Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-latency, high-throughput garbage collection

To achieve short pauses, state-of-the-art concurrent copying collectors such as C4, Shenandoah, and ZGC use substantially more CPU cycles and memory than simpler collectors. They suffer from design limitations: i) concurrent copying with inherently expensive read and write barriers, ii) scalability limitations due to tracing, and iii) immediacy limitations for mature objects that impose memory overheads. This paper takes a different approach to optimizing responsiveness and throughput. It uses the insight that regular, brief stop-the-world collections deliver sufficient responsiveness at greater efficiency than concurrent evacuation. It introduces LXR, where stop-the-world collections use reference counting (RC) and judicious copying. RC delivers scalability and immediacy, promptly reclaiming young and mature objects. RC, in a hierarchical Immix heap structure, reclaims most memory without any copying. Occasional concurrent tracing identifies cyclic garbage. LXR introduces: i) RC remembered sets for judicious copying of mature objects; ii) a novel low-overhead write barrier that combines coalescing reference counting, concurrent tracing, and remembered set maintenance; iii) object reclamation while performing a concurrent trace; iv) lazy processing of decrements; and v) novel survival rate triggers that modulate pause durations. LXR combines excellent responsiveness and throughput, improving over production collectors. On the widely-used Lucene search engine in a tight heap, LXR delivers 7.8× better throughput and 10× better 99.99% tail latency than Shenandoah. On 17 diverse modern workloads in a moderate heap, LXR outperforms OpenJDK’s default G1 on throughput by 4% and Shenandoah by 43%.
PyLSE: a pulse-transfer level language for superconductor electronics

Superconductor electronics (SCE) run at hundreds of GHz and consume only a fraction of the dynamic power of CMOS, but are naturally pulse-based, and operate on impulses with picosecond widths. The transiency of these operations necessitates using logic cells that are inherently stateful. Adopting stateful gates, however, implies an entire reconstruction of the design, simulation, and verification stack. Though challenging, this unique opportunity allows us to build a design framework from the ground up using fundamental principles of programming language design. To this end, we propose PyLSE, an embedded pulse-transfer level language for superconductor electronics. We define PyLSE through formal semantics based on transition systems, and build a framework around them to simulate and analyze SCE cells digitally. To demonstrate its features, we verify its results by model checking in UPPAAL, and compare its complexity and timing against a set of cells designed as analog circuit schematics and simulated in Cadence.
Methodological Frames: Paul Bernays, Mathematical Structuralism, and Proof Theory

sphere; in this sphere, the structure of their connection presents itself as an object of pure mathematical thinking and is being investigated with the sole focus on logical relations.38 36 The three aspects of 19thcentury developments he pointed out in his (1930), and which I discussed at the very beginning of this paper, are already present here in (Bernays 1922a). As to the philosophical significance of this new kind of axiomatics, he emphasized that (1) it involves an “Abgehen vom Apriorismus” (95) and (2) mathematics, so understood and developed, is an “allgemeine Formenlehre” (99). 37 The development of Hilbert’s foundational investigations in this critical period between the 1917– 18 lectures and the 1921– 22 lectures is described in (Sieg 1999). All the relevant sources are, of course, available now in (Ewald and Sieg 2013). 38 Here is the German text: “Die räumlichen Verhältnisse werden gleichsam in die Sphäre des MathematischAbstrakten projiziert, in welcher die Struktur ihres Zusammenhanges sich als ein Objekt des rein mathematischen Denkens darstellt und einer Forschungsweise unterzogen wird, die nur auf die logischen Beziehungen gerichtet ist. ” Methodological Frames 377 How is such an investigation to be realized? The structural axiomatic treatment provides the basis for the exclusive focus on logical relations. Any mathematical proof is taken to be “a concrete object all of whose parts can be surveyed; it must be possible, at least in principle, to communicate it [the proof] completely from beginning to end” (97). That a proof does or does not end in a contradiction is “a concretely checkable property. ” At exactly this point, the logical calculus of “Peano, Frege, and Russell” comes in: these three logicians expanded the calculus in such a way “that the thoughtinferences of mathematical proofs can be completely reproduced by symbolic operations” (98). A joint formal development of mathematics and logic is thus ensured, but there is no sense yet of the theoretical means needed for metamathematical investigations. Bernays only writes that, in principle, it is possible to obtain consistency proofs for analysis through “elementary, ostensively certain considerations. ” Hilbert (1922) thought that one would not have to appeal to any principle of induction, thus sidestepping Poincaré’s objection to his earlier syntactic consistency proof. This brief appendix is simply to point out that Bernays, in his first paper on foundational matters, is fully aligned with Hilbert and uses the representation of mathematical proofs in formalisms as a tool for their investigation, not as a way for characterizing mathematics as a formal game.
A Scalable Nyquist Stability Criterion with Application to Power System Small-Signal Stability

