Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-loop three-gluon vertex from the Curci-Ferrari model and its leading infrared behavior to all loop orders

We evaluate the three-gluon vertex with one vanishing external momentum within the Curci-Ferrari (CF) model at two-loop order and compare our results to Landau-gauge lattice simulations of the same vertex function for the SU(2) and SU(3) gauge groups in four dimensions. The parameters of the model being adjusted by fitting the two-point functions to lattice data, our evaluation of the three-gluon vertex arises as a pure prediction. We find that two-loop corrections systematically improve the agreement between the model and the lattice data as compared to earlier one-loop calculations, with a better agreement in the SU(3) case, as already seen in previous studies [1,2]. We also analyze the renormalization scheme dependence of our calculation. In all cases, this dependence diminishes when two-loop corrections are included, which is consistent with the perturbative CF paradigm. In addition, we study the low momentum regime of the three-gluon vertex in relation with the possibility of zero-crossing. Within the CF model, we show that the leading infrared behavior of the exact vertex is given by the same linear logarithm that arises at one-loop order, multiplied by the all orders cubic ghost dressing function at zero-momentum (we provide similar exact results for other vertex functions). We argue that this property remains true within the FP framework under the assumption that the resummed gluon propagator features a decoupling behavior. This shows that the zero-crossing is a property of the exact three-gluon vertex function. Within the CF model, we find however that the scale of the zero-crossing is considerably reduced when going from one- to two-loop order. This seems consistent with some recent lattice simulations [3]. Our analysis also allows us to support recent claims about the dominance of the tree-level tensor component [4].
Classical and Quantum Algorithms for Variants of Subset-Sum via Dynamic Programming

Subset-Sum is an NP-complete problem where one must decide if a multiset of $n$ integers contains a subset whose elements sum to a target value $m$. The best-known classical and quantum algorithms run in time $\tilde{O}(2^{n/2})$ and $\tilde{O}(2^{n/3})$, respectively, based on the well-known meet-in-the-middle technique. Here we introduce a novel classical dynamic-programming-based data structure with applications to Subset-Sum and a number of variants, including Equal-Sums (where one seeks two disjoint subsets with the same sum), 2-Subset-Sum (a relaxed version of Subset-Sum where each item in the input set can be used twice in the summation), and Shifted-Sums, a generalization of both of these variants, where one seeks two disjoint subsets whose sums differ by some specified value. Given any modulus $p$, our data structure can be constructed in time $O(n^2p)$, after which queries can be made in time $O(n^2)$ to the lists of subsets summing to any value modulo $p$. We use this data structure in combination with variable-time amplitude amplification and a new quantum pair finding algorithm, extending the quantum claw finding algorithm to the multiple solutions case, to give an $O(2^{0.504n})$ quantum algorithm for Shifted-Sums, an improvement on the best-known $O(2^{0.773n})$ classical running time. Incidentally, we obtain new $\tilde{O}(2^{n/2})$ and $\tilde{O}(2^{n/3})$ classical and quantum algorithms for Subset-Sum, not based on the seminal meet-in-the-middle method. We also study Pigeonhole Equal-Sums and Pigeonhole Modular Equal-Sums, where the existence of a solution is guaranteed by the pigeonhole principle. For the former problem, we give faster classical and quantum algorithms with running time $\tilde{O}(2^{n/2})$ and $\tilde{O}(2^{2n/5})$, respectively. For the more general modular problem, we give a classical algorithm that also runs in time $\tilde{O}(2^{n/2})$.
Hyper-Representations for Pre-Training and Transfer Learning

Learning representations of neural network weights given a model zoo is an emerging and challenging area with many potential applications from model inspection, to neural architecture search or knowledge distillation. Recently, an autoencoder trained on a model zoo was able to learn a hyper-representation, which captures intrinsic and extrinsic properties of the models in the zoo. In this work, we extend hyper-representations for generative use to sample new model weights as pre-training. We propose layer-wise loss normalization which we demonstrate is key to generate high-performing models and a sampling method based on the empirical density of hyper-representations. The models generated using our methods are diverse, performant and capable to outperform conventional baselines for transfer learning. Our results indicate the potential of knowledge aggregation from model zoos to new models via hyper-representations thereby paving the avenue for novel research directions.
Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark

Most existing works on few-shot object detection (FSOD) focus on a setting where both pre-training and few-shot learning datasets are from a similar domain. However, few-shot algorithms are important in multiple domains; hence evaluation needs to reflect the broad applications. We propose a Multi-dOmain Few-Shot Object Detection (MoFSOD) benchmark consisting of 10 datasets from a wide range of domains to evaluate FSOD algorithms. We comprehensively analyze the impacts of freezing layers, different architectures, and different pre-training datasets on FSOD performance. Our empirical results show several key factors that have not been explored in previous works: 1) contrary to previous belief, on a multi-domain benchmark, fine-tuning (FT) is a strong baseline for FSOD, performing on par or better than the state-of-the-art (SOTA) algorithms; 2) utilizing FT as the baseline allows us to explore multiple architectures, and we found them to have a significant impact on down-stream few-shot tasks, even with similar pre-training performances; 3) by decoupling pre-training and few-shot learning, MoFSOD allows us to explore the impact of different pre-training datasets, and the right choice can boost the performance of the down-stream tasks significantly. Based on these findings, we list possible avenues of investigation for improving FSOD performance and propose two simple modifications to existing algorithms that lead to SOTA performance on the MoFSOD benchmark. The code is available at https://github.com/amazon-research/few-shot-object-detection-benchmark.
Operational significance of nonclassicality in nonequilibrium Gaussian quantum thermometry

We provide new operational significance of nonclassicality in nonequilibrium temperature estimation of bosonic baths with Gaussian probe states and Gaussian dynamics. We find a bound on the thermometry performance using classical probe states. Then we show that by using nonclassical probe states, single-mode and two-mode squeezed vacuum states, one can profoundly improve the classical limit. Interestingly, we observe that this improvement can also be achieved by using Gaussian measurements. Hence, we propose a fully Gaussian protocol for enhanced thermometry, which can simply be realized and used in quantum optics platforms.
FairGRAPE: Fairness-aware GRAdient Pruning mEthod for Face Attribute Classification

Existing pruning techniques preserve deep neural networks' overall ability to make correct predictions but may also amplify hidden biases during the compression process. We propose a novel pruning method, Fairness-aware GRAdient Pruning mEthod (FairGRAPE), that minimizes the disproportionate impacts of pruning on different sub-groups. Our method calculates the per-group importance of each model weight and selects a subset of weights that maintain the relative between-group total importance in pruning. The proposed method then prunes network edges with small importance values and repeats the procedure by updating importance values. We demonstrate the effectiveness of our method on four different datasets, FairFace, UTKFace, CelebA, and ImageNet, for the tasks of face attribute classification where our method reduces the disparity in performance degradation by up to 90% compared to the state-of-the-art pruning algorithms. Our method is substantially more effective in a setting with a high pruning rate (99%). The code and dataset used in the experiments are available at https://github.com/Bernardo1998/FairGRAPE
A Research on Continuous Using Intention of the Q&A Community A Research on Continuous Using Intention of the Q&A Community

We set “zhihu.com” as an example and integrate a new model to study the user's intention to continuously using the network Q & A community platform based on traditional S-O-R model, Information System (IS) success model, the unified theory of acceptance and use of technology (UTAUT) model and Error Correction Model (ECM). In the social dimension, technological dimension, service quality dimension, we carried out an empirical research on the important factors affecting the user's continually using the network Q & A community platform. Finally, we put forward suggestions and Countermeasures on how to manage the network innovation community and encourage users to participate it effectively.
Analysis and gyrokinetic simulation of MHD Alfven wave interactions

