Miracle-dz/UniFAR-FTUs huggingface dataset:
Cognitive Dissonance or Agency Theory, what Explains the Loss of Impairment of Goodwill?

Objective: The purpose of this research is to analyze the impairment loss of goodwill in companies listed in Brasil, Bolsa, Balcao [B]3 from the perspective of Agency Theory and Cognitive Dissonance Theory. Method: We used a sample composed of Brazilian non-financial publicly traded companies listed in [B]3. The analysis was conducted through the logistic regression, considering an unbalanced panel data set for the period from 2010 to 2016. Originality/Relevance: This study uses a theory of psychology, the Cognitive Dissonance Theory, to try to explain the occurrence of goodwill impairment loss. Results: The results point to the Cognitive Dissonance Theory as the theory that explains the goodwill impairment loss, since managers tend to record a goodwill impairment loss in the current year, when there was already a loss recorded in the previous year, as a way to decrease the mental discomfort caused between their cognitions. Theoretical/Methodological contributions: It contributes to the literature in order to associate the decisions made by managers with a psychological behavioral theory that until now has not received prominence in the attempts to justify the decisions of agents. The gain from this research is also directed to investors, as it allows them to make decisions not only based on what the accounting reports say, but taking into account other aspects, such as managers’ behavior and cognitions.
New Non-Secant Implicit Update Method for Unconstrained Optimization

— Multi-step methods are secant-like techniques of the quasi-Newton type that, unlike the classical methods, construct nonlinear alternatives to the quantities 𝒔 𝒊 = 𝒙 𝒊+𝟏 − 𝒙 𝒊 and 𝒚 𝒊 = 𝒈(𝒙 𝒊+𝟏 ) − 𝒈(𝒙 𝒊 ) in the so-called Secant equation 𝑩 𝒊+𝟏 𝒔 𝒊 = 𝒚 𝒊 , where 𝒙 𝒊 denotes the current estimate for the minimum of the function being minimized ( 𝒇(𝒙) ) and where 𝒈(𝒙) denote the gradient of f and the n x n matrix 𝑩 𝒊+𝟏 is the new approximation to the Hessian of f. Multi-step methods instead utilize data available from the m most recent iterations and thus create an alternative to the Secant equation with the intention of creating better Hessian approximation that induce faster convergence to the minimizer of f. Such methods now satisfy a relationship of the like 𝑩 𝒊+𝟏 𝒓 𝒊 = 𝒘 𝒊, for 𝒓 𝒊 = 𝒔 𝒊 − 𝝁 𝒊 𝒔 𝒊−𝟏 and 𝒘 𝒊 = 𝒚 𝒊 − 𝝁 𝒊 𝒚 𝒊−𝟏 , for some carefully chosen 𝝁 𝒊 . The methods , based on reported numerical results published in several research papers related to the subject, have introduced substantial savings in both iteration and function evaluation counts. Implicit updates were developed within the context of multi-step methods and have been implemented as a means to improve the quality of the interpolation curves in such methods. The idea that prior to updating the Hessian approximation at the current iteration, an extra update is carried out implicitly and in a cheap manner to use that update in the construction of the nonlinear polynomials that interpolate the recent m available step and gradient vectors used in the upcoming approximated Hessian computation so that the numerical performance of the multi-step methods improves further. In this paper, an improved implicit technique is introduced that is based on a variant of the Secant equation that is intended to include further already computed data (such as function values), that would, otherwise, be discarded and left unexploited, in the construction of a, hopefully, better Hessian approximation that yields, eventually, faster convergence. The implicit methods used here satisfy a relationship of the type 𝑩 𝒊+𝟏 𝒓 𝒊 = 𝒓 𝒊 + 𝜷 𝒊 𝒘 𝒊 . The numerical experimentations on the new methods are promising and open venue for further investigation of such
Zinc isotope analyses of singularly small samples (

