Miracle-dz/UniFAR-FTUs huggingface dataset:
The Insurance Role of Shareholder Litigation Rights: Implications for Market Efficiency and Misreporting Incentives

This paper studies the economic effects of endowing a large investor with litigation rights for her incentives to acquire and trade on private information. The anticipation of ex post reimbursement lowers the expected cost of purchasing stock, which leads the investor to take a larger position upon observing favorable private information, revealing more information. However, under certain circumstances the investor's incentives to acquire information weaken. Litigation rights improve (impair) market efficiency if the investor's information acquisition costs are large (small). In addition, a myopic manager's incentives to misreport financial information are inversely proportional to market efficiency, implying that litigation rights lead to less (more) misreporting for large (small) information acquisition costs. The paper also shows that the variation of stock returns to earnings announcements can be used for the empirical testing for the outlined effects.
Modelling of the Effect of Concentrated Nitration Conditions on the Efficiency of the Production of 3,7-Dinitro-1,3,5,7-tetraazabicyclo[3,3,1]nonane (DPT)

Table S2. Data of chromatograms obtained for DPT containing HMX and α-HMX The chromatogram obtained for DPT (28.3979 g) containing HMX (0.0156 g) (Figure 6S) Region Retention time (min) related to 1 2.52 solvent used 2 4.15 DPT 3 5.19 HMX impurity The chromatogram obtained for DPT (28.3979 g) containing α-HMX (0.0275 g) (Figure 7S) Region Retention time (min) related to 1 4.95 DPT 2 5.37 α-HMX 3 9.28 RDX impurity
Partial replacement of wheat flour by pumpkin seed flour in the production of cupcakes filled with carob

Abstract Pumpkin seeds are considered waste and its composition has high protein content, being feasible for human consumption, also reducing costs in waste treatment. In this work we provided an alternative to reuse the wastes of pumpkin seed applied in cupcakes formulation as a complementary source to wheat flour. Four formulations with different proportions of pumpkin seed flour were investigated. The effects of partial wheat flour substitution were evaluated using the parameters texture, height, diameter, specific volume, color, proximate composition and scanning electron microscopy. The formulations used in this work consisted of a control, prepared with wheat flour and three provided of 25%, 50% and 75% of pumpkins seed flour in partial in partial replacement to wheat flour. The incorporation of pumpkin seed flour influenced products significantly (p <0.05), providing greater texture, cohesion, pH, soluble solids (Brix). According to results, the formulation with 50% pumpkin seed flour was considered the best proportion due to similarities with the formulation of 100% wheat flour besides high protein.
A scaling law of multilevel evolution: how the balance between within- and among-collective evolution is determined

Abstract Numerous living systems are hierarchically organized, whereby replicating components are grouped into reproducing collectives—e.g., organelles are grouped into cells, and cells are grouped into multicellular organisms. In such systems, evolution can operate at two levels: evolution among collectives, which tends to promote selfless cooperation among components within collectives (called altruism), and evolution within collectives, which tends to promote cheating among components within collectives. The balance between within- and among-collective evolution thus exerts profound impacts on the fitness of these systems. Here, we investigate how this balance depends on the size of a collective (denoted by N) and the mutation rate of components (m) through mathematical analyses and computer simulations of multiple population genetics models. We first confirm a previous result that increasing N or m accelerates within-collective evolution relative to among-collective evolution, thus promoting the evolution of cheating. Moreover, we show that when within- and among-collective evolution exactly balance each other out, the following scaling relation generally holds: Nmα is a constant, where scaling exponent α depends on multiple parameters, such as the strength of selection and whether altruism is a binary or quantitative trait. This relation indicates that although N and m have quantitatively distinct impacts on the balance between within- and among-collective evolution, their impacts become identical if m is scaled with a proper exponent. Our results thus provide a novel insight into conditions under which cheating or altruism evolves in hierarchically organized replicating systems.
Hot Corino Chemistry in the Class I Binary Source Ser-emb 11

