Miracle-dz/UniFAR-FTUs huggingface dataset:
Blizzard: Adding True Persistence to Main Memory Data Structures

Persistent memory (PMEM) devices present an opportunity to retain the flexibility of main memory data structures and algorithms, but augment them with reliability and persistence. The challenge in doing this is to combine replication (for reliability) and failure atomicity (for persistence) with concurrency (for fully utilizing persistent memory bandwidth). These requirements are at odds due to the sequential nature of replicating a log of updates versus concurrent updates that are necessary for fully leveraging the path from CPU to memory. We present Blizzard -- a fault-tolerant, PMEM-optimized persistent programming runtime. Blizzard addresses the fundamental tradeoff by combining (1) a coupled operations log that permits tight integration of a PMEM-specialized user-level replication stack with a PMEM-based persistence stack, and (2) explicit control over the commutativity among concurrent operations. We demonstrate the generality and potential of Blizzard with three illustrative applications with very different data structure requirements for their persistent state. These use cases demonstrate that with Blizzard, PMEM native data structures can deliver up to 3.6x performance benefit over the alternative purpose-build persistent application runtimes, while being simpler and safer (by providing failure atomicity and replication).
Neutron Star Kicks and Implications for Their Rotation at Birth

Neutron stars are born out of core-collapse supernovae, and they are imparted natal kicks at birth as a consequence of asymmetric ejection of matter and possibly neutrinos. Unless the force resulting from the kicks is exerted exactly at their center, it will also cause the neutron star to rotate. In this paper, we discuss the possibility that neutron stars may receive off-center natal kicks at birth, which imprint a natal rotation. In this scenario, the observed pulsar spin and transverse velocity in the Galaxy are expected to correlate. We develop a model of the natal rotation imparted to neutron stars and constrain it by the observed population of pulsars in our Galaxy. When considering a single-kick position parameter, we find that the location of the off-center kick is Rkick=2.03−1.69+3.74 km at 90% confidence, and is robust when considering pulsars with different observed periods, transverse velocities, and ages. Nonetheless, the model encounters challenges in effectively fitting the data, particularly at small transverse velocities, prompting the exploration of alternative models that include more complex physics. Our framework could be used as a guide for core-collapse simulations of massive stars.
Hunting for gamma-ray emission from fast radio bursts

Context. Fast radio bursts (FRBs) are a recently discovered class of GHz-band, ms-duration, Jansky-level-flux astrophysical transients. Although hundreds of models have been proposed so far for FRB progenitors (the most popular ones involve magnetars), their physical origin and emission mechanism are still a mystery, making them one of the most compelling problems in astrophysics. Aims. FRBs are caused by astrophysical processes that are not yet understood. Exploring their high-energy counterpart is crucial for constraining their origin and emission mechanism. Methods. Thanks to more than 13 years of gamma-ray data collected by the Large Area Telescope on board the Fermi Gamma-ray Space Telescope, and to more than 1000 FRB events (from 561 non-repeating and 22 repeating sources), one of the largest samples created thus far, we performed the largest and deepest search for high-energy emission from FRB sources to date (between 100 MeV and 1 TeV). In addition to the analysis involving individual FRB events on different timescales (from a few seconds up to several years), we performed, for the first time, a stacking analysis on the full sample of FRB events as well as a search for triplet photons in coincidence with the radio event. Results. We do not detect significant emission, reporting the most stringent constraints, on short timescales, for the FRB-like emission from SGR 1935+2154 with Eγ < 1041 erg, corresponding to a factor η < 107 with respect to the emitted radio energy. Similarly, for the stacked signal of steady emission from all repeaters, the obtained upper limit (UL) on the FRBs luminosity (Lγ < 1.6 × 1043 erg s−1) is more than two orders of magnitude lower than those derived from the individual sources. Finally, no individual or triplet photons have been significantly associated with FRB events. We derived the LAT ms-sensitivity to be ∼0.3 ph cm−2 s−1 and constrained the gamma-ray energy Eγ,δT = 1 ms ≲ 1047(DL/150 Mpc)2 erg, ruling out a gamma-ray-to-radio energy ratio greater than 109 on ms timescales. Conclusions. The results reported here represent the most stringent UL reported so far on the high-energy emission from FRBs on short and long time scales, as well as on cumulative emission and individual photon searches. While the origin of FRBs is still unclear, our work provides important constraints for FRB modelling, which might shed light on their emission mechanism.
Time is Money: Strategic Timing Games in Proof-of-Stake Protocols

