithm

Large-scaled cloud systems have been there in various domains by extendinglarge number of resources. Efficient allocation of shared assets in cloud is a vital but provocative issue. It is known that existing load balancing policies like Random,size-based polices, Join Shortest Queue are implemented in cloud as they are simple and efficient. The performance of the above mentioned policies decrease when workloads are temporally correlated. We propose the new load balancing method, Dynamic Chained Failover Algorithm in this paper where particular time period at which the server is getting overloaded is taken and particular task which causes overloading in particular interval is placed in all servers as a replica instead of data reallocation. Overall system performance is increased. Exploiting capable server to improve the system performance is thus demanded.
Stochastic Average Model Methods

We consider the solution of finite-sum minimization problems, such as those appearing in nonlinear least-squares or general empirical risk minimization problems. We are motivated by problems in which the summand functions are computationally expensive and evaluating all summands on every iteration of an optimization method may be undesirable. We present the idea of stochastic average model (SAM) methods, inspired by stochastic average gradient methods. SAM methods sample component functions on each iteration of a trust-region method according to a discrete probability distribution on component functions; the distribution is designed to minimize an upper bound on the variance of the resulting stochastic model. We present promising numerical results concerning an implemented variant extending the derivative-free model-based trust-region solver POUNDERS, which we name SAM-POUNDERS.
Multi-modal Depression Estimation based on Sub-attentional Fusion

Failure to timely diagnose and effectively treat depression leads to over 280 million people suffering from this psychological disorder worldwide. The information cues of depression can be harvested from diverse heterogeneous resources, e.g., audio, visual, and textual data, raising demand for new effective multi-modal fusion approaches for automatic estimation. In this work, we tackle the task of automatically identifying depression from multi-modal data and introduce a sub-attention mechanism for linking heterogeneous information while leveraging Convolutional Bidirectional LSTM as our backbone. To validate this idea, we conduct extensive experiments on the public DAIC-WOZ benchmark for depression assessment featuring different evaluation modes and taking gender-specific biases into account. The proposed model yields effective results with 0.89 precision and 0.70 F1-score in detecting major depression and 4.92 MAE in estimating the severity. Our attention-based fusion module consistently outperforms conventional late fusion approaches and achieves competitive performance compared to the previously published depression estimation frameworks, while learning to diagnose the disorder end-to-end and relying on far fewer preprocessing steps.
Fast Numerical Integration Techniques for 2.5-Dimensional Inverse Problems

Inverse scattering involving microwave and ultrasound waves require numerical solution of nonlinear optimization problem. To alleviate the computational burden of a full three-dimensional (3-D) inverse problem, it is a common practice to approximate the object as two-dimensional (2-D) and treat the transmitter and receiver sensors as 3-D, through a Fourier integration of 2-D modes of scattering. The resulting integral is singular, and hence requires a prohibitively large number of integration points, where each point corresponds to a 2-D solution. To reduce the computational complexity, this paper proposes fast integration approaches by a set of transformations. We model the object in 2-D but the transmit and receiver pairs as 3-D; hence, we term the solution as a 2.5-D inverse problem. Convergence results indicate that the proposed integration techniques have exponential convergence and hence have a reduces the computational complexity to compute 2.5-D Green's function to solve inverse scattering problems.
Beyond Hard Labels: Investigating data label distributions

High-quality data is a key aspect of modern machine learning. However, labels generated by humans suffer from issues like label noise and class ambiguities. We raise the question of whether hard labels are sufficient to represent the underlying ground truth distribution in the presence of these inherent imprecision. Therefore, we compare the disparity of learning with hard and soft labels quantitatively and qualitatively for a synthetic and a real-world dataset. We show that the application of soft labels leads to improved performance and yields a more regular structure of the internal feature space.
LiveSchema: A Gateway Towards Learning on Knowledge Graph Schemas

One of the major barriers to the training of algorithms on knowledge graph schemas, such as vocabularies or ontologies, is the difficulty that scientists have in finding the best input resource to address the target prediction tasks. In addition to this, a key challenge is to determine how to manipulate (and embed) these data, which are often in the form of particular triples (i.e., subject, predicate, object), to enable the learning process. In this paper, we describe the LiveSchema initiative, namely a gateway that offers a family of services to easily access, analyze, transform and exploit knowledge graph schemas, with the main goal of facilitating the reuse of these resources in machine learning use cases. As an early implementation of the initiative, we also advance an online catalog, which relies on more than 800 resources, with the first set of example services.
Rough homogenization for Langevin dynamics on fluctuating Helfrich surfaces