We report the detection of more than 120 emission lines corresponding to eight complex organic molecules (COMs; CH3OH, CH3CH2OH, CH3OCH3, CH3OCHO, CH3COCH3, NH2CHO, CH2DCN, and CH3CH2CN) and three isotopologues (CH2DOH, 13CH3CN, and CH3C15N) toward the western component of the Ser-emb 11 binary young stellar object using observations with the Atacama Large Millimeter/submillimeter Array at ∼1 mm. The complex organic emission was unresolved with a ∼0. ″5 beam (∼220 au) in a compact region around the central protostar, and a population diagram analysis revealed excitation temperatures above 100 K for all COMs, indicating the presence of a hot corino. The estimated column densities were in the range of 1017−1018 cm−2 for the O-bearing COMs, and three orders of magnitude lower for the N-bearing species. We also report the detection of H2CO and CH3OH emission in a nearby millimeter source that had not been previously cataloged. Ser-emb 11 is classified in the literature as a Class I source near the Class 0/I cutoff. The estimated COM relative abundances in Ser-emb 11 W and the other three Class I hot corino sources reported in the literature are consistent with those of Class 0 hot corinos, suggesting a continuity in the chemical composition of hot corinos during protostellar evolution.
An Anomaly-based Multi-class Classifier for Network Intrusion Detection

—Network intrusion detection systems (NIDS) are one of several solutions that make up a computer security system. They are responsible for inspecting network trafﬁc and triggering alerts when detecting intrusion attempts. One of the most popular approaches in NIDS research today is the Anomaly-based technique, characterized by the ability to recognize previously unobserved attacks. Some A-NIDS systems go beyond the separation into normal and anomalous classes by trying to identify the type of detected anomalies. This is an important capability of a security system, as it allows a more effective response to an intrusion attempt. The existing systems with this ability are often subject to limitations such as high complexity and incorrect labeling of unknown attacks. In this work, we propose an algorithm to be used in NIDS that overcomes these limitations. Our proposal is an adaptation of the Anomaly-based classiﬁer EFC to perform multi-class classiﬁcation. It has a single layer, with low temporal complexity, and can correctly classify not only the known attacks, but also unprecedented attacks. Our proposal was evaluated in two up-to-date ﬂow-based intrusion detection datasets: CIDDS-001 and CICIDS2017. We also conducted a speciﬁc experiment to assess our classiﬁer’s ability to correctly label unknown attacks. Our results show that the multi-class EFC is a promising classiﬁer to be used in NIDS.
Self-Supervised Learning for Semi-Supervised Temporal Language Grounding

Given a text description, Temporal Language Grounding (TLG) aims to localize temporal boundaries of the segments that contain the specified semantics in an untrimmed video. TLG is inherently a challenging task, as it requires comprehensive understanding of both sentence semantics and video contents. Previous works either tackle this task in a fully-supervised setting that requires a large amount of temporal annotations or in a weakly-supervised setting that usually cannot achieve satisfactory performance. Since manual annotations are expensive, to cope with limited annotations, we tackle TLG in a semi-supervised way by incorporating self-supervised learning, and propose <bold>S</bold>elf-<bold>S</bold>upervised <bold>S</bold>emi-<bold>S</bold>upervised <bold>T</bold>emporal <bold>L</bold>anguage <bold>G</bold>rounding (S<inline-formula><tex-math notation="LaTeX">$^{4}$</tex-math></inline-formula>TLG). S<inline-formula><tex-math notation="LaTeX">$^{4}$</tex-math></inline-formula>TLG consists of two parts: (1) A pseudo label generation module that adaptively produces instant pseudo labels for unlabeled samples based on predictions from a teacher model; (2) A self-supervised feature learning module with inter-modal and intra-modal contrastive losses to learn video feature representations under the constraints of video content consistency and video-text alignment. We conduct extensive experiments on the ActivityNet-CD-OOD and Charades-CD-OOD datasets. The results demonstrate that our proposed S<inline-formula><tex-math notation="LaTeX">$^{4}$</tex-math></inline-formula>TLG can achieve competitive performance compared to fully-supervised state-of-the-art methods while only requiring a small portion of temporal annotations.
Alzheimer’s Dementia Detection Using Acoustic & Linguistic Features and Pre-trained BERT