The potential complementarity between chondrules and matrix of chondrites, the Solar System’s building blocks, is still a highly debated subject. Complementary superchrondritic compositions of chondrite matrices and subchondritic chondrules may point to formation of these components within the same reservoir or, alternatively, to mobilization of elements during secondary alteration on chondrite parent bodies. Zinc isotope fractionation through evaporation during chondrule formation may play an important role in identifying complementary relationships between chondrules and matrix and is additionally a mobile element during hydrothermal processes. In an effort to distinguish between primary Zn isotope fractionation during chondrule formation and secondary alteration, we here report the Zn isotope data of five chondrule cores, five corresponding igneous rims and two matrices of the relatively unaltered Leoville CV3.1 chondrite. The detail required for these analyses necessitated the development of an adjusted Zn isotope analyses protocol outlined in this study. This method allows for the measurement of 5 ng Zn fractions, for which we have analyzed the isotope composition with an external reproducibility of 120 ppm. We demonstrate that we measure primary Zn isotope signatures within the sampled fractions of Leoville, which show negative δZn values for the chondrule cores (δZn = –0.43±0.14 h), more positive values for the igneous rims (δZn = –0.01±0.30 h) and chonPreprint submitted to Geochimica et Cosmochimica Acta July 16, 2019 ar X iv :1 90 7. 06 50 1v 1 [ as tr oph . E P] 1 5 Ju l 2 01 9 dritic values for the matrix (δZn = 0.19±0.14 h). In combination with elemental compositions and petrology of these chondrite fractions, we argue that chondrule cores, igneous rims and matrix could have formed within the same reservoir in the protoplanetary disk. The required formation mechanism involves Zn isotope fractionation through sulfide loss during chondrule core formation and concurrent thermal processing of matrix material. Depleted olivine-bearing grains representing this processed matrix would have accreted to the depleted chondrule cores and subsequently reabsorbed material (including Zn-rich) from a complementary volatile-rich gas, thereby forming the igneous rims. This would have allowed the rims to move towards an isotopically chondritic composition, similar to the non-processed matrix in Leoville. We note that Zn isotope analyses of components in other chondrites (f.e., CM, CO, EC) are necessary to identify if this complementarity relationship is generic or unique for each chondrite group. The development of a Zn isotope protocol for singularly small samples is a step forward in that direction.
Association of Socioeconomic Changes due to the COVID-19 Pandemic With Health Outcomes in Patients With Skin Diseases: Cross-Sectional Survey Study

Background The outbreak of COVID-19 has profoundly influenced people’s lifestyles; these impacts have varied across subgroups of people. The pandemic-related impacts on the health outcomes of people with dermatological conditions are unknown. Objective The aim of this paper was to study the association of COVID-19 pandemic–related impacts with health-related quality of life in patients with skin diseases. Methods This was a cross-sectional study among Chinese patients with skin diseases. A self-administered web-based questionnaire was distributed through social media. Demographic and clinical data and pandemic-related impacts (isolation status, income changes, and employment status) were collected. The main outcomes included perceived stress (Visual Analog Scale), symptoms of anxiety (Generalized Anxiety Disorder-7) and depression (9-Item Patient Health Questionnaire), quality of life (Dermatology Life Quality Index), and health utility mapping based on the EQ-5D-3L descriptive system. Multivariable logistic regression was used to investigate the associations. Results A total of 506 patients with skin diseases completed the survey. The mean age of the patients was 33.5 years (SD 14.0), and 217/506 patients (42.9%) were male. Among the 506 respondents, 128 (25.3%) were quarantined, 102 (20.2%) reported unemployment, and 317 (62.6%) reported decrease or loss of income since the pandemic. The pandemic-related impacts were significantly associated with impaired mental well-being and quality of life with different effects. Unemployment and complete loss of income were associated with the highest risks of adverse outcomes, with increases of 110% to 162% in the prevalence of anxiety, depression, and impaired quality of life. Conclusions Isolation, income loss, and unemployment are associated with impaired health-related quality of life in patients with skin diseases during the COVID-19 pandemic.
Light Can Hack Your Face! Black-box Backdoor Attack on Face Recognition Systems

