Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical Readout of the Néel Vector in the Metallic Antiferromagnet 
Mn2Au

Vladimir Grigorev, 2 Mariia Filianina, 2 Stanislav Yu. Bodnar, 3 Sergei Sobolev, Nilabha Bhattacharjee, Satya Bommanaboyena, Yaryna Lytvynenko, Yurii Skourski, Dirk Fuchs, Mathias Kläui, 2 Martin Jourdan, and Jure Demsar 2 Institute of Physics, Johannes Gutenberg University, 55128 Mainz, Germany Graduate School of Excellence Materials Science in Mainz, 55128 Mainz, Germany Walter Schottky Institut and Physics Department, Technische Universität München, 85748 Garching, Germany Dresden High Magnetic Field Laboratory (HLD-EMFL), Helmholtz-Zentrum Dresden-Rossendorf, 01328 Dresden, Germany Institute for Quantum Materials and Technologies (IQMT), Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany (Dated: June 25, 2021)
A Targeted Assessment of Incremental Processing in Neural Language Models and Humans

We present a targeted, scaled-up comparison of incremental processing in humans and neural language models by collecting by-word reaction time data for sixteen different syntactic test suites across a range of structural phenomena. Human reaction time data comes from a novel online experimental paradigm called the Interpolated Maze task. We compare human reaction times to by-word probabilities for four contemporary language models, with different architectures and trained on a range of data set sizes. We find that across many phenomena, both humans and language models show increased processing difficulty in ungrammatical sentence regions with human and model ‘accuracy’ scores a la Marvin and Linzen (2018) about equal. However, although language model outputs match humans in direction, we show that models systematically under-predict the difference in magnitude of incremental processing difficulty between grammatical and ungrammatical sentences. Specifically, when models encounter syntactic violations they fail to accurately predict the longer reading times observed in the human data. These results call into question whether contemporary language models are approaching human-like performance for sensitivity to syntactic violations.
Redefining measures of Layered Architecture

Layered architecture represents the software structure in the form of layers. Every element in the software is assigned to one of the layers such that the relationship amongst the elements is maintained. A set of design principles rules the process of construction of the layered architecture. Various statistical measures have been defined to check whether the layered architecture of a given software is following these design principles or not. In this paper, we redefine the measures of layered architecture based on the relationship between the software components. The measures check for the violations committed regarding the back calls, skip calls, and cyclic structures. Further, we also introduce a new measure to verify the logical separation amongst the layers. The system's current architecture is extracted from the source code and represented using a three-tier layered structure, which is the defacto standard architecture of Java applications. The redefined measures are applied to determine the conformance of layering principles in the system. We evaluate five different software systems for their architecture consistency. The results obtained on our redefined measures are compared to those obtained by applying the standard set of measures. A quantitative analysis of the proposed measures is performed, and we conclude that they can determine the consideration of layering principles followed during the development of a software system.
3-D Stochastic Numerical Breast Phantoms for Enabling Virtual Imaging Trials of Ultrasound Computed Tomography

Ultrasound computed tomography (USCT) is an emerging imaging modality for breast imaging that can produce quantitative images that depict the acoustic properties of tissues. Computer-simulation studies, also known as virtual imaging trials, provide researchers with an economical and convenient route to systematically explore imaging system designs and image reconstruction methods. When simulating an imaging technology intended for clinical use, it is essential to employ realistic numerical phantoms that can facilitate the objective, or task-based, assessment of image quality (IQ). Moreover, when computing objective IQ measures, an ensemble of such phantoms should be employed, which displays the variability in anatomy and object properties that are representative of the to-be-imaged patient cohort. Such stochastic phantoms for clinically relevant applications of USCT are currently lacking. In this work, a methodology for producing realistic 3-D numerical breast phantoms for enabling clinically relevant computer-simulation studies of USCT breast imaging is presented. By extending and adapting an existing stochastic 3-D breast phantom for use with USCT, methods for creating ensembles of numerical acoustic breast phantoms are established. These breast phantoms will possess clinically relevant variations in breast size, composition, acoustic properties, tumor locations, and tissue textures. To demonstrate the use of the phantoms in virtual USCT studies, two brief case studies are presented, which addresses the development and assessment of image reconstruction procedures. Examples of breast phantoms produced by use of the proposed methods and a collection of 52 sets of simulated USCT measurement data have been made open source for use in image reconstruction development.
Higher Theories and Monads

We extend Bourke and Garner’s idempotent adjunction between monads and pretheories to the framework of ∞-categories and we use this to prove many classical results about monads in the∞-categorical framework. Amongst other things, we show that the category of algebras for an accessible monads on a locally presentable ∞-category E is again locally presentable, and that a diagram of accessible monads on a locally presentable∞-category admits a colimit. Our results also provide a new and simpler way to construct and describe monads in terms of theories.
The Limits of Local Search for the Maximum Weight Independent Set Problem in d-Claw Free Graphs