—A decentralized stability criterion is derived for a power system with heterogeneous subsystems. A condition for frequency stability and stability of interarea modes is derived using the generalized Nyquist criterion. The resulting scalable Nyquist stability criterion requires only locally available infor- mation and gives a priori stability guarantees for connecting new subsystems to an arbitrarily large network. The method can be applied to a general set of agents. For instance, agents with time-delays, nonminimum phase actuators or even unstable dynamics. The scalable Nyquist criterion makes no distinction between nodes with or without synchronous inertia, making it easy to include converter-interfaced renewable energy in the analysis. The method is validated on a detailed nonlinear power system model with frequency droop provided by hydro governors assisted by wind power.
6G Survey on Challenges, Requirements, Applications, Key Enabling Technologies, Use Cases, AI integration issues and Security aspects

The fifth-generation (5G) network is likely to bring in high data rates, more reliability, and low delays for mobile, personal and local area networks. Alongside the rapid growth of smart wireless sensing and communication technologies, data traffic has significantly risen, and existing 5G networks are not fully capable of supporting future massive data traffic in terms of services, storage, and processing. To meet the forthcoming challenges, the research community is investigating the Terahertz-based sixth-generation (6G) wireless network which is supposed to be offered for industrial usage in around 10 years. This is the right time to explore and learn about various 6G aspects that will play a key role in the successful execution and implementation of 6G networks in the future. This survey provides a review of specifications, requirements, applications, enabling technologies including disruptive and innovative, integration of 6G with advanced architectures and networks like software-defined networks (SDN), network functions virtualization (NFV), cloud/fog computing, etc, artificial intelligence (AI) oriented technologies, privacy and security issues and solutions, and potential futuristic use cases: virtual reality, smart healthcare and Industry 5.0. Furthermore, based on the conducted review, challenges and future research directions are highlighted to aid the deployment of 6G networks.
Assessing the trade-off between prediction accuracy and interpretability for topic modeling on energetic materials corpora

As the amount and variety of energetics research increases, machine aware topic identiﬁcation is necessary to streamline future research pipelines. The makeup of an automatic topic identiﬁcation process consists of creating document representations and performing classiﬁcation. However, the implementation of these processes on energetics research imposes new challenges. Energetics datasets contain many scientiﬁc terms that are necessary to understand the context of a document but may require more complex document representations. Secondly, the predictions from classiﬁcation must be understandable and trusted by the chemists within the pipeline. In this work, we study the trade-oﬀ between prediction accuracy and interpretability by implementing three document embedding methods that vary in computational complexity. With our accuracy results, we also introduce local interpretability model-agnostic explanations (LIME) of each prediction to provide a localized understanding of each prediction and to validate classiﬁer decisions with our team of energetics experts. This study was carried out on a novel labeled energetics dataset created and validated by our team of energetics experts.
Learning Disentangled Representations for Counterfactual Regression via Mutual Information Minimization

Learning individual-level treatment effect is a fundamental problem in causal inference and has received increasing attention in many areas, especially in the user growth area which concerns many internet companies. Recently, disentangled representation learning methods that decompose covariates into three latent factors, including instrumental, confounding and adjustment factors, have witnessed great success in treatment effect estimation. However, it remains an open problem how to learn the underlying disentangled factors precisely. Specifically, previous methods fail to obtain independent disentangled factors, which is a necessary condition for identifying treatment effect. In this paper, we propose Disentangled Representations for Counterfactual Regression via Mutual Information Minimization (MIM-DRCFR), which uses a multi-task learning framework to share information when learning the latent factors and incorporates MI minimization learning criteria to ensure the independence of these factors. Extensive experiments including public benchmarks and real-world industrial user growth datasets demonstrate that our method performs much better than state-of-the-art methods.
Randomization Inference Tests for Shift-Share Designs

We consider the problem of inference in shift-share research designs. The choice between existing approaches that allow for unrestricted spatial correlation involves tradeoffs, varying in terms of their validity when there are relatively few or concentrated shocks, and in terms of the assumptions on the shock assignment process and treatment effects heterogeneity. We propose alternative randomization inference methods that combine the advantages of different approaches. These methods are valid in finite samples under relatively stronger assumptions, while asymptotically valid under weaker assumptions.
Disentangled Generation Network for Enlarged License Plate Recognition and A Unified Dataset