The study of low-frequency turbulence in magnetized plasmas is a difficult problem due to both the enormous range of scales involved and the variety of physics encompassed over this range. Much of the progress that has been made in turbulence theory is based upon a result from incompressible magnetohydrodynamics (MHD), in which energy is only transferred from large scales to small via the collision of Alfvén waves propagating oppositely along the mean magnetic field. Improvements in laboratory devices and satellite measurements have demonstrated that, while theories based on this premise are useful over inertial ranges, describing turbulence at scales that approach particle gyroscales requires new theory. In this thesis, we examine the limits of incompressible MHD theory in describing collisions between pairs of Alfvén waves. This interaction represents the fundamental unit of plasma turbulence. To study this interaction, we develop an analytic theory describing the nonlinear evolution of interacting Alfvén waves and compare this theory to simulations performed using the gyrokinetic code AstroGK. Gyrokinetics captures a much richer set of physics than that described by incompressible MHD, and is well-suited to describing Alfvénic turbulence around the ion gyroscale. We demonstrate that AstroGK is well suited to the study of physical Alfvén waves by reproducing laboratory Alfvén dispersion data collected using the LAPD. Additionally, we have developed an initialization alogrithm for use with AstroGK that allows exact Alfvén eigenmodes to be initialized with user specified amplitudes and phases. We demonstrate that our analytic theory based upon incompressible MHD gives excellent agreement with gyrokinetic simulations for weakly turbulent collisions in the limit that k⊥ρi 1. In this limit, agreement is observed in the time evolution
A L P A T I T I T H A A T Y E

he management of patients with ST segment elevation acute myocardial infarction has evolved considerably during the last decades. Restoration of coronary flow can be achieved pharmacologically by the administration of thrombolytic or fibrinolytic drugs, which are widely available and easy to administer, or mechanically by means of percutaneous transluminal coronary angioplasty (PTCA) with or without antecedent drug treatment, which is less available and more complex to implement and carry out. The two strategies are currently subject of a vivid debate by protagonists and antagonists of the two approaches. This paper summarises the available evidence with emphasis on the randomised comparisons of direct PTCA with thrombolysis. These studies and findings are open for interpretation. They are reviewed and discussed, and a rational and pragmatic approach is proposed. hamper ventricular have stimulated (re)appraisal of mechanical reperfusion.
A multiwavelength study of the accretion region in magnetic cataclysmic variables

This thesis is a study of the accretion regions in magnetic cataclysmic variables (MCVs). After an overview of our current understanding of accretion regions in MCVs, I describe a model I have developed which models the polar­ ized cyclotron emission from MCVs. I then use this model to show how different types of cyclotron light curves arise for various shaped accretion regions. I then use this model on the MCV PQ Gem. PQ Gem is an asynchronous interm ediate polar (a subclass of the MCVs) w ith was discovered to have polarized emission on the spin period of the white dwarf. I t was there­ fore an ideal candidate to observe and model. I found th a t the polarized light curves could be modelled by emission from two extended emission regions located on opposite hem ispheres of the white dwarf. The model light curves could explain the observations as a combination of self occul­ tation and absorption of the emission regions. The model is also more self consistent than other attem pts in th a t it models the m ulti color observa­ tions of PQ Gem sim ultaneously and is in agreem ent w ith X-ray observa­ tions. I also give an estim ate for the m agnetic field strength of the white dw arf in th is system. Several authors over the past decade have modelled cyclotron emis­ sion from MCVs using their own models. However model fits have until now been obtained by a tria l and error method. I obtained the results for PQ Gem by a sim ilar method. Therefore I have developed an optim isa­ tion technique th a t is more analytical and objective than previous m eth­ ods. This was achieved with the combination of a genetic algorithm and a more traditional line m inim isation routine. I described the technique in detail and rigorously tes t it on sim ulated light curves. Having developed the optimisation technique, I then applied it to real da ta in order to predict the shape and size of their emission regions. The results are compared to previous models and preconceptions on the shape of emission regions. Also, by m aking assum ptions on the nature of the magnetic field, I then trace the magnetic field lines th a t feed the emis­ sion regions to the orbital plane in order to locate the threading regions in these systems. Finally, I give a sum m ary of this thesis and discuss possible work for the future.
Double-stranded RNA drives SARS-CoV-2 nucleocapsid protein to undergo phase separation at specific temperatures