Deep neural networks (DNN) have shown great success in many computer vision applications. However, they are also known to be susceptible to backdoor attacks. When conducting backdoor attacks, most of the existing approaches assume that the targeted DNN is always available, and an attacker can always inject a specific pattern to the training data to further fine-tune the DNN model. However, in practice, such attack may not be feasible as the DNN model is encrypted and only available to the secure enclave. In this paper, we propose a novel black-box backdoor attack technique on face recognition systems, which can be conducted without the knowledge of the targeted DNN model. To be specific, we propose a backdoor attack with a novel color stripe pattern trigger, which can be generated by modulating LED in a specialized waveform. We also use an evolutionary computing strategy to optimize the waveform for backdoor attack. Our backdoor attack can be conducted in a very mild condition: 1) the adversary cannot manipulate the input in an unnatural way (e.g., injecting adversarial noise); 2) the adversary cannot access the training database; 3) the adversary has no knowledge of the training model as well as the training set used by the victim party. We show that the backdoor trigger can be quite effective, where the attack success rate can be up to $88\%$ based on our simulation study and up to $40\%$ based on our physical-domain study by considering the task of face recognition and verification based on at most three-time attempts during authentication. Finally, we evaluate several state-of-the-art potential defenses towards backdoor attacks, and find that our attack can still be effective. We highlight that our study revealed a new physical backdoor attack, which calls for the attention of the security issue of the existing face recognition/verification techniques.
Efficient Transformers: A Survey

Transformer model architectures have garnered immense interest lately due to their effectiveness across a range of domains like language, vision, and reinforcement learning. In the field of natural language processing for example, Transformers have become an indispensable staple in the modern deep learning stack. Recently, a dizzying number of “X-former” models have been proposed—Reformer, Linformer, Performer, Longformer, to name a few—which improve upon the original Transformer architecture, many of which make improvements around computational and memory efficiency. With the aim of helping the avid researcher navigate this flurry, this article characterizes a large and thoughtful selection of recent efficiency-flavored “X-former” models, providing an organized and comprehensive overview of existing work and models across multiple domains.
SDSS-IV MaNGA: The Nature of an Off-galaxy Hα Blob—A Multiwavelength View of Offset Cooling in a Merging Galaxy Group

Galaxies in dense environments, such as groups and clusters, experience various processes by which galaxies gain and lose gas. Using data from the SDSS-IV MaNGA survey, we previously reported the discovery of a giant (6—8 kpc in diameter) Hα blob, Totoro, about 8 kpc away from a pair of galaxies (Satsuki and Mei) residing in a galaxy group that is experiencing a group–group merger. Here, we combine interferometric 12CO(1–0) molecular gas data, new wide-field Hα, u-band data, and published X-ray data to determine the origin of the blob. Several scenarios are discussed to account for its multiwavelength properties, including (1) Hα gas being stripped from the galaxy Satsuki by ram pressure, (2) a separated low-surface-brightness galaxy, (3) gas being ejected or ionized by an active galactic nucleus (AGN), and (4) a cooling intragroup medium (IGM). Scenarios (1) and (2) are less favored by the present data. Scenario (3) is also less likely as there is no evidence for an active ongoing AGN in the host galaxy. We find that the CO (cold) and Hα (warm) gas coexist with X-ray (hot) structures; moreover, the derived cooling time is within the regime where molecular and Hα gas are expected. The coexistence of gas with different temperatures also agrees with that of cooling gas in other systems. Our multiwavelength results strongly suggest that the CO and Hα gas are the product of cooling from the IGM at its current location, i.e., cooling has occurred, and may be ongoing, well outside the host-galaxy core.
The Devil is the Classifier: Investigating Long Tail Relation Classification with Decoupling Analysis