License plate recognition plays a critical role in many practical applications, but license plates of large vehicles are difficult to be recognized due to the factors of low resolution, contamination, low illumination, and occlusion, to name a few. To overcome the above factors, the transportation management department generally introduces the enlarged license plate behind the rear of a vehicle. However, enlarged license plates have high diversity as they are non-standard in position, size, and style. Furthermore, the background regions contain a variety of noisy information which greatly disturbs the recognition of license plate characters. Existing works have not studied this challenging problem. In this work, we first address the enlarged license plate recognition problem and contribute a dataset containing 9342 images, which cover most of the challenges of real scenes. However, the created data are still insufficient to train deep methods of enlarged license plate recognition, and building large-scale training data is very time-consuming and high labor cost. To handle this problem, we propose a novel task-level disentanglement generation framework based on the Disentangled Generation Network (DGNet), which disentangles the generation into the text generation and background generation in an end-to-end manner to effectively ensure diversity and integrity, for robust enlarged license plate recognition. Extensive experiments on the created dataset are conducted, and we demonstrate the effectiveness of the proposed approach in three representative text recognition frameworks.
Dynamical Planckian scaling of charge response at a particle-hole-asymmetric quantum critical point with Kondo destruction

Metallic quantum criticality is a central theme in a variety of strongly correlated systems. Recent experiments have raised the fundamental question of how the charge response can be singular in cases where the Landau framework of quantum criticality allows singularity only in the spin channel. Motivated by this emerging issue, we study the particle-hole-asymmetric regime of a Bose-Fermi Anderson model with power-law forms for both the bosonic bath spectrum and the fermionic band density of states. We realize a particle-hole-asymmetric quantum-critical state where quasiparticles are lost due to a critical destruction of Kondo screening, and demonstrate a dynamical Planckian scaling of the charge response. Implications for a new regime of heavy-fermion quantum criticality and for Mott-Hubbard systems are discussed.
FACM: Correct the Output of Deep Neural Network with Middle Layers Features against Adversarial Samples

—In the strong adversarial attacks against deep neural network (DNN), the output of DNN will be misclassiﬁed if and only if the last feature layer of the DNN is completely destroyed by adversarial samples, while our studies found that the middle feature layers of the DNN can still extract the effective features of the original normal category in these adversarial attacks. To this end, in this paper, a middle F eature layer A nalysis and C onditional M atching prediction distribution (FACM) model is proposed to increase the robustness of the DNN against adversarial samples through correcting the output of DNN with the features extracted by the middle layers of DNN. In particular, the middle F eature layer A nalysis (FA) module, the conditional matching prediction distribution (CMPD) module and the output decision module are included in our FACM model to collaboratively correct the classiﬁcation of adversarial samples. The experiments results show that, our FACM model can signiﬁcantly improve the robustness of the naturally trained model against various attacks, and our FA model can signiﬁcantly improve the robustness of the adversarially trained model against white-box attacks with weak transferability and black box attacks where FA model includes the FA module and the output decision module, not the CMPD module.
A Power-Pool-Based Power Control in Semi-Grant-Free NOMA Transmission

In this paper, we generate a transmit power pool (PP) for Internet of things (IoT) networks with semi-grant-free non-orthogonal multiple access (SGF-NOMA) via multi-agent deep reinforcement learning (MA-DRL) to enable open loop power control (PC). The PP is mapped with each resource block (RB) to achieve distributed power control (DPC). We first formulate the resource allocation problem as stochastic Markov game, and then solve it using two MA-DRL algorithms, namely double deep Q network (DDQN) and Dueling DDQN. Each GF user as an agent tries to find out the optimal transmit power level and RB to form the desired PP. With the aid of dueling processes, the learning process can be enhanced by evaluating the valuable state without considering the effect of each action at each state. Therefore, DDQN is designed for communication scenarios with a small-size action-state space, while Dueling DDQN is for a large-size case. Moreover, to decrease the training time, we reduce the action space by eliminating invalid actions. To control the interference and guarantee the quality-of-service requirements of grant-based users, we determine the optimal number of GF users for each sub-channel. We show that the PC approach has a strong impact on data rates of both grant-based and GF users. We demonstrate that the proposed algorithm is computationally scalable to large-scale IoT networks and produce minimal signalling overhead. Our results show that the proposed MA-Dueling DDQN based SGF-NOMA with DPC outperforms the existing SGF-NOMA system and networks with pure GF protocols with 17.5\% and 22.2\% gain in terms of the system throughput, respectively. Finally, we show that our proposed algorithm outperforms the conventional open loop PC mechanism.
Leveraging Systematic Knowledge of 2D Transformations

The existing deep learning models suﬀer from out-of-distribution ( o.o.d. ) performance drop in computer vision tasks. In comparison, humans have a remarkable ability to interpret images, even if the scenes in the images are rare, thanks to the systematicity of acquired knowledge. This work focuses on 1) the acquisition of systematic knowledge about 2D transformations, and 2) architectural components that can leverage the learned knowledge in image classiﬁcation tasks. With a new training methodology based on synthetic datasets that are constructed under a causal framework, the deep neural networks acquire knowledge from semantically diﬀerent do-mains ( e.g. even from noise), and exhibit certain level of systematicity in parameter estimation experiments. Based on this, a novel architecture is devised consisting of a classifier , an estimator and an identifier (abbreviated as “CED”). By emulating the “hypothesis-veriﬁcation” process in human visual perception, CED improves the classiﬁcation accuracy signiﬁcantly on test sets under covariate shift.
Refining the Cutoff 3d Gravity / $T\bar{T}$ Correspondence