Alzheimer’s disease is a fatal progressive brain disorder that worsens with time. It is high time we have inexpensive and quick clinical diagnostic techniques for early detection and care. In previous studies, various Machine Learning techniques and Pre-trained Deep Learning models have been used in conjunction with the extraction of various acoustic and linguistic features. Our study focuses on three models for the classification task in the ADReSS (The Alzheimer’s Dementia Recognition through Spontaneous Speech) 2021 Challenge. We use the well-balanced dataset provided by the ADReSS Challenge for training and validating our models. Model 1 uses various acoustic features from the eGeMAPs feature-set, Model 2 uses various linguistic features that we generated from auto-generated transcripts and Model 3 uses the auto-generated transcripts directly to extract features using a Pre-trained BERT and TF-IDF. These models are described in detail in the models section.
Fault Localization in Cloud using Centrality Measures

Fault localization is an imperative method in fault tolerance in a distributed environment that designs a blueprint for continuing the ongoing process even when one or many modules are non-functional. Visualizing a distributed environment as a graph, whose nodes represent faults (fault graph), allows us to introduce probabilistic weights to both edges and nodes that cause the faults. With multiple modules like databases, run-time cloud, etc. making up a distributed environment and extensively, a cloud environment, we aim to address the problem of optimally and accurately performing fault localization in a distributed environment by modifying the Graph optimization approach to localization and centrality, specific to fault graphs.
Exploiting Curriculum Learning in Unsupervised Neural Machine Translation

Back-translation (BT) has become one of the de facto components in unsupervised neural machine translation (UNMT), and it explicitly makes UNMT have translation ability. However, all the pseudo bi-texts generated by BT are treated equally as clean data during optimization without considering the quality diversity, leading to slow convergence and limited translation performance. To address this problem, we propose a curriculum learning method to gradually utilize pseudo bi-texts based on their quality from multiple granularities. Specifically, we first apply cross-lingual word embedding to calculate the potential translation difficulty (quality) for the monolingual sentences. Then, the sentences are fed into UNMT from easy to hard batch by batch. Furthermore, considering the quality of sentences/tokens in a particular batch are also diverse, we further adopt the model itself to calculate the fine-grained quality scores, which are served as learning factors to balance the contributions of different parts when computing loss and encourage the UNMT model to focus on pseudo data with higher quality. Experimental results on WMT 14 En-Fr, WMT 16 En-De, WMT 16 En-Ro, and LDC En-Zh translation tasks demonstrate that the proposed method achieves consistent improvements with faster convergence speed.
Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learning

To defend against fake news, researchers have developed various methods based on texts. These methods can be grouped as 1) pattern-based methods, which focus on shared patterns among fake news posts rather than the claim itself; and 2) fact-based methods, which retrieve from external sources to verify the claim's veracity without considering patterns. The two groups of methods, which have different preferences of textual clues, actually play complementary roles in detecting fake news. However, few works consider their integration. In this paper, we study the problem of integrating pattern- and fact-based models into one framework via modeling their preference differences, i.e., making the pattern- and fact-based models focus on respective preferred parts in a post and mitigate interference from non-preferred parts as possible. To this end, we build a Preference-aware Fake News Detection Framework (Pref-FEND), which learns the respective preferences of pattern- and fact-based models for joint detection. We first design a heterogeneous dynamic graph convolutional network to generate the respective preference maps, and then use these maps to guide the joint learning of pattern- and fact-based models for final prediction. Experiments on two real-world datasets show that Pref-FEND effectively captures model preferences and improves the performance of models based on patterns, facts, or both.
Integration of Backscatter Communication with Multi-cell NOMA: A Spectral Efficiency Optimization under Imperfect SIC

