Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Optimal Testing Results from Global Hypercontractivity

The problem of testing low-degree polynomials has received significant attention over the years due to its importance in theoretical computer science, and in particular in complexity theory. The problem is specified by three parameters: field size q, degree d and proximity parameter δ, and the goal is to design a tester making as few as possible queries to a given function, which is able to distinguish between the case the given function has degree at most d, and the case the given function is δ-far from any degree d function. With respect to these parameters, we say that a tester is optimal if it makes $O(q^{t}+1/\delta)$ queries, where $t=t(d,q)$ is the testing dimension of d, q (defined as the minimum integer so that for all $g:\mathbb{F}_{q}^{n}\rightarrow\mathbb{F}_{q}$ of degree more than d, there is a subspace of dimension t on which their restriction has degree exceeding d). For the field of size q, such tester was first given by Bhattacharyya et al. for q = 2, and later by Haramaty et al. [7] for all prime powers q. In fact, they showed that the natural t-flat tester is an optimal tester for the Reed-Muller code, for an appropriate t. Here, the t-flat tester is the tester that picks a uniformly random affine subspace A of dimension t, and checks that $\operatorname{deg}(f|_{A})\leqslant d$. Their analysis proves that the dependency of the t-flat tester on δ and d is optimal, however the dependency on the field size, i.e. the hidden constant in the O, is a tower-type function in q. We improve the result of Haramaty et al., showing that the dependency on the field size is polynomial. Our technique also applies in the more general setting of lifted affine invariant codes, and gives the same polynomial dependency on the field size. This answers a problem raised in [6]. Our approach significantly deviates from the strategy taken in earlier works [2], [7], [6], and is based on studying the structure of the collection of erroneous subspaces, i.e. subspaces A such that f|A has degree greater than d. Towards this end, we observe that these sets are poorly expanding in the affine version of the Grassmann graph and use that to establish structural results on them via global hypercontractivity. We then use this structure to perform local correction on f.
Sim-to-Real for Soft Robots using Differentiable FEM: Recipes for Meshing, Damping, and Actuation

An accurate, physically-based model of soft robots can unlock downstream applications in optimal control. The Finite Element Method (FEM) is an expressive approach for modeling highly deformable structures such as dynamic, elastomeric soft robots. Recently, Projective Dynamics (PD) has been proposed as a fast FEM; however, PD lacks rigorous benchmarking against reality. In this paper, we compare virtual robot models simulated using PD with measurements from their physical counterparts. In particular, we examine several soft structures with different morphologies: a clamped beam under external force, a pneumatically actuated soft robotic arm, and a soft robotic fish tail. We benchmark and analyze different meshing resolutions and elements (tetrahedra and hexahedra), numerical damping, and the differentiability of PD through a differentiable solution (DiffPD). We also advance PD in application to soft robotics by proposing a predictive model for pneumatic soft robot actuation. Through our case-studies, we provide strategies and algorithms for matching real-world physics in simulation, making PD useful for soft robots.
Measuring the Transferability of $\ell_\infty$ Attacks by the $\ell_2$ Norm

Deep neural networks could be fooled by adversarial examples with trivial differences to original samples. To keep the difference imperceptible in human eyes, researchers bound the adversarial perturbations by the $\ell_\infty$ norm, which is now commonly served as the standard to align the strength of different attacks for a fair comparison. However, we propose that using the $\ell_\infty$ norm alone is not sufficient in measuring the attack strength, because even with a fixed $\ell_\infty$ distance, the $\ell_2$ distance also greatly affects the attack transferability between models. Through the discovery, we reach more in-depth understandings towards the attack mechanism, i.e., several existing methods attack black-box models better partly because they craft perturbations with 70% to 130% larger $\ell_2$ distances. Since larger perturbations naturally lead to better transferability, we thereby advocate that the strength of attacks should be simultaneously measured by both the $\ell_\infty$ and $\ell_2$ norm. Our proposal is firmly supported by extensive experiments on ImageNet dataset from 7 attacks, 4 white-box models, and 9 black-box models.
Critical Points in Quantum Generative Models

One of the most important properties of neural networks is the clustering of local minima of the loss function near the global minimum, enabling efficient training. Though generative models implemented on quantum computers are known to be more expressive than their traditional counterparts, it has empirically been observed that these models experience a transition in the quality of their local minima. Namely, below some critical number of parameters, all local minima are far from the global minimum in function value; above this critical parameter count, all local minima are good approximators of the global minimum. Furthermore, for a certain class of quantum generative models, this transition has empirically been observed to occur at parameter counts exponentially large in the problem size, meaning practical training of these models is out of reach. Here, we give the first proof of this transition in trainability, specializing to this latter class of quantum generative model. We use techniques inspired by those used to study the loss landscapes of classical neural networks. We also verify that our analytic results hold experimentally even at modest model sizes.
(Optimal) Online Bipartite Matching with Predicted Degrees