We consider the Maximum Weight Independent Set Problem (MWIS) in $d$-claw free graphs, i.e. the task of computing an independent set of maximum weight in a given $d$-claw free graph $G=(V,E)$ equipped with a positive weight function $w:V\rightarrow\mathbb{R}_{>0}$. For $k\geq 1$, the MWIS in $k+1$-claw free graphs generalizes the weighted $k$-Set Packing Problem. Given that for $k\geq 3$, this problem does not permit a polynomial time $o(\frac{k}{\log k})$-approximation unless $P=NP$, most previous algorithms for both weighted $k$-Set Packing and the MWIS in $d$-claw free graphs rely on local search. For the last twenty years, Berman's algorithm SquareImp, which yields a $\frac{d}{2}+\epsilon$-approximation for the MWIS in $d$-claw free graphs, has remained unchallenged for both problems. Recently, it was improved by Neuwohner, obtaining an approximation guarantee slightly below $\frac{d}{2}$, and inevitably raising the question of how far one can get by using local search. In this paper, we finally answer this question asymptotically in the following sense: By considering local improvements of logarithmic size, we obtain approximation ratios of $\frac{d-1+\epsilon_d}{2}$ for the MWIS in $d$-claw free graphs for $d\geq 3$ in quasi-polynomial time, where $0\leq \epsilon_d\leq 1$ and $\lim_{d\rightarrow\infty}\epsilon_d = 0$. By employing the color coding technique, we can use the previous result to obtain a polynomial time $\frac{k+\epsilon_{k+1}}{2}$-approximation for weighted $k$-Set Packing. On the other hand, we provide examples showing that no local improvement algorithm considering local improvements of size $\mathcal{O}(\log(|\mathcal{S}|))$ with respect to some power $w^\alpha$ of the weight function, where $\alpha\in\mathbb{R}$ is chosen arbitrarily, but fixed, can yield an approximation guarantee better than $\frac{k}{2}$ for the weighted $k$-Set Packing Problem with $k\geq 3$.
A call for better unit testing for invariant risk minimisation

In this paper we present a controlled study on the linearized IRM framework (IRMv1) introduced in Arjovsky et al. (2020). We show that IRMv1 (and its variants) framework can be potentially unstable under small changes to the optimal regressor. This can, notably, lead to worse generalisation to new environments, even compared with ERM which converges simply to the global minimum for all training environments mixed up all together. We also highlight the isseus of scaling in the the IRMv1 setup. These observations highlight the importance of rigorous evaluation and importance of unit-testing for measuring progress towards IRM.
Strength of Minibatch Noise in SGD

The noise in stochastic gradient descent (SGD), caused by minibatch sampling, is poorly understood despite its practical importance in deep learning. This work presents the first systematic study of the SGD noise and fluctuations close to a local minimum. We first analyze the SGD noise in linear regression in detail and then derive a general formula for approximating SGD noise in different types of minima. For application, our results (1) provide insight into the stability of training a neural network, (2) suggest that a large learning rate can help generalization by introducing an implicit regularization, (3) explain why the linear learning rate-batchsize scaling law fails at a large learning rate or at a small batchsize and (4) can provide an understanding of how discrete-time nature of SGD affects the recently discovered power-law phenomenon of SGD.
Relative stability toward diffeomorphisms indicates performance in deep nets

Understanding why deep nets can classify data in large dimensions remains a challenge. It has been proposed that they do so by becoming stable to diffeomorphisms, yet existing empirical measurements support that it is often not the case. We revisit this question by defining a maximum-entropy distribution on diffeomorphisms, that allows to study typical diffeomorphisms of a given norm. We confirm that stability toward diffeomorphisms does not strongly correlate to performance on benchmark data sets of images. By contrast, we find that the stability toward diffeomorphisms relative to that of generic transformations R f correlates remarkably with the test error ϵ t. It is of order unity at initialization but decreases by several decades during training for state-of-the-art architectures. For CIFAR10 and 15 known architectures we find ϵt≈0.2Rf , suggesting that obtaining a small R f is important to achieve good performance. We study how R f depends on the size of the training set and compare it to a simple model of invariant learning.
TinyTL: Reduce Activations, Not Trainable Parameters for Efficient On-Device Learning

