Miracle-dz/UniFAR-FTUs huggingface dataset:
Growing and Serving Large Open-domain Knowledge Graphs

Applications of large open-domain knowledge graphs (KGs) to real-world problems pose many unique challenges. In this paper, we present extensions to Saga our platform for continuous construction and serving of knowledge at scale. In particular, we describe a pipeline for training knowledge graph embeddings that powers key capabilities such as fact ranking, fact verification, a related entities service, and support for entity linking. We then describe how our platform, including graph embeddings, can be leveraged to create a Semantic Annotation service that links unstructured Web documents to entities in our KG. Semantic annotation of the Web effectively expands our knowledge graph with edges to open-domain Web content which can be used in various search and ranking problems. Finally, we leverage annotated Web documents to drive Open-domain Knowledge Extraction. This targeted extraction framework identifies important coverage issues in the KG, then finds relevant data sources for target entities on the Web and extracts missing information to enrich the KG. Finally, we describe adaptations to our knowledge platform needed to construct and serve private personal knowledge on-device. This includes private incremental KG construction, cross- device knowledge sync, and global knowledge enrichment.
Survey of Malware Analysis through Control Flow Graph using Machine Learning

Malware is a significant threat to the security of computer systems and networks which requires sophisticated techniques to analyze the behavior and functionality for detection. Traditional signature-based malware detection methods have become ineffective in detecting new and unknown malware due to their rapid evolution. One of the most promising techniques that can overcome the limitations of signature-based detection is to use control flow graphs (CFGs). CFGs leverage the structural information of a program to represent the possible paths of execution as a graph, where nodes represent instructions and edges represent control flow dependencies. Machine learning (ML) algorithms are being used to extract these features from CFGs and classify them as malicious or benign. In this survey, we aim to review some state-of-the-art methods for malware detection through CFGs using ML, focusing on the different ways of extracting, representing, and classifying. Specifically, we present a comprehensive overview of different types of CFG features that have been used as well as different ML algorithms that have been applied to CFG-based malware detection. We provide an in-depth analysis of the challenges and limitations of these approaches, as well as suggest potential solutions to address some open problems and promising future directions for research in this field.
AI in the Loop - Functionalizing Fold Performance Disagreement to Monitor Automated Medical Image Segmentation Pipelines

Methods for automatically flag poor performing-predictions are essential for safely implementing machine learning workflows into clinical practice and for identifying difficult cases during model training. We present a readily adoptable method using sub-models trained on different dataset folds, where their disagreement serves as a surrogate for model confidence. Thresholds informed by human interobserver values were used to determine whether a final ensemble model prediction would require manual review. In two different datasets (abdominal CT and MR predicting kidney tumors), our framework effectively identified low performing automated segmentations. Flagging images with a minimum Interfold test Dice score below human interobserver variability maximized the number of flagged images while ensuring maximum ensemble test Dice. When our internally trained model was applied to an external publicly available dataset (KiTS21), flagged images included smaller tumors than those observed in our internally trained dataset, demonstrating the methods robustness to flagging poor performing out-of-distribution input data. Comparing interfold sub-model disagreement against human interobserver values is an efficient way to approximate a model's epistemic uncertainty - its lack of knowledge due to insufficient relevant training data - a key functionality for adopting these applications in clinical practice.
Long-term adherence to polytherapy in heart failure patients: a novel approach emphasising the importance of secondary prevention

Heart failure (HF) is a severe and costly clinical syndrome associated with increased healthcare costs and a high burden of mortality and morbidity. Although drug therapy is the mainstay of treatment for heart failure, non-adherence to prescribed therapies is common and is associated with worse health outcomes and increased hospitalizations. In this study, we propose a novel approach using Latent Markov models to analyze drug adherence to polytherapy over time using a secondary database. Our methodology enables us to evaluate patients' drug utilization behaviour, identify complex behavioural patterns, and incorporate them into predictive models to improve clinical outcomes. The significance of adhering to prescribed therapies for patients' prognosis has been highlighted in this study. Our findings show that adherent patients gained an additional ten months of life over a seven-year follow-up period compared to non-adherent patients. This underscores the importance of secondary prevention and continuous monitoring of heart failure patients. These procedures are crucial for identifying areas of improvement and promoting better adherence to prescribed therapies.
Vortex wake patterns in superfluid 4He