We propose a model for online graph problems where algorithms are given access to an oracle that predicts (e.g., based on past data) the degrees of nodes in the graph. Within this model, we study the classic problem of online bipartite matching, and a natural greedy matching algorithm called MinPredictedDegree, which uses predictions of the degrees of ofﬂine nodes. For the bipartite version of a stochastic graph model due to Chung, Lu, and Vu where the expected values of the ofﬂine degrees are known and used as predictions, we show that MinPredictedDegree stochastically dominates any other online algorithm, i.e., it is optimal for graphs drawn from this model. Since the “symmetric” version of the model, where all online nodes are identical, is a special case of the well-studied “known i.i. d. model”, it follows that the competitive ratio of MinPredictedDegree on such inputs is at least 0.7299. For the special case of graphs with power law degree distributions, we show that MinPredictedDegree frequently produces matchings almost as large as the true maximum matching on such graphs. We complement these results with an extensive empirical evaluation showing that MinPredictedDegree compares favorably to state-of-the-art online algorithms for online matching.
Samsung’s System for the IWSLT 2019 End-to-End Speech Translation Task

This paper describes the submission to IWSLT 2019 End- to-End speech translation task by Samsung R&D Institute, Poland. We decided to focus on end-to-end English to German TED lectures translation and did not provide any submission for other speech tasks. We used a slightly altered Transformer architecture with standard convolutional layer preparing the audio input to Transformer en- coder. Additionally, we propose an audio segmentation al- gorithm maximizing BLEU score on tst2015 test set.
Domain Adaptation of Document-Level NMT in IWSLT19

We describe our four NMT systems submitted to the IWSLT19 shared task in English→Czech text-to-text translation of TED talks. The goal of this study is to understand the interactions between document-level NMT and domain adaptation. All our systems are based on the Transformer model implemented in the Tensor2Tensor framework. Two of the systems serve as baselines, which are not adapted to the TED talks domain: SENTBASE is trained on single sen- tences, DOCBASE on multi-sentence (document-level) sequences. The other two submitted systems are adapted to TED talks: SENTFINE is fine-tuned on single sentences, DOCFINE is fine-tuned on multi-sentence sequences. We present both automatic-metrics evaluation and manual analysis of the translation quality, focusing on the differences between the four systems.
KIT’s Submission to the IWSLT 2019 Shared Task on Text Translation

In this paper, we describe KIT’s submission for the IWSLT 2019 shared task on text translation. Our system is based on the transformer model [1] using our in-house implementation. We augment the available training data using back-translation and employ fine-tuning for the final model. For our best results, we used a 12-layer transformer-big config- uration, achieving state-of-the-art results on the WMT2018 test set. We also experiment with student-teacher models to improve performance of smaller models.
Advances on pathogenesis of migraine in cold regions

Abstract Migraine is a common primary headache which seriously affects the quality of patients’ life due to the high prevalence and disability rate. Recent years a large number of studies have found that temperature is directly bound to migraine and migraine patients in cold regions have higher prevalence, different manifestations and poor response to the conventional therapy. We propose in this review article a new concept of migraine in cold regions on the basis of geography and summarize the research advances on the pathogenesis of migraine in cold regions to provide conceptual basis for the clinical diagnosis and treatment of this disease entity.
Surface modified nanoparticles: a green catalyst for the reduction of ketones

Abstract In the present study, a creative and eco-friendly synthetic method was demonstrated for the reduction of ketones using surface modified metal oxide nanoparticles (MO NPs). Nickel oxide, iron oxide and copper oxide nanoparticles were biosynthesized from the aqueous immature fruit extract of Cocos nucifera and the nanoparticle’s surface was modified using glycerol to improve the catalytic efficiency. Surface modification of the nanoparticles enhanced the catalytic activity of nanoparticles as compared to the unmodified metal oxide nanoparticles (MO NPs) for the reduction of ketones. Among the MO NPs used as catalysts for the reduction of acetophenone derivatives, glycerol modified iron oxide nanoparticles showed greater results with 74%–91% of yield due to its uniform morphology. The catalyst retained its stability even after seven repeated cycles of reaction.
A new decade awaits sticky platelet syndrome: where are we now, how do we manage and what are the complications?