In this paper, we study different scaling rough path limit regimes in space and time for the Langevin dynamics on a quasi-planar fluctuating Helfrich surfaces. The convergence results of the processes were already proven in the work by Duncan, Elliott, Pavliotis and Stuart (2015). We extend this work by proving the convergence of the It\^o and Stratonovich rough path lift. For the rough path limit, there appears, typically, an area correction term to the It\^o iterated integrals, and in certain regimes to the Stratonovich iterated integrals. This yields additional information on the homogenization limit and enables to conclude on homogenization results for diffusions driven by the Brownian motion on the membrane using the continuity of the It\^o-Lyons map in rough paths topology.
Exploiting Word Semantics to Enrich Character Representations of Chinese Pre-trained Models

Most of the Chinese pre-trained models adopt characters as basic units for downstream tasks. However, these models ignore the information carried by words and thus lead to the loss of some important semantics. In this paper, we propose a new method to exploit word structure and integrate lexical semantics into character representations of pre-trained models. Speciﬁcally, we project a word’s embedding into its internal characters’ embeddings according to the similarity weight. To strengthen the word boundary information, we mix the representations of the internal characters within a word. After that, we apply a word-to-character alignment attention mechanism to emphasize important characters by masking unimportant ones. Moreover, in order to reduce the error propagation caused by word segmentation, we present an ensemble approach to combine segmentation results given by diﬀerent tokenizers. The experimental results show that our approach achieves superior performance over the basic pre-trained models BERT, BERT-wwm and ERNIE on diﬀerent Chinese NLP tasks: sentiment classiﬁcation, sentence pair matching, natural language inference and machine reading comprehension. We make further analysis to prove the eﬀectiveness of each component of our model.
The Classification of Cancer Gene using Hybrid Method of Machine Learning

: The purpose of this research work is to develop a method of classifying cancer using Gene expression data that is being used to gather information from tissue samples is expected to significantly improve the development of efficient cancer diagnosis and to provide understanding and insight into cancer related cellular processes. In this research, we propose a method for selection which uses factor analysis to further improve the SVM-based classification performance of gene expression data. We examine two sets of published gene expression data to validate the new feature selection method by means of Machine Learning with Binary Classification i.e. SVM classifier with different parameters. Experiments show that the proposed method can select a small quantity of principal factors to represent a large number of genes and SVM has a superior classification performance with the common factors which are extracted from gene expression data. Moreover, experiment results demonstrate successful cross validation accuracy of 93.75% for the breast cancer dataset and 98% for the leukemia dataset.
Hierarchy exploitation to detect missing annotations on hierarchical multi-label classification

The availability of genomic data has grown exponentially in the last decade, mainly due to the development of new sequencing tech-nologies. Based on the interactions between genes (and gene products) extracted from the increasing genomic data, numerous studies have focused on the identiﬁcation of associations between genes and functions. While these studies have shown great promise, the problem of annotating genes with functions remains an open challenge. In this work, we present a method to detect missing annotations in hierarchical multi-label classiﬁcation datasets. We propose a method that exploits the class hierarchy by computing aggregated probabilities to the paths of classes from the leaves to the root for each instance. The proposed method is presented in the context of predicting missing gene function annotations, where these aggregated probabilities are further used to select a set of annotations to be veriﬁed through in vivo experiments. The experiments on Oriza sativa Japonica , a variety of rice, showcase that incorporating the hierarchy of classes into the method often improves the predictive performance and our proposed method yields superior results when compared to competitor methods from the literature.
Understanding Unfairness in Fraud Detection through Model and Data Bias Interactions