We propose a model suggesting that honest-but-rational consensus participants may play timing games, and strategically delay their block proposal to optimize MEV capture, while still ensuring the proposal's timely inclusion in the canonical chain. In this context, ensuring economic fairness among consensus participants is critical to preserving decentralization. We contend that a model grounded in honest-but-rational consensus participation provides a more accurate portrayal of behavior in economically incentivized systems such as blockchain protocols. We empirically investigate timing games on the Ethereum network and demonstrate that while timing games are worth playing, they are not currently being exploited by consensus participants. By quantifying the marginal value of time, we uncover strong evidence pointing towards their future potential, despite the limited exploitation of MEV capture observed at present.
Image Matching by Bare Homography

This paper presents Slime, a novel non-deep image matching framework which models the scene as rough local overlapping planes. This intermediate representation sits in-between the local affine approximation of the keypoint patches and the global matching based on both spatial and similarity constraints, providing a progressive pruning of the correspondences, as planes are easier to handle with respect to general scenes. Slime decomposes the images into overlapping regions at different scales and computes loose planar homographies. Planes are mutually extended by compatible matches and the images are split into fixed tiles, with only the best homographies retained for each pair of tiles. Stable matches are identified according to the consensus of the admissible stereo configurations provided by pairwise homographies. Within tiles, the rough planes are then merged according to their overlap in terms of matches and further consistent correspondences are extracted. The whole process only involves homography constraints. As a result, both the coverage and the stability of correct matches over the scene are amplified, together with the ability to spot matches in challenging scenes, allowing traditional hybrid matching pipelines to make up lost ground against recent end-to-end deep matching methods. In addition, the paper gives a thorough comparative analysis of recent state-of-the-art in image matching represented by end-to-end deep networks and hybrid pipelines. The evaluation considers both planar and non-planar scenes, taking into account critical and challenging scenarios including abrupt temporal image changes and strong variations in relative image rotations. According to this analysis, although the impressive progress done in this field, there is still a wide room for improvements to be investigated in future research.
Expressiveness Remarks for Denoising Diffusion Models and Samplers

Denoising diffusion models are a class of generative models which have recently achieved state-of-the-art results across many domains. Gradual noise is added to the data using a diffusion process, which transforms the data distribution into a Gaussian. Samples from the generative model are then obtained by simulating an approximation of the time reversal of this diffusion initialized by Gaussian samples. Recent research has explored adapting diffusion models for sampling and inference tasks. In this paper, we leverage known connections to stochastic control akin to the F\"ollmer drift to extend established neural network approximation results for the F\"ollmer drift to denoising diffusion models and samplers.
Make-An-Animation: Large-Scale Text-conditional 3D Human Motion Generation

Text-guided human motion generation has drawn significant interest because of its impactful applications spanning animation and robotics. Recently, application of diffusion models for motion generation has enabled improvements in the quality of generated motions. However, existing approaches are limited by their reliance on relatively small-scale motion capture data, leading to poor performance on more diverse, in-the-wild prompts. In this paper, we introduce Make-An-Animation, a text-conditioned human motion generation model which learns more diverse poses and prompts from large-scale image-text datasets, enabling significant improvement in performance over prior works. Make-An-Animation is trained in two stages. First, we train on a curated large-scale dataset of (text, static pseudo-pose) pairs extracted from image-text datasets. Second, we fine-tune on motion capture data, adding additional layers to model the temporal dimension. Unlike prior diffusion models for motion generation, Make-An-Animation uses a U-Net architecture similar to recent text-to-video generation models. Human evaluation of motion realism and alignment with input text shows that our model reaches state-of-the-art performance on text-to-motion generation. Generated samples can be viewed at https://azadis.github. io/make-an-animation.
The distribution of the maximum protection number in simply generated trees

The protection number of a vertex $v$ in a tree is the length of the shortest path from $v$ to any leaf contained in the maximal subtree where $v$ is the root. In this paper, we determine the distribution of the maximum protection number of a vertex in simply generated trees, thereby refining a recent result of Devroye, Goh and Zhao. Two different cases can be observed: if the given family of trees allows vertices of outdegree $1$, then the maximum protection number is on average logarithmic in the tree size, with a discrete double-exponential limiting distribution. If no such vertices are allowed, the maximum protection number is doubly logarithmic in the tree size and concentrated on at most two values. These results are obtained by studying the singular behaviour of the generating functions of trees with bounded protection number. While a general distributional result by Prodinger and Wagner can be used in the first case, we prove a variant of that result in the second case.
Understanding visual processing of motion: completing the picture using experimentally driven computational models of MT