On-device learning enables edge devices to continually adapt the AI models to new data, which requires a small memory footprint to fit the tight memory constraint of edge devices. Existing work solves this problem by reducing the number of trainable parameters. However, this doesn't directly translate to memory saving since the major bottleneck is the activations, not parameters. In this work, we present Tiny-Transfer-Learning (TinyTL) for memory-efficient on-device learning. TinyTL freezes the weights while only learns the bias modules, thus no need to store the intermediate activations. To maintain the adaptation capacity, we introduce a new memory-efficient bias module, the lite residual module, to refine the feature extractor by learning small residual feature maps adding only 3.8% memory overhead. Extensive experiments show that TinyTL significantly saves the memory (up to 6.5x) with little accuracy loss compared to fine-tuning the full network. Compared to fine-tuning the last layer, TinyTL provides significant accuracy improvements (up to 34.1%) with little memory overhead. Furthermore, combined with feature extractor adaptation, TinyTL provides 7.3-12.9x memory saving without sacrificing accuracy compared to fine-tuning the full Inception-V3.
Unit Ball Model for Embedding Hierarchical Structures in the Complex Hyperbolic Space

Learning the representation of data with hierarchical structures in the hyperbolic space attracts increasing attention in recent years. Due to the constant negative curvature, the hyperbolic space resembles tree metrics and captures the tree-like properties naturally, which enables the hyperbolic embeddings to improve over traditional Euclidean models. However, many real-world hierarchically structured data such as taxonomies and multitree networks have varying local structures and they are not trees, thus they do not ubiquitously match the constant curvature property of the hyperbolic space. To address this limitation of hyperbolic embeddings, we explore the complex hyperbolic space, which has the variable negative curvature, for representation learning. Specifically, we propose to learn the embeddings of hierarchically structured data in the unit ball model of the complex hyperbolic space. The unit ball model based embeddings have a more powerful representation capacity to capture a variety of hierarchical structures. Through experiments on synthetic and real-world data, we show that our approach improves over the hyperbolic embedding models significantly. We also explore the competence of complex hyperbolic geometry on the multitree structure and $1$-$N$ structure.
Rapid Scoping Review on the Topic of Ensuring Social Protection and Basic Services to Inform the United Nations Framework for the Immediate Socioeconomic Response to COVID-19

This rapid scoping review has informed the development of the UN Research Roadmap for the COVID-19 Recovery on the topic of “Ensuring Social Protection and Basic Services. ” The aim was to provide a robust synthesis of key concepts and existing evidence drawn from a wide range of disciplines to support the identification and appraisal of research priorities. An emergent theme has been the notion that measures implemented in response to COVID-19 merely ameliorate symptoms of entrenched, systemic gender-, age-, and race-based inequity, inequality, and exclusion. Key findings include the critical role of contextual and community-based knowledge for informing the design, development, and delivery of programs, as well as the urgent need for implementation science to move existing knowledge into action. This review also describes how the disruption associated with “shock events” such as the COVID-19 pandemic is often associated with unusually high levels of interest and willingness to invest in programs and policies to strengthen strained systems. As such, an unprecedented window of opportunity exists to leverage measures implemented in response to the COVID-19 pandemic to effect large-scale, sustainable change and thereby increase the resiliency of our interconnected systems for the future.
Evaluation of molecular markers GSTM1 and GSTT1 and clinical factors in breast cancer: case-control study and literature review

Abstract The study was conducted to evaluate the frequency of polymorphisms in GSTM1 and GSTT1 genes in patients with breast cancer compared with individuals without history of cancer, and the association of these polymorphisms with clinical/epidemiological parameters. There were evaluated 752 women (219 patients and 533 controls). Molecular analysis was performed by the Polymerase Chain Reaction (PCR). Statistical analysis was used multiple logistic regression and descriptive statistics. Age ≥ 50 years (OR = 3.22, 95% CI = 2.30–4.51, p < 0.001) and alcohol consumption (OR = 1.60, 95% CI = 1.13–2.27, p = 0.008) were associated to the development of breast cancer, while smoking and null genotypes GSTM1 and GSTT1 presented no association. GSTM1 and GSTT1 polymorphisms presented no relationship with the clinical and histopathological parameters or molecular subtypes of breast cancer. Ninety-two percent of tumours were invasive ductal, 66% were grade II, 65% were larger than 2 cm, the stages II (35.3%) and III (31.2%) were the most prevalent, and 47.7% were molecular subtype luminal B. Individuals aged ≥ 50 years and alcohol consumers have more chance to developing breast cancer. GSTM1 and GSTT1 polymorphisms are not associated to the risk of breast cancer.
Reconceptualizing the permanence of alexandrite laser hair removal results: a long-term follow-up study