In recent years, machine learning algorithms have become ubiq-uitous in a multitude of high-stakes decision-making applications. The unparalleled ability of machine learning algorithms to learn patterns from data also enables them to incorporate biases embedded within. A biased model can then make decisions that disproportionately harm certain groups in society — limiting their access to financial services, for example. The awareness of this problem has given rise to the field of Fair ML, which focuses on studying, measuring, and mitigating unfairness in algorithmic prediction, with respect to a set of protected groups ( e.g. , race or gender). How-ever, the underlying causes for algorithmic unfairness still remain elusive, with researchers divided between blaming either the ML algorithms or the data they are trained on. In this work, we maintain that algorithmic unfairness stems from interactions between models and biases in the data, rather than from isolated contributions of either of them. To this end, we propose a taxonomy to characterize data bias and we study a set of hypotheses regarding the fairness-accuracy trade-offs that fairness-blind ML algorithms exhibit under different data bias settings. On our real-world account-opening fraud use case, we find that each setting entails specific trade-offs, affecting fairness in expected value and variance — the latter often going unnoticed. Moreover, we show how algorithms compare dif-ferently in terms of accuracy and fairness, depending on the biases affecting the data. Finally, we note that under specific data bias conditions, simple pre-processing interventions can successfully balance group-wise error rates, while the same techniques fail in more complex settings.
SURIMI: Supervised Radio Map Augmentation with Deep Learning and a Generative Adversarial Network for Fingerprint-based Indoor Positioning

Indoor Positioning based on Machine Learning has drawn increasing attention both in the academy and the industry as meaningful information from the reference data can be extracted. Many researchers are using supervised, semi-supervised, and unsupervised Machine Learning models to reduce the positioning error and offer reliable solutions to the end-users. In this article, we propose a new architecture by combining Convolutional Neural Network (CNN), Long short-term memory (LSTM) and Generative Adversarial Network (GAN) in order to increase the training data and thus improve the position accuracy. The proposed combination of supervised and unsupervised models was tested in 17 public datasets, providing an extensive analysis of its performance. As a result, the positioning error has been reduced in more than 70% of them.
Estimating the Power Consumption of Heterogeneous Devices when performing AI Inference

Modern-day life is driven by electronic devices connected to the internet. The emerging research field of the Internet-of-Things (IoT) has become popular, just as there has been a steady increase in the number of connected devices. Since many of these devices are utilised to perform CV tasks, it is essential to understand their power consumption against performance. We report the power consumption profile and analysis of the NVIDIA Jetson Nano board while performing object classification. The authors present an extensive analysis regarding power consumption per frame and the output in frames per second using YOLOv5 models. The results show that the YOLOv5n outperforms other YOLOV5 variants in terms of throughput (i.e. 12.34 fps) and low power consumption (i.e. 0.154 mWh/frame).
Color Gradients and Half-mass Radii of Galaxies Out to z = 2 in the CANDELS/3D-HST Fields: Further Evidence for Important Differences in the Evolution of Mass-weighted and Light-weighted Sizes

Recent studies have indicated that the ratio between half-mass and half-light radii, r mass/r light, varies significantly as a function of stellar mass and redshift, complicating the interpretation of the ubiquitous r light − M * relation. To investigate, in this study we construct the light and color profiles of ∼3000 galaxies at 1 < z < 2 with logM*/M⊙>10.25 using imcascade, a Bayesian implementation of the Multi-Gaussian Expansion (MGE) technique. imcascade flexibly represents galaxy profiles using a series of Gaussians, free of any a priori parameterization. We find that both star-forming and quiescent galaxies have, on average, negative color gradients. For star-forming galaxies, we find steeper gradients that evolve with redshift and correlate with dust content. Using the color gradients as a proxy for gradients in the M/L ratio, we measure half-mass radii for our sample of galaxies. There is significant scatter in individual r mass/r light ratios, which is correlated with variation in the color gradients. We find that the median r mass/r light ratio evolves from 0.75 at z = 2 to 0.5 at z = 1, consistent with previous results. We characterize the r mass − M * relation, and we find that it has a shallower slope and shows less redshift evolution than the r light − M * relation. This applies both to star-forming and quiescent galaxies. We discuss some of the implications of using r mass instead of r light, including an investigation of the size−inclination bias and a comparison to numerical simulations.
Shock cooling emission from explosions of red super-giants: I. A numerically calibrated analytic model

