Miracle-dz/UniFAR-FTUs huggingface dataset:
Extending Merge Resolution to a Family of Proof Systems

Merge Resolution ( MRes [4]) is a recently introduced proof system for false QBFs. Unlike other known QBF proof systems, it builds winning strategies for the universal player within the proofs. Every line of this proof system consists of existential clauses along with countermodels. MRes stores the countermodels as merge maps. Merge maps are deterministic branching programs in which isomorphism checking is eﬃcient as a result MRes is a polynomial time veriﬁable proof system. In this paper, we introduce a family of proof systems MRes- R in which, the information of countermodels are stored in any pre-ﬁxed complete representation R , instead of merge maps. Hence corresponding to each possible complete representation R , we have a sound and refutationally complete QBF-proof system in MRes- R . To handle arbitrary representations for the strategies, we introduce consistency checking rules in MRes- R instead of isomorphism checking in MRes . As a result these proof systems are not polynomial time veriﬁable. Consequently, the paper shows that using merge maps is too restrictive and can be replaced with arbitrary representations leading to several interesting proof systems. The paper also studies proof theoretic properties of the family of new proof systems MRes- R . We show that eFrege+ ∀ red simulates all valid refutations from proof systems in MRes- R . Since proof systems in MRes- R may use arbitrary representations, in order to simulate them, we ﬁrst represent the steps used by the proof systems as a new simple complete structure. As a consequence, the corresponding proof system belonging to MRes- R is able to simulate all proof systems in MRes- R . Finally, we simulate this proof system via eFrege+ ∀ red using the ideas from [10]. On the lower bound side, we lift the lower bound result of regular MRes ([5]) for all regular proof systems in MRes- R . To be precise, we show that the completion principle formulas from [17] which are shown to be hard for regular MRes in [5], are also hard for any regular proof system in MRes- R . Thereby, the paper lifts the lower bound of regular MRes to an entire class of proof systems, which use some complete representation, including those undiscovered, instead of merge maps.
Stepsize Variations for Lyapunov Exponents to Counter Persistent Errors

Lyapunov exponents are important quantities in the analysis of complex dynamical systems. Usually, they are computed with Benettin's algorithm, which propagates linear perturbations along a background trajectory. While many applications trust in convergence of the algorithm, they often do not consider the effects of numerical errors. In fact, numerical errors tend to accumulate in the averaging process of Benettin's algorithm when using constant or adaptive stepsizes and lead to limits different from the Lyapunov exponents. In this article, we demonstrate how local integration errors can affect the convergence of Benettin's algorithm and suggest rules for stepsizes to counter persistent errors. Our examples demonstrate that stepsize variations can improve the accuracy of Benettin's algorithm. More rigorous results are obtained in a theoretical analysis, in which we prove convergence of Benettin's algorithm for linear systems.
ACGNet: Action Complement Graph Network for Weakly-supervised Temporal Action Localization

Weakly-supervised temporal action localization (WTAL) in untrimmed videos has emerged as a practical but challenging task since only video-level labels are available. Existing approaches typically leverage off-the-shelf segment-level features, which suffer from spatial incompleteness and temporal incoherence, thus limiting their performance. In this paper, we tackle this problem from a new perspective by enhancing segment-level representations with a simple yet effective graph convolutional network, namely action complement graph network (ACGNet). It facilitates the current video segment to perceive spatial-temporal dependencies from others that potentially convey complementary clues, implicitly mitigating the negative effects caused by the two issues above. By this means, the segment-level features are more discriminative and robust to spatial-temporal variations, contributing to higher localization accuracies. More importantly, the proposed ACGNet works as a universal module that can be flexibly plugged into different WTAL frameworks, while maintaining the end-to-end training fashion. Extensive experiments are conducted on the THUMOS'14 and ActivityNet1.2 benchmarks, where the state-of-the-art results clearly demonstrate the superiority of the proposed approach.
BlockReduce - Scaling Proof-of-Work Blockchains

—This paper introduces BlockReduce, a Proof-of-Work (PoW) based blockchain system which achieves high transaction throughput through a hierarchy of merged mined blockchains, each operating in parallel on a partition the overall application state. Most notably, the full PoW available within the network is applied to all blockchains in BlockReduce, and cross-blockchain state transitions are enabled seamlessly within the core protocol. This paper shows that, given a hierarchy of blockchains and its associated security model, the protocol scales superlinearly in transaction throughput with the number of blockchains operated by the protocol.
A New Gauge for Asymptotically Flat Spacetime