Abstract Nucleocapsid protein (N-protein) is required for multiple steps in betacoronaviruses replication. SARS-CoV-2-N-protein condenses with specific viral RNAs at particular temperatures making it a powerful model for deciphering RNA sequence specificity in condensates. We identify two separate and distinct double-stranded, RNA motifs (dsRNA stickers) that promote N-protein condensation. These dsRNA stickers are separately recognized by N-protein's two RNA binding domains (RBDs). RBD1 prefers structured RNA with sequences like the transcription-regulatory sequence (TRS). RBD2 prefers long stretches of dsRNA, independent of sequence. Thus, the two N-protein RBDs interact with distinct dsRNA stickers, and these interactions impart specific droplet physical properties that could support varied viral functions. Specifically, we find that addition of dsRNA lowers the condensation temperature dependent on RBD2 interactions and tunes translational repression. In contrast RBD1 sites are sequences critical for sub-genomic (sg) RNA generation and promote gRNA compression. The density of RBD1 binding motifs in proximity to TRS-L/B sequences is associated with levels of sub-genomic RNA generation. The switch to packaging is likely mediated by RBD1 interactions which generate particles that recapitulate the packaging unit of the virion. Thus, SARS-CoV-2 can achieve biochemical complexity, performing multiple functions in the same cytoplasm, with minimal protein components based on utilizing multiple distinct RNA motifs that control N-protein interactions.
Oxytocin suppresses CXCL10 production in TNF‐α‐stimulated human dental pulp stem cells

Oxytocin (OX) is a posterior pituitary hormone secreted into the blood from axon terminals projecting from the posterior pituitary. Recent reports indicate OX plays an important role in the progression of inflammatory diseases such as rheumatoid arthritis. Pulpitis is caused by the activation of the biological defense mechanism of the dental pulp against cariogenic bacteria. However, the role of OX in the pathogenesis of pulpitis remains unknown. The aim of this study was to examine the effect of OX on CXC chemokine ligand 10 (CXCL10) production in human dental pulp stem cells (HDPSCs). Expression of the oxytocin receptor (OXR) on HDPSCs was detected by Western blot analysis and immunofluorescence. CXCL10 production in HDPSCs was measured using an enzyme‐linked immunosorbent assay kit. Western blot analysis was performed to determine the phosphorylation levels of signal transduction molecules, including nuclear factor kappa B, mitogen‐activated protein kinases (MAPKs), and Akt in HDPSCs. HDPSCs expressed OXR. OX significantly decreased CXCL10 production in tumor necrosis factor (TNF)‐α‐stimulated HDPSCs. The p38 MAPK and Akt pathways were related to the OX‐suppressed CXCL10 production in TNF‐α‐stimulated HDPSCs. These results indicate that OX appears to modulate the immune response in pulpitis via suppression of CXCL10 production by HDPSCs.
Topical emollient application in term healthy newborns: A systematic review