Supernova light curves are dominated at early time, hours to days, by photons escaping from the expanding shock heated envelope. We provide a simple analytic description of the time dependent luminosity, L, and color temperature, Tcol, for explosions of red supergiants (with convective polytropic envelopes), valid up to H recombination (T ≈ 0.7 eV). The analytic description interpolates between existing expressions valid at different (planar then spherical) stages of the expansion, and is calibrated against numerical hydrodynamic diffusion calculations for a wide range of progenitor parameters (mass, radius, core/envelope mass and radius ratios, metalicity), and explosion energies. The numerically derived L and Tcol are described by the analytic expressions with $10{{\%}}$ and $5{{\%}}$ accuracy respectively. Tcol is inferred from the hydrodynamic profiles using frequency independent opacity, based on tables we constructed for this purpose (and will be made publicly available) including bound-bound and bound-free contributions. In an accompanying paper (Paper II) we show, using a large set of multi-group photon diffusion calculations, that the spectral energy distribution is well described by a Planck spectrum with T = Tcol, except at UV frequencies, where the flux can be significantly suppressed due to strong line absorption. We defer the full discussion of the multi-group results to paper II, but provide here for completeness an analytic description also of the UV suppression. Our analytic results are a useful tool for inferring progenitor properties, explosion velocity, and also relative extinction based on early multi-band shock cooling observations of supernovae.
Unusual compression behavior of TiO2 polymorphs from first principles

The physical mechanisms behind the reduction of the bulk modulus of a high-pressure cubic TiO2 phase are confirmed by first-principles calculations. An unusual and abrupt change occurs in the dependence of energy on pressure at 43 GPa, indicating a pressure-induced phase transition from columbite TiO2 to a newly-identified modified fluorite TiO2 with a Pca21 symmetry. Oxygen atom displacement in Pca21 TiO2 unexpectedly reduces the bulk modulus by 34% relative to fluorite TiO2. This discovering provides a direct evidence for understanding the compressive properties of such groups of homologous materials
L AGRANGIAN M ETHOD FOR E PISODIC L EARNING

This paper considers the problem of learning optimal value functions for finitetime decision tasks via saddle-point optimization of a nonlinear Lagrangian function that is derived from the Q-form Bellman optimality equation. Despite a long history of research on this topic in the literature, previous works on this general approach have been focusing on a linear special case known as the linear programming approach to RL/MDP. Our paper brings new perspectives to this general approach in the following aspects: 1) Inspired by the usually-used linear V -form Lagrangian, we proposed a nonlinear Q-form Lagrangian function and proved that it enjoys strong duality property in spite of its nonlinearity. The Lagrangian duality property immediately leads to a new imitation learning algorithm, which we applied to Machine Translation and obtained favorable performance on standard MT benchmark. 2) We pointed out a fundamental limit in existing works which exclusively seeks to find the minimax-type saddle points of the Lagrangian function. We however proved that another class of saddle points, the maximin-type ones, turn out to have better optimality property. 3) In contrast to most previous works, our theory and algorithm are oriented to the undiscounted episode-wise reward, which is practically more relevant than the usually considered discounted-MDP setting, thus have filled a gap between theory and practice on the topic.
Constraint-based Gait Control Using Differential Inequalities

—This paper proposes a bipedal walking controller based on quadratic programming and differential inequalities to keep the gait balance during the complete walking cycle. We design the differential inequalities constraints using geometric primitives within the dual quaternion algebra. Simulations on the lower body of the Poppy humanoid robot show the effectiveness of the approach.
The Use of Dietary Mint Leaves Powder ( Mentha piperita ) as a Phytobiotic on Intestinal Histomorphology of Broiler Chickens

— This study aimed to determine the effect of dietary mint leaves powder on the intestinal histomorphology of broiler chicken. The materials used in this study were 200 one-day-old male broiler chickens (Lohmann strain) that were kept for 35 days. The method used was an experiment using a completely randomized design with five treatments and five replications (each replication consisted of 8 birds). The treatments used were Control: basal diet without feed additive, AGP: basal diet + 0.01% zinc bacitracin, MLP0.5: basal diet + 0.5% mint leaves powder, MLP1.0: basal diet + 1% mint leaves powder, and MLP1.5: basal diet + 1.5% mint leaves powder. The variables observed in this study were villous height, basal villous width, apical villous width, villous number, villous surface area, and crypt depth. The results showed that the administration of peppermint leaves powder could increase apical villous width (P<0.05) and villous number (P<0.01). However, the treatments did not affect (P>0.05) on villous height, basal villous width, crypt depth, and villous surface area. It could be concluded that the use of mint leaves powder could beneficially increase the villous number of broiler chickens.