ABSTRACT Introduction Sticky platelet syndrome is a less known platelet function disorder with a familiar occurrence and likely genetic background. Clinically, it is characterized by an increased risk of venous and arterial thromboembolic events and obstetric placenta-mediated complications. The increased aggregation after low-dose ADP and/or epinephrine is its distinctive laboratory feature. Though described for almost 40 years, several issues regarding its etiology, involved pathomechanisms, genetic background, optimal diagnostic and treatment approach remain controversial. Areas covered The work aims to summarize published studies, the actual definition of the syndrome, and point out its drawbacks. A literature search on Medline, Embase, and archives from EHA congresses was performed (terms: ‘sticky platelet syndrome’ – ‘platelet hyperreactivity’ – ‘platelet hyperaggregability’). The authors added in their unpublished data. The introductory overview of the present understanding is followed by the discussion of the pathophysiologic, diagnostic, and therapeutic problems. Expert Opinion Despite the growing evidence provided by case reports and series, the lack of robust studies limits the decision-making on diagnostics and management. The diagnostic issues, particularly the standardization of light transmission aggregometry, represent the crucial problem for the broader acceptance of the syndrome. PLAIN LANGUAGE SUMMARY Sticky platelet syndrome is aplatelet function disorder. It is associated with an increased risk of venous thromboembolism, arterial thrombosis, and obstetric placenta-mediated complications. Increased aggregation after low-dose ADP and/or epinephrine is the defining laboratory feature. Furthermore, afew studies report the familiar occurrence with possible genetic background. Several issues regarding the syndrome remain controversial: its exact etiology, genetics, optimal diagnostic, and treatment approach. These uncertainties provide ground for debate of the syndrome as aunique clinical entity. The review has two goals. Firstly, it summarizes the published studies and the actual definition of the syndrome. Secondly, it tries to point out the open pathophysiologic, diagnostic, and therapeutic problems.
Prevalence of mental health and suicide risk in prisons in low- and middle-income countries: a rapid review

ABSTRACT The majority of the world’s total prison population resides in low- and middle-income countries (LMIC), but most research into the prevalence of mental illness and suicide risk comes from high-income countries (HIC). The provision of mental health care varies widely across prisons in LMIC, with reported disparity between high levels of need and funding, resources and services. This rapid review aims to explore the existing evidence-base relating to suicide and mental health prevalence in prisons in LMIC. A total of 2,799 published and grey literature were identified through systematic searches, with 35 papers included in the final review. The prevalence of suicide ideation was lower among prisoners in LMIC than HIC, with common mental illness, severe mental illness, and substance misuse more prevalent among LMIC prisoners. Rates of suicide ideation and common mental illness were higher among females than males. These findings highlight the need for dedicated research using LMIC samples and consideration of resources within LMIC prisons. The results of this analysis can be used to inform next steps to tackle the high level of unmet mental health needs amongst prisoner populations in LMIC.
Performance governance and management in higher education revisited: international developments and perspectives

ABSTRACT The article identifies current trends with respect to the governance and management of institutional performance in higher education institutions and discusses how recent developments in this area can be interpreted. It is argued that performance governance is changing with respect to the way performances are documented, measured and disseminated. It is argued that meaning creation and the relationship between institutional achievements and their significance for current societal challenges are becoming more focal. The article showcases recent policy initiatives and practices with respect to performance and accountability. The article ends by highlighting possible implications for institutional management and leadership.
Quality management, performance measurement and indicators in higher education institutions: between burden, inspiration and innovation

ABSTRACT Performance indicators are increasingly used to measure the performance and quality of higher education. The purpose of this article is to discuss their role for reflecting on the challenges faced by high-participation higher education systems, regarding quality of student outcomes, equity of access, societal relevance and financial sustainability. Based on a review of existing international comparable metrics, policy and scholarly literature on higher education performance, the article discusses the strengths and weaknesses of current performance indicators and the perennial tension between the burden of accountability and the inspiration and innovation that may result from the developmental use of performance indicators for improvement. It concludes by summarising some observable results of performance and quality management and reflecting on some possible future trajectories.
Performance indicators in higher education quality management of learning and teaching: lessons from a benchlearning exercise of six European universities

ABSTRACT The article presents results of an Erasmus+ project that aimed at suggesting a comprehensive performance indicator set for learning and teaching. Based on a benchlearning exercise among the six project universities, the article presents findings of an exploratory research on the assessment of the developed indicator set by members of four stakeholder groups (leadership, quality management staff, teachers, students). It emerges that learning and teaching quality should be assessed through a holistic approach, though some learning and teaching domains (competences and learning outcomes) appear more important for learning and teaching quality enhancement. Perception on the usefulness of the project’s performance indicator list varies considerably among different stakeholders, influenced by both external factors and consolidated internal practices. Moreover, findings highlight a modest awareness of how learning and teaching quality is assessed. The project results may be a diagnostic instrument or a strategic opportunity to complement existing institutional and national quality management systems in learning and teaching.
The impact of hyperglycemia upon BeWo trophoblast cell metabolic function: A multi-OMICS and functional metabolic analysis