Background This systematic review of randomized trials assessed the effect of emollient application compared to no emollient application in term or near-term healthy newborns. Methods We searched MEDLINE via PubMed, Cochrane CENTRAL, Embase, and CINAHL (updated until November 2021), clinical trials databases, and reference lists of retrieved articles. Key outcomes were neonatal mortality, systemic infections, atopic dermatitis, skin condition, and adverse events. Two authors separately evaluated the risk of bias, extracted data, and synthesized effect estimates using relative risks (RR). The GRADE approach was used to assess the certainty of evidence. Results We screened 19 243 records and included 16 eligible trials involving 5643 participants. Five trials recruited 3352 healthy newborns (term = 728; gestation ≥35 weeks = 2624); and 11 trials included 2291 term newborns who were ‘at risk’ for developing atopy but were otherwise healthy. We conducted a separate analysis for these two groups of newborns. Emollient application (creams or nut, seed, and vegetable oils) started in the neonatal period and continued for four weeks to two years across studies. Meta-analysis for term healthy newborns suggests that topical emollient application may have little to no effect on atopic dermatitis (RR = 1.29, 95% CI = 0.96-1.72; two trials, 1408 newborns; low certainty evidence). Effects on food allergy (RR = 0.84; 95% CI = 0.42-1.70; one trial, 233 newborns), allergic sensitization to food allergens (RR 1.31; 95% CI 1.03 to 1.68; one trial, 234 newborns) and inhalational allergens (RR = 0.97; 95% CI = 0.44, 2.14; 1 trial, 234 newborns), skin dryness (RR = 0.74, 95% CI = 0.55-1.00; two trials, 294 newborns), and skin problems (RR = 0.92, 95% CI = 0.81-1.05; two trials, 292 newborns) were uncertain. Meta-analysis for ‘at-risk’ newborns suggests that intervention probably lowers the risk of atopic dermatitis (RR = 0.74, 95% CI = 0.63-0.86; 11 studies, 1988 infants; moderate certainty evidence), but may have little or no effect on food allergy and allergic sensitization to food or inhalation allergens. The effect on skin dryness and skin rash was uncertain. Conclusions Topical emollient application may not prevent atopic dermatitis in term healthy newborns. There is little data for other skin and allergic outcomes. Registration Priyadarshi M, Balachander B, Rao S, Gupta S, Sankar MJ. Use of emollients in term healthy newborns: A systematic review. PROSPERO 2020 CRD42020177437.
Zinc controls PML nuclear body formation through regulation of a paralog specific auto-inhibition in SUMO1

Abstract SUMO proteins are important regulators of many key cellular functions in part through their ability to form interactions with other proteins containing SUMO interacting motifs (SIMs). One characteristic feature of all SUMO proteins is the presence of a highly divergent intrinsically disordered region at their N-terminus. In this study, we examine the role of this N-terminal region of SUMO proteins in SUMO–SIM interactions required for the formation of nuclear bodies by the promyelocytic leukemia (PML) protein (PML-NBs). We demonstrate that the N-terminal region of SUMO1 functions in a paralog specific manner as an auto-inhibition domain by blocking its binding to the phosphorylated SIMs of PML and Daxx. Interestingly, we find that this auto-inhibition in SUMO1 is relieved by zinc, and structurally show that zinc stabilizes the complex between SUMO1 and a phospho-mimetic form of the SIM of PML. In addition, we demonstrate that increasing cellular zinc levels enhances PML-NB formation in senescent cells. Taken together, these results provide important insights into a paralog specific function of SUMO1, and suggest that zinc levels could play a crucial role in regulating SUMO1-SIM interactions required for PML-NB formation and function.
Experimental removal of nematode parasites increases growth, sprint speed, and mating success in brown anole lizards

Abstract Parasites interact with nearly all free‐living organisms and can impose substantial fitness costs by reducing host survival, mating success, and fecundity. Parasites may also indirectly affect host fitness by reducing growth and performance. However, experimentally characterizing these costs of parasitism is challenging in the wild because common antiparasite drug formulations require repeated dosing that is difficult to implement in free‐living populations, and because the extended‐release formulations that are commercially available for livestock and pets are not suitable for smaller animals. To address these challenges, we developed a method for the long‐term removal of nematode parasites from brown anole lizards (Anolis sagrei) using an extended‐release formulation of the antiparasite drug ivermectin. This treatment eliminated two common nematode parasites in captive adult males and dramatically reduced the prevalence and intensity of infection by these parasites in wild adult males and females. Experimental parasite removal significantly increased the sprint speed of captive adult males, the mating success of wild adult males, and the growth of wild juveniles of both sexes. Although parasite removal did not have any effect on survival in wild anoles, parasites may influence fitness directly through reduced mating success and indirectly through reduced growth and performance. Our method of long‐term parasite manipulation via an extended‐release formulation of ivermectin should be readily adaptable to many other small vertebrates, facilitating experimental tests of the extent to which parasites affect host phenotypes, fitness, and eco‐evolutionary dynamics in the wild.