ABSTRACT Background: The permanence of Alexandrite laser hair removal (LHR) has been of great interest. However, studies on long-term outcome are lacking. Objective: To evaluate the hair reduction rates (HRRs) and laser parameters of patients who no longer need epilation after LHR, and redefine permanence. Patients & Methods: Out of 183, 100 patients who discontinue epilation at least 2 years after LHR were included in the study. Self-perceived HRRs and patient satisfaction were recorded through patient questionnaires. The correlations between HRRs and treatment parameters, and the presence of polycystic ovary syndrome (PCOS) were assessed. Results: Treatment sites were the face, legs, trunk, arm and bikini regions. Twenty-one patients had PCOS. The means of results (in range) were as follows; HRRs [56.28% (±19.73) and 67.74% (±16.42)]; the session numbers [5.52 (±2.50) and 9.80 (±4.15)] and, the treatment durations [62.2 (±40.0) and 90.0 (±45.9) weeks]. All patients stated they no longer require epilation since the remaining hairs were too thin to be noticed. Conclusion: Our results demonstrate that, even in the presence of PCOS, hair reduction together with thinning satisfy patients without total removal of hairs. Therefore, the elimination of the need for epilation might be considered as the ‘permanent result’ of LHR.
Integrating intratumoral and peritumoral features to predict tumor recurrence in intrahepatic cholangiocarcinoma

Previous studies have suggested that the intratumoral texture features may reflect the tumor recurrence risk in intrahepatic cholangiocarcinoma (ICC). The peritumoral features may be associated with the distribution of microsatellites. Therefore, integrating the imaging features based on intratumoral and peritumoral areas may provide more accurate predictions in tumor recurrence (both early and late recurrences) than the predictions conducted based on the intratumoral area only. This retrospective study included 209 ICC patients. We divided the patient population into two sub-groups according to the order of diagnosis time: a training cohort (159 patients) and an independent validation cohort (50 patients). The MR imaging features were quantified based on the intratumoral and peritumoral (3 and 5 mm) areas. The radiomics signatures, clinical factor-based models and combined radiomics-clinical models were developed to predict the tumor recurrence. The prediction performance was measured based on the validation cohort using the area under receiver operating characteristic curve (AUC) index. For the prediction of early recurrence, the combined radiomics-clinical model of intratumoral area with 5 mm peritumoral area showed the highest performance (0.852(95% confidence interval (CI), 0.724–0.937)). The AUC for the clinical factor-based model was 0.805(95%CI, 0.668–0.903). For the prediction of late recurrence, the radiomics signature of intratumoral area with 5 mm peritumoral area had the optimal performance with an AUC of 0.735(95%CI, 0.591–0.850). The clinical factor-based showed inferior performance (0.598(95%CI, 0.450–0.735)). For both early and late recurrences prediction, the optimal models were all constructed using imaging features extracted based on intratumoral and peritumoral areas together. These suggested the importance of involving the intratumoral and peritumoral areas in the radiomics studies.
Medication Use in the Management of Comorbidities Among Individuals With Autism Spectrum Disorder From a Large Nationwide Insurance Database

Key Points Question How have medications been used recently in the management of symptoms and comorbidities of autism spectrum disorder (ASD)? Findings This population-based cohort study of 26 722 individuals with ASD in the US retrospectively assessed the most common medications used to treat their symptoms and comorbidities during 6 years. Medication type and frequency varied considerably, depending on the comorbidity diagnosed, and individual drug regimens shifted frequently within medication classes over time. Meaning Many individuals with ASD undergo treatment with a wide variety of medications on a trial basis, resulting in frequent changes in drug regimens over time as clinicians attempt to manage associated symptoms and comorbidities.
The Association between Fourier Parameters and Clinical Parameters in Myopic Children Undergoing Orthokeratology

ABSTRACT Purpose: To explore how Fourier parameters are associated with axial length growth (ALG) and clinical parameters in children who underwent orthokeratology. Materials and Methods: A total of 267 children received orthokeratology. Baseline cycloplegic autorefraction was performed. Axial length was measured at baseline and one year after the lens dispatch, and the difference was defined as ALG. Corneal topography was performed at the same two visits. Central treatment zone (CTZ) was identified from the difference between the two tangential maps, and its center distance to corneal center was defined as decentration. A relative refractive corneal power (RCRP) map was derived by subtracting the center value from every point on the one-year axial map. It was decomposed into 3 Fourier components: a mean (F0), a single-cycle sinewave (F1), and a double-cycle sinewave (F2). Linear regressions were used to reveal the association between ALG and these parameters. Results: At baseline, the age was 10.18 ± 1.48 year, spherical equivalent (SE) was – 3.10 ± 1.15D, astigmatism was 1.17 ± 0.58D, and axial length was 24.69 ± 0.81 mm. The mean ALG was 0.181 ± 0.22 mm. In multiple regression, ALG was negatively associated with F1 (p < .001), not F0 and F2. Amplitude-wise, F0 and F1 were correlated with decentration (p < .01) and SE (p < .01), and F2 was associated with astigmatism (p < .001). Direction-wise, F1 was correlated with decentration (p < .001) and F2 was associated with astigmatism (p < .001). Conclusions: Among Fourier parameters, F0 and F1 were negatively associated with ALG in myopic children undergoing orthokeratology. Their associations to SE and CTZ decentration may partially explain the effect on ALG retardation.