Pre-existing and gestationally-developed diabetes mellitus have been linked with impaired metabolic function in placental villous trophoblast cells, that is thought to underlie the development of metabolic diseases early in the lives of the exposed offspring. Previous research using placental cell lines and ex vivo trophoblast preparations have highlighted hyperglycemia as an important independent regulator of placental function. However, it is poorly understood if hyperglycemia directly influences aspects of placental metabolic function, including nutrient storage and mitochondrial respiration, that have been found to be impaired in diabetic placentae. Therefore, the current study examined metabolic and mitochondrial function as well as nutrient storage in both undifferentiated cytotrophoblast and differentiated syncytiotrophoblast BeWo cells cultured under hyperglycemia conditions (25 mM glucose) for 72 hours to further characterize the direct impacts of placental hyperglycemic exposure. Hyperglycemic-exposed BeWo trophoblasts displayed increased triglyceride and glycogen nutrient stores. However, specific functional readouts of metabolic enzyme activity and mitochondrial oxidative respiratory activity were not altered. We speculated that increased glycogen content may reduce free glucose available for oxidation and subsequently protect trophoblast cells from mitochondrial damage during acute high glucose exposures. To further characterize the impacts of independent hyperglycemia, the current study subsequently utilized a multi-omics approach and evaluated the transcriptomic and metabolomic signatures of BeWo cytotrophoblasts. Hyperglycemic exposure was associated with an altered transcriptomic profile (e.g. increased expression of ACSL1 (+1.36 fold-change (FC), HSD11B2 (+1.35 FC), and RPS6KA5 (+1.32 FC)), and metabolome profile (e.g. increased levels lactate (+2.72 FC), malonate (+3.74 FC), and riboflavin (+3.68 FC)) in BeWo cytotrophoblasts that further highlighted trophoblast metabolic function is modulated independently by hyperglycemia. Overall, these results demonstrate that hyperglycemia is an important independent regulator of key areas of placental metabolic function and nutrient storage, and these data continue to expand our knowledge on the mechanisms governing the development of placental dysfunction.
The Drosophila melanogaster enzyme glycerol-3-phosphate dehydrogenase 1 is required for oogenesis, embryonic development, and amino acid homeostasis

As the fruit fly, Drosophila melanogaster, progresses from one life stage to the next, many of the enzymes that compose intermediary metabolism undergo substantial changes in both expression and activity. These predictable shifts in metabolic flux allow the fly meet stage-specific requirements for energy production and biosynthesis. In this regard, the enzyme Glyerol-3-phosphate dehydrogenase (GPDH1) has been the focus of biochemical genetics studies for several decades, and as a result, is one of the most well characterized Drosophila enzymes. Among the findings of these earlier studies is that GPDH1 acts throughout the fly lifecycle to promote mitochondrial energy production and triglyceride accumulation while also serving a key role in maintaining redox balance. Here we expand upon the known roles of GPDH1 during fly development by examining how depletion of both the maternal and zygotic pools of this enzyme influences development, metabolism, and viability. Our findings not only confirm previous observations that Gpdh1 mutants exhibit defects in larval development, lifespan, and fat storage but also reveal that GPDH1 serves essential roles in oogenesis and embryogenesis. Moreover, metabolomics analysis reveals that a Gpdh1 mutant stock maintained in a homozygous state exhibits larval metabolic defects that significantly differ from those observed in the F1 mutant generation. Overall, our findings highlight unappreciated roles for GPDH1 in early development and uncover previously undescribed metabolic adaptations that could allow flies to survive loss of this key enzyme.
Increased low-frequency brain responses to music after psilocybin therapy for depression

Psychedelic-assisted psychotherapy with psilocybin is an emerging therapy with great promise for depression, and modern psychedelic therapy (PT) methods incorporate music as a key element. Music is an effective emotional/hedonic stimulus that could also be useful in assessing changes in emotional responsiveness following psychedelic therapy. Brain responses to music were assessed before and after PT using functional Magnetic Resonance Imaging (fMRI) and ALFF (Amplitude of Low Frequency Fluctuations) analysis methods. Nineteen patients with treatment-resistant depression underwent two treatment sessions involving administration of psilocybin, with MRI data acquired one week prior and the day after completion of the second of two psilocybin dosing sessions. Comparison of music-listening and resting-state scans revealed significantly greater ALFF in bilateral superior temporal cortex for the post-treatment music scan, and in the right ventral occipital lobe for the post-treatment resting-state scan. ROI analyses of these clusters revealed a significant effect of treatment in the superior temporal lobe for the music scan only. Somewhat consistently, voxelwise comparison of treatment effects showed relative increases for the music scan in the bilateral superior temporal lobes and supramarginal gyrus, and relative decreases in the medial frontal lobes for the resting-state scan. ALFF in these music-related clusters was significantly correlated with intensity of subjective effects felt during the dosing sessions. These data suggest a specific effect of PT on the brain’s response to a hedonic stimulus (music), implying an elevated responsiveness to music after psilocybin therapy that was related to subjective drug effects felt during dosing.