We present a new gauge for asymptotically ﬂat spacetime that can treat future and past null inﬁnities ( I + or I − ) democratically. Our gauge is complementary to Bondi and Ashtekar-Hansen gauges, and is adapted to the S -matrix being the natural observable. One new feature is that the holographic directions are null. We present a set of consistent fall-oﬀs in terms of null coordinates at I + and I − , with ﬁnite BMS ± charges. The diagonal BMS 0 symmetry of the gravitational S -matrix emerges upon demanding asymptotic CPT invariance. Trivial diﬀeomorphisms, (absence of) log fall-oﬀs, possible enhancements of BMS algebra, and the possibility of holographic renormalization of data at I + − and I − + , play interesting roles. Gory details of the various new technical features that emerge, are elaborated in a companion paper to this letter.
Dust-induced beam losses in the cryogenic arcs of the CERN Large Hadron Collider

The interaction of dust particles with the LHC proton beams accounts for a major fraction of irregular beam loss events observed in LHC physics operation. The events cease after a few beam revolutions because of the expulsion of dust particles from the beam once they become ionized in the transverse beam tails. Despite the transient nature of these events, the resulting beam losses can trigger beam aborts or provoke quenches of superconducting magnets. In this paper, we study the characteristics of beam-dust particle interactions in the cryogenic arcs by reconstructing key observables like nuclear collision rates, loss durations and integral losses per event. The study is based on events recorded during 6.5 TeV operation with stored beam intensities of up to ∼ 3× 10 protons per beam. We show that inelastic collision rates can reach almost 10 collisions per second, resulting in a loss of up to ∼ 1.6×10 protons per event. We demonstrate that the experimental distributions and their dependence on beam parameters can be described quantitatively by a previously developed simulation model if dust particles are assumed to be attracted by the beam. The latter finding is consistent with recent time profile studies and yields further evidence that dust particles carry a negative charge when entering the beam. We also develop different hypotheses regarding the absence of higher-loss events in the measurements, although such events are theoretically not excluded by the simulation model. The results provide grounds for predicting dust-induced beam losses in presence of higher-intensity beams in future runs of the High-Luminosity LHC.
On models of Cosmology

In this work the most promising string cosmology models are re-viewed model by model in detail. These models are based on conceptsform string theory and heterotic M theory to construct possible scenar-ios for the beginning and the evolution of the universe. In all models asin standard cosmology the problem of particle-antiparticle asymmetryarises, the issue is that all models predict equal number of matter andantimatter to be present in the universe, this contradicts the observa-tions that the number of antiparticle is much less than the number ofparticles. In order to solve the asymmetry dilemma of particles andantiparticles, it is proposed that particles and antiparticles were dis-tributed in the brane and antibrane, respectively, immediately afterthe collision. Implications of these scenarios are discussed in detail.
An imprecise-probabilistic characterization of frequentist statistical inference

Between the two dominant schools of thought in statistics, namely, Bayesian and classical/frequentist, a main difference is that the former is grounded in the mathematically rigorous theory of probability while the latter is not. In this paper, I show that the latter is grounded in a different but equally mathematically rigorous theory of imprecise probability. Specifically, I show that for every suitable testing or confidence procedure with error rate control guarantees, there exists a consonant plausibility function whose derived testing or confidence procedure is no less efficient. Beyond its foundational implications, this characterization has at least two important practical consequences: first, it simplifies the interpretation of p-values and confidence regions, thus creating opportunities for improved education and scientific communication; second, the constructive proof of the main results leads to a strategy for new and improved methods in challenging inference problems.
Sensitivities on nonspinning and spinning primordial black hole dark matter with global 21-cm troughs

Detection of the global 21 cm signal arising from neutral hydrogen can revolutionize our understanding of the standard evolution of the universe after recombination. In addition, it can also be an excellent probe of Dark Matter (DM). Among all the DM candidates, Primordial Black Holes (PBHs) are one of the most well-motivated. Hawking emission from low-mass PBHs can have sub-stantial eﬀect on the thermal and ionization history of the early universe, and that in turn can have an imprint on the global 21 cm signal. Recently EDGES has claimed a global 21 cm signal, though SARAS 3 has rejected that claim. In this work, we investigate the sensitivities on non-spinning and spinning PBHs arising from an EDGES-like measurement of the global 21 cm signal, and ﬁnd that the sensitivities will be competitive with those arising from other astrophysical observables. We show that the sensitivities can be signiﬁcantly strengthened depending on various uncertain astrophysical parameters. Besides, we also derive projections on the PBH density from the absorption trough expected during the Dark Ages. Our work shows that the near future unambiguous detection of the global 21 cm absorption troughs can be an excellent probe of PBH DM.
A time-stepping coupled finite element-state space model for induction motor drives. II. Machine performance computation and verification