Excitations in the form of quantized vortex rings are known to exist in superfluid 4He at energies and momenta exceeding those of the Landau phonon–roton spectrum. They form a vortex branch of elementary excitations spectrum which is disconnected from the Landau spectrum. Interference of vortex ring excitations determines wake patterns due to uniformly traveling sources in bulk superfluid at low speeds and pressures. The dispersion law of these excitations resembles that of gravity waves on deep water with infrared wave number cutoff. As a result, vortex wake patterns featuring elements of the Kelvin ship wake are predicted. Specifically, at lowest speeds the pattern with fully developed transverse and diverging wavefronts is present. At intermediate speeds transverse wavefronts are absent within a cone whose opening angle increases with the source velocity. At largest speeds only diverging wavefronts confined within a cone whose opening angle decreases with the source velocity are found. When experimentally observed, these changes in appearance of wake patterns serve as indicators of the beginning part of the vortex branch of elementary excitations.
Modelling Human Visual Motion Processing with Trainable Motion Energy Sensing and a Self-attention Network for Adaptive Motion Integration

Visual motion processing is essential for humans to perceive and interact with dynamic environments. Despite extensive research in cognitive neuroscience, image-computable models that can extract informative motion flow from natural scenes in a manner consistent with human visual processing have yet to be established. Meanwhile, recent advancements in computer vision (CV), propelled by deep learning, have led to significant progress in optical flow estimation, a task closely related to motion perception. Here we propose an image-computable model of human motion perception by bridging the gap between biological and CV models. Specifically, we introduce a novel two-stages approach that combines trainable motion energy sensing with a recurrent self-attention network for adaptive motion integration and segregation. This model architecture aims to capture the computations in V1-MT, the core structure for motion perception in the biological visual system, while providing the ability to derive informative motion flow for a wide range of stimuli, including complex natural scenes. In silico neurophysiology reveals that our model's unit responses are similar to mammalian neural recordings regarding motion pooling and speed tuning. The proposed model can also replicate human responses to a range of stimuli examined in past psychophysical studies. The experimental results on the Sintel benchmark demonstrate that our model predicts human responses better than the ground truth, whereas the state-of-the-art CV models show the opposite. Our study provides a computational architecture consistent with human visual motion processing, although the physiological correspondence may not be exact.
Photoinduced dynamics of flat bands in the kagome metal CoSn

CoSn is a prototypical kagome compound showing lattice-born flat bands with suppressed bandwidth over large parts of the Brillouin zone. Here, by means of time- and angle-resolved photoelectron spectroscopy, we provide direct evidence of the response to photoexcitation of the flat bands, that underlie information about localization in real space. In particular, we detect a sudden shift and broadening of the flat bands, while after one picosecond only a broadening survives. We ascribe both these effects to an ultrafast disruption of electron localization, which renormalizes the effective electron-electron interaction and affects the flat band dispersion. Since both variations are in the order of a few meV, our measurements suggest that the flat bands are resilient to near-infrared photoexcitation.
Releasing Inequality Phenomena in L∞-Adversarial Training via Input Gradient Distillation

Since adversarial examples appeared and showed the catastrophic degradation they brought to DNN, many adversarial defense methods have been devised, among which adversarial training is considered the most effective. However, a recent work showed the inequality phenomena in $l_{\infty}$-adversarial training and revealed that the $l_{\infty}$-adversarially trained model is vulnerable when a few important pixels are perturbed by i.i. d. noise or occluded. In this paper, we propose a simple yet effective method called Input Gradient Distillation (IGD) to release the inequality phenomena in $l_{\infty}$-adversarial training. Experiments show that while preserving the model's adversarial robustness, compared to PGDAT, IGD decreases the $l_{\infty}$-adversarially trained model's error rate to inductive noise and inductive occlusion by up to 60\% and 16.53\%, and to noisy images in Imagenet-C by up to 21.11\%. Moreover, we formally explain why the equality of the model's saliency map can improve such robustness.
Non-Hermitian Casimir Effect of Magnons

There has been a growing interest in non-Hermitian quantum mechanics. The key concepts of quantum mechanics are quantum fluctuations. Quantum fluctuations of quantum fields confined in a finite-size system induce the zero-point energy shift. This quantum phenomenon, the Casimir effect, is one of the most striking phenomena of quantum mechanics in the sense that there are no classical analogs and has been attracting much attention beyond the hierarchy of energy scales, ranging from elementary particle physics to condensed matter physics, together with photonics. However, the non-Hermitian extension of the Casimir effect and the application to spintronics have not yet been investigated enough, although exploring energy sources and developing energy-efficient nanodevices are its central issues. Here we fill this gap. By developing a magnonic analog of the Casimir effect into non-Hermitian systems, we show that this non-Hermitian Casimir effect of magnons is enhanced as the Gilbert damping constant (i.e., the energy dissipation rate) increases. When the damping constant exceeds a critical value, the non-Hermitian Casimir effect of magnons exhibits an oscillating behavior, including a beating one, as a function of the film thickness and is characterized by the exceptional point. Our result suggests that energy dissipation serves as a key ingredient of Casimir engineering.
What's the Problem, Linda? The Conjunction Fallacy as a Fairness Problem