Abstract Computational modeling helps neuroscientists to integrate and explain experimental data obtained through neurophysiological and anatomical studies, thus providing a mechanism by which we can better understand and predict the principles of neural computation. Computational modeling of the neuronal pathways of the visual cortex has been successful in developing theories of biological motion processing. This review describes a range of computational models that have been inspired by neurophysiological experiments. Theories of local motion integration and pattern motion processing are presented, together with suggested neurophysiological experiments designed to test those hypotheses.
Stochastic Thermodynamics of Multiple Co-Evolving Systems—Beyond Multipartite Processes

Many dynamical systems consist of multiple, co-evolving subsystems (i.e., they have multiple degrees of freedom). Often, the dynamics of one or more of these subsystems will not directly depend on the state of some other subsystems, resulting in a network of dependencies governing the dynamics. How does this dependency network affect the full system’s thermodynamics? Prior studies on the stochastic thermodynamics of multipartite processes have addressed this question by assuming that, in addition to the constraints of the dependency network, only one subsystem is allowed to change state at a time. However, in many real systems, such as chemical reaction networks or electronic circuits, multiple subsystems can—or must—change state together. Here, we investigate the thermodynamics of such composite processes, in which multiple subsystems are allowed to change state simultaneously. We first present new, strictly positive lower bounds on entropy production in composite processes. We then present thermodynamic uncertainty relations for information flows in composite processes. We end with strengthened speed limits for composite processes.
How to perform Fisher forecasts with simulations

The Fisher information matrix is a quantity of fundamental importance for information geometry and asymptotic statistics. In practice, it is widely used to quickly estimate the expected information available in a data set and guide experimental design choices. In many modern applications, it is intractable to analytically compute the Fisher information and Monte Carlo methods are used instead. The standard Monte Carlo method produces estimates of the Fisher information that can be biased when the Monte-Carlo noise is non-negligible. Most problematic is noise in the derivatives as this leads to an overestimation of the available constraining power, given by the inverse Fisher information. In this work we ﬁnd another simple estimate that is oppositely biased and produces an underestimate of the constraining power. This estimator can either be used to give approximate bounds on the parameter constraints or can be combined with the standard estimator to give improved, approximately unbiased estimates. Both the alternative and the combined estimators are asymptotically unbiased so can be also used as a convergence check of the standard approach. We discuss potential limitations of these estimators and provide methods to assess their reliability. These methods accelerate the convergence of Fisher forecasts, as unbiased estimates can be achieved with fewer Monte Carlo samples, and so can be used to reduce the simulated data set size by several orders of magnitude.
Weakening Description Invariance to Deal with Framing Effects: An Axiomatic Approach

The goal of this paper is to provide an axiomatic framework that can account for framing effects violating the so-called axiom of description invariance. Most existing discussions of the latter in economics are made with respect to Kahneman and Tversky's 1980s work. However, other psychologists have, over the last twenty-five years or so, progressively refined the conditions under which framing effects violating description invariance hold. Our axiomatic framework is motivated by these developments. We argue that description invariance is an implicit axiom of the standard model primarily on the formal structure of its object of choice and only derivatively on the formal structure of preferences. The conditions under which it is violated in psychologists' experiments are a useful guide to make it formally explicit. Furthermore, they also provide normative justifications for weakening it. We propose a way to do so in a mathematically tractable fashion that can account for all the variations in framing effects for which prospect theory cannot (at least straightforwardly) account for.
Constraints on the top quark’s charge with the CMS experiment

The standard model of particle physics is being probed at the LHC collider ring at Geneva. Amongst others, top-quark pairs are produced in large quantities. This makes precision measurements of top-quark properties possible. The top quark as the electroweak-isospin partner of the bottom quark is expected to have an electric charge of +2/3 e. A measurement is presented that discriminates between the top quark charge hypotheses of +2/3 e and −4/3 e using the muon+jets final state of top-pair events tt → (bμν)(bqq′). The measurement is performed with proton-proton collisions at √ s = 7 TeV recorded by the CMS detector in the year 2011, corresponding to an integrated luminosity of 5.0 fb−1. Charge correlations between the high-pT muons from the hard process and a muon from one of the bottom quarks are exploited to sort the events in a +2/3 e or −4/3 e category. The normalized asymmetry between both categories is calculated. The top quark charge scenario of −4/3 e would correspond to an asymmetry of A = −1. This is excluded with high significance while the measured asymmetry of A = 1.10 ± 0.12(stat.) ± 0.34(sys.) confirms the standard-model expectation of A = +1.
Insertion and iterated insertion as operations on formal languages