Long-tailed relation classification is a challenging problem as the head classes may dominate the training phase, thereby leading to the deterioration of the tail performance. Existing solutions usually address this issue via class-balancing strategies, e.g., data re-sampling and loss re-weighting, but all these methods adhere to the schema of entangling learning of the representation and classifier. In this study, we conduct an in-depth empirical investigation into the long-tailed problem and found that pre-trained models with instance-balanced sampling already capture the well-learned representations for all classes; moreover, it is possible to achieve better long-tailed classification ability at low cost by only adjusting the classifier. Inspired by this observation, we propose a robust classifier with attentive relation routing, which assigns soft weights by automatically aggregating the relations. Extensive experiments on two datasets demonstrate the effectiveness of our proposed approach. Code and datasets are available in this https URL.
Functional inequalities involving nonlocal operators on complete Riemannian manifolds and their applications to the fractional porous medium equation

The objective of this paper is twofold. First, we conduct a careful study of various functional inequalities involving the fractional Laplacian operators, including nonlocal Sobolev-Poincare, Nash, Super Poincare and logarithmic Sobolev type inequalities, on complete Riemannian manifolds satisfying some mild geometric assumptions. Second, based on the derived nonlocal functional inequalities, we analyze the asymptotic behavior of the solution to the fractional porous medium equation, $\partial_t u +(-\Delta)^\sigma (|u|^{m-1}u )=0 $ with $m>0$ and $\sigma\in (0,1)$. In addition, we establish the global well-posedness of the equation on an arbitrary complete Riemannian manifold.
Momentum Kicks in Imperfect Which-Way Measurement

There has been an intense debate on the question as to whether a quanton, passing through a double-slit, experiences a 'momentum kick' due to the act of which-way detection. There have been conflicting points of view on this issue over many decades. This issue is addressed here in the general setting where the which-way detection may be imperfect. It is shown here that the loss of interference may still be interpreted as arising out of tiny momentum kicks which the quanton appears to receive, irrespective of the nature of the which-way detector. Interestingly, the magnitude of the random momentum kicks is always h/2d, d being the slit separation, irrespective of how perfect or imperfect the which-way detection is. This is contrary to what has been suggested in the earlier literature. The imperfection of which-way detection decides how frequent are the momentum kicks. It has been shown earlier that for perfect which-way detection, the quanton receives a momentum kick fifty percent of the time. Here it is shown that for imperfect which-way detection, the quanton receives momentum kicks of the same magnitude, but less often. A precise relation between the frequency of kicks and the visibility of interference is found here.
Deep Reinforcement Learning Based Fast Initial Access For mmWave Based User-centric Systems

User-centric (UC) systems face a complex problem of initial access (IA) beam discovery due to a large number of distributed access points (APs). The use of millimeter-wave (mmWave) frequencies, further exacerbates the IA problem due to its shorter channel coherence time and higher blockage susceptibility compared to the lower frequency bands. In this paper, we provide a novel deep reinforcement learning-based algorithm called RapidIA to efficiently perform IA in mmWave based UC systems. The key advantage of RapidIA is that it can predict the IA beam steering vector using just one reference signal from the user. The policy of the deep reinforcement learning model in RapidIA learns the mapping between received reference signals and the beam codebook. The mmWave channel and the simulation environment are modeled using a ray-tracing approach. Ray-tracing based simulations show that the RapidIA algorithm consistently achieves a beam prediction accuracy level of 100%. With respect to beam discovery delay, RapidIA shows a considerable improvement compared to other beam sweeping based IA systems.
On nonlinear problems of parabolic type with implicit constitutive equations involving flux