Pure gravity in AdS$_3$ is a theory of boundary excitations, most simply expressed as a constrained free scalar with an improved stress tensor that is needed to match the Brown--Henneaux central charge. Excising a finite part of AdS gives rise to a static gauge Nambu--Goto action for the boundary graviton. We show that this is the $T\bar{T}$ deformation of the infinite volume theory, as the effect of the improvement term on the deformed action can be absorbed into a field redefinition. The classical gravitational stress tensor is reproduced order by order by the $T\bar{T}$ trace equation. We calculate the finite volume energy spectrum in static gauge and find that the trace equation imposes sufficient constraints on the ordering ambiguities to guarantee agreement with the light-cone gauge prediction. The correlation functions, however, are not completely fixed by the trace equation. We show how both the gravitational action and the $T\bar{T}$ deformation allow for finite improvement terms, and we match these to the undetermined total derivative terms in Zamolodchikov's point splitting definition of the $T\bar{T}$ operator.
Pressure-regulated, Feedback-modulated Star Formation in Disk Galaxies

The star formation rate (SFR) in galactic disks depends on both the quantity of the available interstellar medium (ISM) gas and its physical state. Conversely, the ISM’s physical state depends on the SFR, because the “feedback” energy and momentum injected by recently formed massive stars is crucial to offsetting losses from turbulent dissipation and radiative cooling. The ISM’s physical state also responds to the gravitational field that confines it, with increased weight driving higher pressure. In a quasi-steady state, it is expected that the mean total pressure of different thermal phases will match each other, that the component pressures and total pressure will satisfy thermal and dynamical equilibrium requirements, and that the SFR will adjust as needed to provide the requisite stellar radiation and supernova feedback. The pressure-regulated, feedback-modulated (PRFM) theory of the star-forming ISM formalizes these ideas, leading to a prediction that the SFR per unit area, ΣSFR, will scale nearly linearly with ISM weight  . In terms of the large-scale gas surface density Σgas, stellar plus dark matter density ρ sd, and effective ISM velocity dispersion σ eff, an observable weight estimator is ≈PDE=πGΣgas2/2+Σgas(2Gρsd)1/2σeff , and this is predicted to match the total midplane pressure P tot. Using a suite of multiphase magnetohydrodynamic simulations run with the TIGRESS computational framework, we test the principles of the PRFM model and calibrate the total feedback yield ϒtot = P tot/ΣSFR ∼ 1000 km s−1, as well as its components. We compare the results from TIGRESS to theory, previous numerical simulations, and observations, finding excellent agreement.
Self-Supervised Learning of Audio Representations From Audio-Visual Data Using Spatial Alignment

Learning from audio-visual data offers many possibilities to express correspondence between the audio and visual content, similar to the human perception that relates aural and visual information. In this work, we present a method for self-supervised representation learning based on audio-visual spatial alignment (AVSA), a more sophisticated alignment task than the audio-visual correspondence (AVC). In addition to the correspondence, AVSA also learns from the spatial location of acoustic and visual content. Based on 360$^\circ$ video and Ambisonics audio, we propose selection of visual objects using object detection, and beamforming of the audio signal towards the detected objects, attempting to learn the spatial alignment between objects and the sound they produce. We investigate the use of spatial audio features to represent the audio input, and different audio formats: Ambisonics, mono, and stereo. Experimental results show a 10% improvement on AVSA for the first order ambisonics intensity vector (FOA-IV) in comparison with log-mel spectrogram features; the addition of object-oriented crops also brings significant performance increases for the human action recognition downstream task. A number of audio-only downstream tasks are devised for testing the effectiveness of the learnt audio feature representation, obtaining performance comparable to state-of-the-art methods on acoustic scene classification from ambisonic and binaural audio.
A technical note for a Shor's algorithm by phase estimation

The objective of this paper concerns at first the motivation and the method of Shor's algorithm including an excursion into quantum mechanics and quantum computing introducing an algorithmic description of the method. The corner stone of the Shor's algorithm is the modular exponentiation that is the most computational component (in time and space). Second, a linear depth unit based on phase estimation is introduced and a description of a generic version of a modular multiplier based on phases is introduced to build block of a modular exponentiation circuit. Our proposal includes numerical experiments achieved on both the IBM simulator using the Qiskit library and on quantum physical optimizers provided by IBM.