Future wireless networks are expected to connect large-scale low-powered communication devices using the available spectrum resources. Backscatter communications (BC) is an emerging technology towards battery-free transmission in future wireless networks by leveraging ambient radio frequency (RF) waves that enable communications among wireless devices. Non-orthogonal multiple access (NOMA) has recently drawn significant attention due to its high spectral efficiency. The combination of these two technologies can play an important role in the development of future networks. This paper proposes a new optimization approach to enhance the spectral efficiency of nonorthogonal multiple access (NOMA)-BC network. Our framework simultaneously optimizes the power allocation of base station and reflection coefficient (RC) of the backscatter device in each cell under the assumption of imperfect signal decoding. The problem of spectral efficiency maximization is coupled on power and RC which is challenging to solve. To make this problem tractable, we first decouple it into two subproblems and then apply the decomposition method and Karush-Kuhn-Tucker conditions to obtain the efficient solution. Numerical results show the performance of the proposed NOMA-BC network over the pure NOMA network without BC.
End-to-End AI-based MRI Reconstruction and Lesion Detection Pipeline for Evaluation of Deep Learning Image Reconstruction

Deep learning techniques have emerged as a promising approach to highly accelerated MRI. However, recent reconstruction challenges have shown several drawbacks in current deep learning approaches, including the loss of fine image details even using models that perform well in terms of global quality metrics. In this study, we propose an end-to-end deep learning framework for image reconstruction and pathology detection, which enables a clinically aware evaluation of deep learning reconstruction quality. The solution is demonstrated for a use case in detecting meniscal tears on knee MRI studies, ultimately finding a loss of fine image details with common reconstruction methods expressed as a reduced ability to detect important pathology like meniscal tears. Despite the common practice of quantitative reconstruction methodology evaluation with metrics such as SSIM, impaired pathology detection as an automated pathology-based reconstruction evaluation approach suggests existing quantitative methods do not capture clinically important reconstruction outcomes.
Energetic Particle Acceleration in Compressible Magnetohydrodynamic Turbulence

Magnetohydrodynamic (MHD) turbulence is an important agent of energetic particle acceleration. Focusing on the compressible properties of magnetic turbulence, we adopt the test particle method to study the particle acceleration from Alfvén, slow, and fast modes in four turbulence regimes that may appear in a realistic astrophysical environment. Our studies show that (1) the second-order Fermi mechanism drives the acceleration of particles in the cascade processes of three modes by particle-turbulence interactions, regardless of whether the shock wave appears; (2) not only can the power spectra of maximum-acceleration rates reveal the inertial range of compressible turbulence, but also recover the scaling and energy ratio relationship between the modes; (3) fast mode dominates the acceleration of particles, especially in the case of super-Alfvénic and supersonic turbulence, slow mode dominates the acceleration for sub-Alfvénic turbulence in the very-high-energy range, and the acceleration of Alfvén mode is significant at the early stage of the acceleration; (4) particle acceleration from three modes results in a power-law distribution in the certain range of evolution time. From the perspective of particle-wave mode interaction, this paper promotes the understanding for both the properties of turbulence and the behavior of particle acceleration, which will help provide insight into astrophysical processes involved in MHD turbulence.
Unbiased Loss Functions for Multilabel Classification with Missing Labels