The field of Artificial Intelligence (AI) is focusing on creating automated decision-making (ADM) systems that operate as close as possible to human-like intelligence. This effort has pushed AI researchers into exploring cognitive fields like psychology. The work of Daniel Kahneman and the late Amos Tversky on biased human decision-making, including the study of the conjunction fallacy, has experienced a second revival because of this. Under the conjunction fallacy a human decision-maker will go against basic probability laws and rank as more likely a conjunction over one of its parts. It has been proven overtime through a set of experiments with the Linda Problem being the most famous one. Although this interdisciplinary effort is welcomed, we fear that AI researchers ignore the driving force behind the conjunction fallacy as captured by the Linda Problem: the fact that Linda must be stereotypically described as a woman. In this paper we revisit the Linda Problem and formulate it as a fairness problem. In doing so we introduce perception as a parameter of interest through the structural causal perception framework. Using an illustrative decision-making example, we showcase the proposed conceptual framework and its potential impact for developing fair ADM systems.
UniS-MMC: Multimodal Classification via Unimodality-supervised Multimodal Contrastive Learning

Multimodal learning aims to imitate human beings to acquire complementary information from multiple modalities for various downstream tasks. However, traditional aggregation-based multimodal fusion methods ignore the inter-modality relationship, treat each modality equally, suffer sensor noise, and thus reduce multimodal learning performance. In this work, we propose a novel multimodal contrastive method to explore more reliable multimodal representations under the weak supervision of unimodal predicting. Specifically, we first capture task-related unimodal representations and the unimodal predictions from the introduced unimodal predicting task. Then the unimodal representations are aligned with the more effective one by the designed multimodal contrastive method under the supervision of the unimodal predictions. Experimental results with fused features on two image-text classification benchmarks UPMC-Food-101 and N24News show that our proposed Unimodality-Supervised MultiModal Contrastive UniS-MMC learning method outperforms current state-of-the-art multimodal methods. The detailed ablation study and analysis further demonstrate the advantage of our proposed method.
Towards Speech Dialogue Translation Mediating Speakers of Different Languages

We present a new task, speech dialogue translation mediating speakers of different languages. We construct the SpeechBSD dataset for the task and conduct baseline experiments. Furthermore, we consider context to be an important aspect that needs to be addressed in this task and propose two ways of utilizing context, namely monolingual context and bilingual context. We conduct cascaded speech translation experiments using Whisper and mBART, and show that bilingual context performs better in our settings.
Optimizing Pre-Scheduled, Intermittently-Observed MDPs

A challenging category of robotics problems arises when sensing incurs substantial costs. This paper examines settings in which a robot wishes to limit its observations of state, for instance, motivated by specific considerations of energy management, stealth, or implicit coordination. We formulate the problem of planning under uncertainty when the robot's observations are intermittent but their timing is known via a pre-declared schedule. After having established the appropriate notion of an optimal policy for such settings, we tackle the problem of joint optimization of the cumulative execution cost and the number of state observations, both in expectation under discounts. To approach this multi-objective optimization problem, we introduce an algorithm that can identify the Pareto front for a class of schedules that are advantageous in the discounted setting. The algorithm proceeds in an accumulative fashion, prepending additions to a working set of schedules and then computing incremental changes to the value functions. Because full exhaustive construction becomes computationally prohibitive for moderate-sized problems, we propose a filtering approach to prune the working set. Empirical results demonstrate that this filtering is effective at reducing computation while incurring only negligible reduction in quality. In summarizing our findings, we provide a characterization of the run-time vs quality trade-off involved.
Quantization of a New Canonical, Covariant, and Symplectic Hamiltonian Density

We generalize Koopman–von Neumann classical mechanics to poly symplectic fields and recover De Donder–Weyl’s theory. Compared with Dirac’s Hamiltonian density, it inspires a new Hamiltonian formulation with a canonical momentum field that is Lorentz-covariant with symplectic geometry. We provide commutation relations for the classical and quantum fields that generalize the Koopman–von Neumann and Heisenberg algebras. The classical algebra requires four fields that generalize spacetime, energy–momentum, frequency–wavenumber, and the Fourier conjugate of energy–momentum. We clarify how first and second quantization can be found by simply mapping between operators in classical and quantum commutator algebras.
Terahertz spin conductance probes of coherent and incoherent spin tunneling through MgO tunnel junctions