We study systems of nonlinear partial differential equations of parabolic type, in which the elliptic operator is replaced by the first-order divergence operator acting on a flux function, which is related to the spatial gradient of the unknown through an additional implicit equation. This setting, broad enough in terms of applications, significantly expands the paradigm of nonlinear parabolic problems. Formulating four conditions concerning the form of the implicit equation, we first show that these conditions describe a maximal monotone [Formula: see text]-coercive graph. We then establish the global-in-time and large-data existence of a (weak) solution and its uniqueness. To this end, we adopt and significantly generalize Minty’s method of monotone mappings. A unified theory, containing several novel tools, is developed in a way to be tractable from the point of view of numerical approximations.
3D_DEN: Open-Ended 3-D Object Recognition Using Dynamically Expandable Networks

Service robots, in general, have to work independently and adapt to the dynamic changes happening in the environment in real time. One important aspect in such scenarios is to continually learn to recognize newer object categories when they become available. This combines two main research problems, namely, continual learning and 3-D object recognition. Most of the existing research approaches include the use of deep convolutional neural networks (CNNs) focusing on image data sets. A modified approach might be needed for continually learning 3-D object categories. A major concern in using CNNs is the problem of catastrophic forgetting when a model tries to learn a new task. Despite various proposed solutions to mitigate this problem, there still exist some downsides of such solutions, e.g., computational complexity, especially when learning a substantial number of tasks. These downsides can pose major problems in robotic scenarios where real-time response plays an essential role. Toward addressing this challenge, we propose a new deep transfer learning approach based on a dynamic architectural method to make robots capable of open-ended learning about new 3-D object categories. Furthermore, we make sure that the mentioned downsides are minimized to a great extent. Experimental results showed that the proposed model outperformed state-of-the-art approaches with regards to accuracy and also substantially minimizes computational overhead. The code is available online at: https://github.com/sudhakaranjain/3D_DEN.
From Pulses to Circuits and Back Again: A Quantum Optimal Control Perspective on Variational Quantum Algorithms

The last decade has witnessed remarkable progress in the development of quantum technologies. Although fault-tolerant devices likely remain years away, the noisy intermediate-scale quantum devices of today may be leveraged for other purposes. Leading candidates are variational quantum algorithms (VQAs), which have been developed for applications including chemistry, optimization, and machine learning, but whose implementations on quantum devices have yet to demonstrate improvements over classical capabilities. In this Perspective, we propose a variety of ways that progress toward this potential crossover point could be informed by quantum optimal control theory. To set the stage, we identify VQAs and quantum optimal control as formulations of variational optimization at the circuit level and pulse level, respectively, where these represent just two levels in a broader hierarchy of abstractions that we consider. In this unified picture, we suggest several ways that the different levels of abstraction may be connected, in order to facilitate the application of quantum optimal control theory to VQA challenges associated with ansatz selection, optimization landscapes, noise, and robustness. A major theme throughout is the need for sufficient control resources in VQA implementations; we discuss different ways this need can manifest, outline a variety of open questions, and conclude with a look to the future.
The Biology of Canadian Weeds: 155. Panicum miliaceum L.

Abstract: Proso millet, Panicum miliaceum L. (Poaceae), has been a crop in Asia and Europe for 10 000 years and for over 300 years in Canada, where it is grown for grain, rescue crops, birdseed, forage, and recently for swath grazing. Crop-like biotypes have escaped from cultivation throughout Canada, but these infestations were generally temporary. Probably the first persistent, weedy biotype in Canada (Quebec) has large, dormant, dark red seeds. Its eradication has been attempted for many years. In the 1970s, a worse problem, a black-seeded biotype with many weedy attributes, including shattering as seeds ripen and strongly dormant seeds distasteful to many birds, spread across southern Ontario and became labelled as noxious. Concurrently, it was described as the worst weed in the US Corn Belt. In the 1980s, Canadian studies revealed infestations of other weedy biotypes, differing in seed colour and other attributes. Panicum miliaceum is an extremely variable species world-wide. Since the late 1990s, weedy proso millet has declined dramatically after the adoption of newer herbicides, particularly glyphosate. This development may lead to increases in cultivated proso millet, especially where climate change favours short-season crops grown under drier conditions.