In a companion paper, a time-stepping coupled finite element state-space algorithm for the modeling of induction motor drives was developed. The model formulation and algorithm allows one to rigorously model the effects of space harmonics caused by magnetic circuit nonlinearities, topology and winding layouts, as well as their interaction with time harmonics caused by the inverter operation. In this paper, the model is used in the computation of induction motor performance with emphasis on the comparison between sinusoidal and inverter operations, with regard to effects on ohmic and core losses, as well as torque ripples and drive input/output power relationships. This includes harmonic contents of various currents, flux linkages and voltages, etc. The model allows for future study of failure modes such as bar and end-ring breakages in the squirrel-cages of such machines.
Processing sequence for non-destructive inspection based on 3D terahertz images

In this paper we present an innovative data and image processing sequence to perform non-destructive inspection from 3D terahertz (THz) images. We develop all the steps starting from a 3D tomographic reconstruction of a sample from its radiographs acquired with a monochromatic millimetre wave imaging system. Thus an automated segmentation provides the different volumes of interest (VOI) composing the sample. Then a 3D visualization and dimensional measurements are performed on these VOI, separately, in order to provide an accurate nondestructive testing (NDT) of the studied sample. This sequence is implemented onto an unique software and validated through the analysis of different objects
Coarse graining in effective theories of lattice QCD in 1+1d and 2+1d

In the strong coupling and heavy quark mass regime, lattice QCD dimensionally reduces to effective theories of Polyakov loops depending on the parameters of the original Wilson action $\beta, \kappa$ and $N_\tau$. We apply coarse graining techniques to such theories in 1d and 2d, corresponding to lattice QCD at finite temperature and non-zero chemical potential in 1+1d and 2+1d, respectively. In 1d the method is applied to the effective theories up to $\mathcal{O}(\kappa^4)$. Using the transfer matrix, the recursion relations are solved analytically. The thermodynamic limit is taken for some observables. Afterwards, continuum extrapolation is performed numerically and results are discussed. In 2d the coarse graining method is applied in the pure gauge and static quark limit. Running couplings are obtained and the fixed points of the transformations are discussed. Finally, the critical coupling of the deconfinement transition is determined in both limits. Agreement to about 12% with Monte Carlo results of 2+1d Yang-Mills theory from the literature is observed.
A Scalable Deep Reinforcement Learning Model for Online Scheduling Coflows of Multi-Stage Jobs for High Performance Computing

Coflow is a recently proposed networking abstraction to help improve the communication performance of data-parallel computing jobs. In multi-stage jobs, each job consists of multiple coflows and is represented by a Directed Acyclic Graph (DAG). Efficiently scheduling coflows is critical to improve the data-parallel computing performance in data centers. Compared with hand-tuned scheduling heuristics, existing work DeepWeave [1] utilizes Reinforcement Learning (RL) framework to generate highly-efficient coflow scheduling policies automatically. It employs a graph neural network (GNN) to encode the job information in a set of embedding vectors, and feeds a flat embedding vector containing the whole job information to the policy network. However, this method has poor scalability as it is unable to cope with jobs represented by DAGs of arbitrary sizes and shapes, which requires a large policy network for processing a high-dimensional embedding vector that is difficult to train. In this paper, we first utilize a directed acyclic graph neural network (DAGNN) to process the input and propose a novel Pipelined-DAGNN, which can effectively speed up the feature extraction process of the DAGNN. Next, we feed the embedding sequence composed of schedulable coflows instead of a flat embedding of all coflows to the policy network, and output a priority sequence, which makes the size of the policy network depend on only the dimension of features instead of the product of dimension and number of nodes in the job’s DAG. Furthermore, to improve the accuracy of the priority scheduling policy, we incorporate the Self-Attention Mechanism into a deep RL model to capture the interaction between different parts of the embedding sequence to make the output priority scores relevant. Based on this model, we then develop a coflow scheduling algorithm for online multi-stage jobs. Our simulation results are based on the real trace of Facebook. Compared with a state-of-the-art approach, our model can shorten the average weighted job completion time by up to 40.42% and complete jobs at least 1.68 times faster. It also has better scalability and robustness.
Prospects for differentiating extended coronal geometries in AGNs with the IXPE mission