The operations of insertion ((<---)) and iterated insertion ((<---)*) are simple variants of Kleene's operations (.) and (,*) ({Kle 56}) in a manner similar to the operations shuffle and iterated shuffle (see e.g. {Jan 81}). Using the operation of iterated insertion, we can generate both the semi-Dyck and the two-sided Dyck languages (see e.g. {Har 78}) from certain finite subsets of these languages. Thus the class of languages of the form S('(<---)*) for finite S forms a natural class of generalized Dyck languages. We present several combinatorial results that demonstrate that the problems of equivalence, of ambiguous representation, of confluence (see {Bk 82}) and of regularity are decidable for this class of languages. In obtaining the latter result, we give two important general results involving the application of the theory of well quasi orders (see e.g. {Kru 72}) to the study of regular languages. On the other hand, we show that the problem "S('(<---)*) (INTERSECT) T('(<---)*) = {(lamda)}? " is undecidable for finite, unambiguous S and T. Furthermore, by extending the regular expressions to include the operations (<---) and (<---)(,*) we obtain the class of insertion languages which generalizes both the regular languages and the Dyck languages, but is properly contained within the class of context-free languages. Our main result here is that the problem "L = (SIGMA)('*)? " is undecidable for the class of insertion languages. From this result, it follows that the equivalence problem and the problem "Is L regular? " are also undecidable for this class.
Study of relationship between yield and yield Components in garden pea (Pisum sativum L.) by using correlation and path analysis

An experiment was conducted at Horticulture Research Center of Sardar Vallabhbhai Patel University of Agriculture and Technology, Meerut to study the correlation and path analysis of garden pea (Pisum sativum L.) by using 15 diverse genotypes. Correlation and path analysis furnish information regarding the nature and magnitude of various associates and help in measurement of direct effect of one variable on other. It is most important to know the direct and indirect effect on yield component for selecting suitable genotypes for improving the yield. Analyzed data of various parameters revealed that the genotypes, KS-205, KS-216, AP-5, KS-175 and KS-159 and the cross KS-156 x AP-1, KS-175 x AP-1 and KS-156 x Arkel gave promising results among all the genotypes and their crosses in relation to correlation and path analysis of garden pea.
Connectome-based prediction of craving in gambling disorder and cocaine use disorder

Abstract Introduction Craving, involving intense and urgent desires to engage in specific behaviours, is a feature of addictions. Multiple studies implicate regions of salience/limbic networks and basal ganglia, fronto-parietal, medial frontal regions in craving in addictions. However, prior studies have not identified common neural networks that reliably predict craving across substance and behavioural addictions. Methods Functional magnetic resonance imaging during an audiovisual cue-reactivity task and connectome-based predictive modelling (CPM), a data-driven method for generating brain-behavioural models, were used to study individuals with cocaine-use disorder and gambling disorder. Functions of nodes and networks relevant to craving were identified and interpreted based on meta-analytic data. Results Craving was predicted by neural connectivity across disorders. The highest degree nodes were mostly located in the prefrontal cortex. Overall, the prediction model included complex networks including motor/sensory, fronto-parietal, and default-mode networks. The decoding revealed high functional associations with components of memory, valence ratings, physiological responses, and finger movement/motor imagery. Conclusions Craving could be predicted across substance and behavioural addictions. The model may reflect general neural mechanisms of craving despite specificities of individual disorders. Prefrontal regions associated with working memory and autobiographical memory seem important in predicting craving. For further validation, the model should be tested in diverse samples and contexts.
Frequency of in season strength and power training for rugby league

The purpose of this study was to determine the contribution of different in-season strength and power training frequencies to strength and power performance over the course of a 22 week rugby league competition period. Twenty-eight male (n=28) participants, with both high and low strength pre-training status, were divided into three groups following a 15 week pre-season strength and power training programme. A four week periodised in-season strength and power training programme, with intensities ranging from 75-100%, was cycled for the 22 week competition season. Strength and power training was conducted one day.week by the first high pre-training status group (HTFL, n=11), and two day.week by the second high pre-training status group (HTF2, n=9). The low pre-training status group (LTF1, n=8) performed the same strength and power training frequency and programme as HTF1. Training intensity (% 1RM) and volume (sets x repetitions) of in-season strength and power training sessions were standardised for both groups during each training week. Strength, power, and speed data were collected pre-season, and four times during the in-season period. No differences were found between HTF1 and HTF2 in performance variables throughout the 22-week in-season period. Both HTF1 and HTF2 displayed similar significant detraining effects in strength, power, and speed, regardless of in-season training frequency (p<0.05). LTF1 showed no change from pre-season strength and power performance following 22 weeks of the competition period (p<0.05). It was concluded that in-season strength and power training frequency may have a limited role in determining the success of the in-season strength and power training programme in highly trained footballers. The results of the present study suggest a number of factors other than in-season strength and power training frequency may affect in-season strength and power performance and detraining in high strength pre-training status athletes. The effect the start of a competition period has on dynamic athletic performance needs further investigation. TABLE OF CONTENTS CHAPTER ONE