We study femtosecond spin currents through MgO tunneling barriers in CoFeB(2 nm)|MgO($d$)|Pt(2 nm) stacks by terahertz emission spectroscopy. To obtain transport information independent of extrinsic experimental factors, we determine the complex-valued spin conductance $\tilde{G}_d (\omega)$ of the MgO layer (thickness d= 0-6 {\AA} over a wide frequency range $(\omega/2\pi=$ 0.5-8 THz). In the time $(t)$ domain,$ G_d (t)$ has an instantaneous and delayed component that point to (i) spin transport through Pt pinholes in MgO, (ii) coherent spin tunneling and (iii) incoherent resonant spin tunneling mediated by defect states in MgO. A remarkable signature of (iii) is its relaxation time that grows monotonically with $d$ to as much as 270 fs at $d= 6$ {\AA}, in full agreement with an analytical model. Our results indicate that terahertz spin conductance spectroscopy will yield new and relevant insights into ultrafast spin transport for a wide range of materials.
Bounded KRnet and its applications to density estimation and approximation

In this paper, we develop an invertible mapping, called B-KRnet, on a bounded domain and apply it to density estimation/approximation for data or the solutions of PDEs such as the Fokker-Planck equation and the Keller-Segel equation. Similar to KRnet, the structure of B-KRnet adapts the triangular form of the Knothe-Rosenblatt rearrangement into a normalizing flow model. The main difference between B-KRnet and KRnet is that B-KRnet is defined on a hypercube while KRnet is defined on the whole space, in other words, we introduce a new mechanism in B-KRnet to maintain the exact invertibility. Using B-KRnet as a transport map, we obtain an explicit probability density function (PDF) model that corresponds to the pushforward of a prior (uniform) distribution on the hypercube. To approximate PDFs defined on a bounded computational domain, B-KRnet is more effective than KRnet. By coupling KRnet and B-KRnet, we can also define a deep generative model on a high-dimensional domain where some dimensions are bounded and other dimensions are unbounded. A typical case is the solution of the stationary kinetic Fokker-Planck equation, which is a PDF of position and momentum. Based on B-KRnet, we develop an adaptive learning approach to approximate partial differential equations whose solutions are PDFs or can be regarded as a PDF. In addition, we apply B-KRnet to density estimation when only data are available. A variety of numerical experiments is presented to demonstrate the effectiveness of B-KRnet.
Quantum Monte Carlo for gauge fields and matter without the fermion determinant

Ab-initio Monte Carlo simulations of strongly-interacting fermionic systems are plagued by the fermion sign problem, making the non-perturbative study of many interesting regimes of dense quantum matter, or of theories of odd numbers of fermion flavors, challenging. Moreover, typical fermion algorithms require the computation (or sampling) of the fermion determinant. We focus instead on the meron cluster algorithm, which can solve the fermion sign problem in a class of models without involving the determinant. We develop and benchmark new meron algorithms to simulate fermions coupled to $\mathbb{Z}_2$ and $U(1)$ gauge fields in the presence of appropriate four-fermi interactions. Such algorithms can be used to uncover potential exotic properties of matter, particularly relevant for quantum simulator experiments. We demonstrate the emergence of the Gauss' Law at low temperatures for a $U(1)$ model in $(1+1)-$d.
IMPLEMENTASI METODE HIERARCHICAL CLUSTERING PADA DATA GENETIK MIKROARRAY

Teknologi Mikroarray menghasilkan data genetik mikroarray, yang jumlahnya akan semakin banyak dari hari ke hari. Jika dikelola dengan baik, data genetik mikroarray yang dihasilkan akan sangat bermanfaat dalam bidang kesehatan. Namun, jumlah data yang terus bertambah akan mempersulit dalam pengambilan informasi yang berguna. Pada kajian ini, data genetik mikroarray dikelola dengan menggunakan metoda Agglomerative Hierarchical Clustering. Dengan metoda ini, data genetik mikroarray dikelompokkan ke dalam clusteringclustering. Gen-gen dengan ekspresi gen yang mirip akan dikelompokkan ke dalam satu clustering. Hasil metoda Agglomerative Hierarchical Clustering adalah dendogram yang menggambarkan clustering-clustering dan hubungan antar clustering tersebut. Algoritma yang digunakan pada Hierarchical Clustering adalah Hierarchical Clustering Algorithm. Pada algoritma ini dibutuhkan Proximity Matrix yang menyajikan jarak antar gen. Jarak antar gen dapat dihitung dengan menggunakan tiga fungsi yaitu Euclidean Distance, Manhattan Distance dan Pearson Correlation. Gen–gen dengan jarak terdekat akan digabungkan dalam satu clustering. Untuk menghitung jarak antar cluster yang baru terbentuk dengan gen-gen lain dapat digunakan tiga algoritma yaitu Single Linkage, Complete Linkage dan Average Linkage. Clustering yang dihasilkan dengan menggunakan algoritma penghitungan jarak antar clustering yang berbeda, akan mempengaruhi kepadatan hasil clustering dan kesamaan hasil clustering.