X-ray polarimetry can potentially constrain the unknown geometrical shape of AGN coronae. We present simulations of the X-ray polarization signal expected from AGN coronae, assuming three different geometries, namely slab, spherical and conical. We use the fully relativistic Monte-Carlo Comptonization code monk to compute the X-ray polarization degree and angle. We explore different coronal parameters such as shape, size, location and optical depth. Different coronal geometries give a significantly different X-ray polarization signal. A slab corona yields a high polarization degree, up to 14% depending on the viewing inclination; a spherical corona yields low values, about 1–3%, while a conical corona yields intermediate values. We also find a difference of 90 degrees in polarization angle between the slab corona and the spherical or conical coronae. Upcoming X-ray polarimetry missions like IXPE will allow us to observationally distinguish among different coronal geometries in AGNs for the first time.
iSegFormer: Interactive Image Segmentation with Transformers

In this brief report, we introduce iSegFormer, a novel transformer-based approach for interactive image segmentation. iSegFormer is built upon existing segmentation transformers with user clicks as an additional input, al-lowing users to interactively and iteratively reﬁne the segmentation mask. We evaluate iSegFormer on several public benchmarks, including both natural and medical images. Evaluation results show that iSegFormer achieves decent performance comparing with state-of-the-art on natural images and generalizes very well to medical images without ﬁne tuning. The results in the report serve as a proof of concept that transformers can be easily applied to interactive segmentation tasks. Code is available at https:// github.com/qinliuliuqin/iSegFormer.git .
Adding semantics to measurements: Ontology-guided, systematic performance analysis

The design and operation of modern software systems exhibit a shift towards virtualization, containerization and service-based orchestration. Performance capacity engineering and resource utilization tuning become priority requirements in such environments. Measurement-based performance evaluation is the cornerstone of capacity engineering and designing for performance. Moreover, the increasing complexity of systems necessitates rigorous performance analysis approaches. However, empirical performance analysis lacks sophisticated model-based support similar to the functional design of the system. The paper proposes an ontology-based approach for facilitating and guiding the empirical evaluation throughout its various steps. Hyperledger Fabric (HLF), an open-source blockchain platform by the Linux Foundation, is modelled and evaluated as a pilot example of the approach, using the standard TPC-C performance benchmark workload.
A novel variable optical reflection standard to implement 16-term optical calibration

Commercial lightwave analysers use advanced normalisation techniques to implement optical calibration. As integrated optical components gain wider usage, leakage effects cannot be neglected, and hence there will be an increasing need to perform 16-term calibration. One of the main constraints at present to implement optical 16-term or even 12-term calibration is the lack of enough optical reflection standards. Given the bandwidth limitation of the line standard, it is desirable to have at least three optical reflection standards in addition to the thru standard. In this work, a variable optical reflection standard which makes use of bulk optics is proposed. The structure consists of a fibre pigtail, a lens and a mirror all separated by air. The variation in optical reflection is produced by varying the distance between the lens and the other two elements.
Role of external knowledge flows in cluster upgrading: an empirical analysis of the Mirandola biomedical district in Italy

The paper analyses the role of external knowledge flows in the upgrading of the biomedical district of Mirandola, Emilia-Romagna region, in Italy. The district produces two types of products, namely disposables and electromedical machines, and the paper considers this second production type. The Mirandola district has been able to maintain a relatively good competitive position. The paper shows that while all firms in this sector in this region tend to have fairly good performance in terms of size and revenue growth, a significant difference exists in terms of innovation performance. Firms in the Mirandola district produce more patents and more scientific publications. From a methodological point of view, negative binomial regressions are made on the determinants of patenting and publishing activity by firms. Two major factors seem to explain the superior performance in terms of innovation. First, the significant role of the leader firms in the district. Second, linkages external to the district, namely relationships with research centres (universities in particular) located not only in the region but also in the rest of the country and abroad. The importance of external relations and institutional diversity (in terms of variety of institutions collaborating in scientific publications), appear to grow over time.
Before and after GDPR: tracking in mobile apps

Third-party tracking, the collection and sharing of behavioural data about individuals, is a significant and ubiquitous privacy threat in mobile apps. The EU General Data Protection Regulation (GDPR) was introduced in 2018 to protect personal data better, but there exists, thus far, limited empirical evidence about its efficacy. This paper studies tracking in nearly two million Android apps from before and after the introduction of the GDPR. Our analysis suggests that there has been limited change in the presence of third-party tracking in apps, and that the concentration of tracking capabilities among a few large gatekeeper companies persists. However, change might be imminent.
Lyric document embeddings for music tagging

We present an empirical study on embedding the lyrics of a song into a fixed-dimensional feature for the purpose of music tagging. Five methods of computing token-level and four methods of computing document-level representations are trained on an industrial-scale dataset of tens of millions of songs. We compare simple averaging of pretrained embeddings to modern recurrent and attention-based neural architectures. Evaluating on a wide range of tagging tasks such as genre classification, explicit content identification and era detection, we find that averaging word embeddings outperform more complex architectures in many downstream metrics.