This paper considers binary and multilabel classification problems in a setting where labels are missing independently and with a known rate. Missing labels are a ubiquitous phenomenon in extreme multi-label classification (XMC) tasks, such as matching Wikipedia articles to a small subset out of the hundreds of thousands of possible tags, where no human annotator can possibly check the validity of all the negative samples. For this reason, propensity-scored precision -- an unbiased estimate for precision-at-k under a known noise model -- has become one of the standard metrics in XMC. Few methods take this problem into account already during the training phase, and all are limited to loss functions that can be decomposed into a sum of contributions from each individual label. A typical approach to training is to reduce the multilabel problem into a series of binary or multiclass problems, and it has been shown that if the surrogate task should be consistent for optimizing recall, the resulting loss function is not decomposable over labels. Therefore, this paper derives the unique unbiased estimators for the different multilabel reductions, including the non-decomposable ones. These estimators suffer from increased variance and may lead to ill-posed optimization problems, which we address by switching to convex upper-bounds. The theoretical considerations are further supplemented by an experimental study showing that the switch to unbiased estimators significantly alters the bias-variance trade-off and may thus require stronger regularization, which in some cases can negate the benefits of unbiased estimation.
Marginals Versus Copulas: Which Account For More Model Risk In Multivariate Risk Forecasting?

Copulas. We study the model risk of multivariate risk models in a comprehensive empirical study on Copula-GARCH models used for forecasting Value-at-Risk and Expected Shortfall. To determine whether model risk inherent in the forecasting of portfolio risk is caused by the candidate marginal or copula models, we analyze different groups of models in which we fix either the marginals, the copula, or neither. Model risk is economically significant, is especially high during periods of crisis, and is almost completely due to the choice of the copula. We then propose the use of the model confidence set procedure to narrow down the set of available models and reduce model risk for Copula-GARCH risk models. Our proposed approach leads to a significant improvement in the mean absolute deviation of one day ahead forecasts by our various candidate risk models.
FooBaR: Fault Fooling Backdoor Attack on Neural Network Training

Neural network implementations are known to be vulnerable to physical attack vectors such as fault injection attacks. As of now, these attacks were only utilized during the inference phase with the intention to cause a misclassification. In this work, we explore a novel attack paradigm by injecting faults during the training phase of a neural network in a way that the resulting network can be attacked during deployment without the necessity of further faulting. In particular, we discuss attacks against ReLU activation functions that make it possible to generate a family of malicious inputs, which are called fooling inputs, to be used at inference time to induce controlled misclassifications. Such malicious inputs are obtained by mathematically solving a system of linear equations that would cause a particular behaviour on the attacked activation functions, similar to the one induced in training through faulting. We call such attacks fooling backdoor s as the fault attacks at training phase inject backdoors into the network that allow an attacker to produce fooling inputs. We evaluate our approach against multi-layer perceptron networks and convolutional networks on a popular image classification task obtaining high attack success rates (from 60% to 100%) and high classification confidence when as little as 25 neurons are attacked, while preserving high accuracy on the originally intended classification task.
Automated Feature-Topic Pairing: Aligning Semantic and Embedding Spaces in Spatial Representation Learning

Automated characterization of spatial data is a kind of critical geographical intelligence. As an emerging technique for characterization, Spatial Representation Learning (SRL) uses deep neural networks (DNNs) to learn non-linear embedded features of spatial data for characterization. However, SRL extracts features by internal layers of DNNs, and thus suffers from lacking semantic labels. Texts of spatial entities, on the other hand, provide semantic understanding of latent feature labels, but is insensible to deep SRL models. How can we teach a SRL model to discover appropriate topic labels in texts and pair learned features with the labels? This paper formulates a new problem: feature-topic pairing, and proposes a novel Particle Swarm Optimization (PSO) based deep learning framework. Specifically, we formulate the feature-topic pairing problem into an automated alignment task between 1) a latent embedding feature space and 2) a textual semantic topic space. We decompose the alignment of the two spaces into: 1) point-wise alignment, denoting the correlation between a topic distribution and an embedding vector; 2) pair-wise alignment, denoting the consistency between a feature-feature similarity matrix and a topic-topic similarity matrix. We design a PSO based solver to simultaneously select an optimal set of topics and learn corresponding features based on the selected topics. We develop a closed loop algorithm to iterate between 1) minimizing losses of representation reconstruction and feature-topic alignment and 2) searching the best topics. Finally, we present extensive experiments to demonstrate the enhanced performance of our method.