Miracle-dz/UniFAR-FTUs huggingface dataset:
Geometry of Associated Quantum Vector Bundles and the Quantum Gauge Group

In Differential Geometry, it is well--known that given a principal $G$--bundle with a principal connection, for every unitary finite--dimensional linear representation of $G$, one can induce a linear connection and a hermitian structure on associated vector bundles, which are compatible. Furthermore, the gauge group acts on the space of principal connections and on the space of linear connections defined in associated vector bundles. This paper aims to present the non--commutative counterpart of all these classical facts using the theory of quantum bundles and quantum connections.
Understanding and Mitigating Banking Trojans: From Zeus to Emotet

Banking Trojans came a long way in the past decade, and the recent case of Emotet showed their enduring relevance. The evolution of the modern computing landscape can be traced through Emotet and Zeus, both representative examples from the end of the past decade. As an example of earlier malware, Zeus only needed to employ simple anti-analysis techniques to stay undetected; while the more recent Emotet had to constantly evolve to stay a step ahead. Current host-based antimalware solutions face an increasing number of obstacles to perform their function. A multi-layer approach to network security is necessary for network-based intrusion response systems to secure modern networks of heterogeneous devices. A system based on a combination of a graphical network security model and a game theoretic model of cyber attacks was tested on a testbed with Windows machines infected with Trojans; experimental results showed that the proposed system effectively blocked Trojans’ network communications effectively preventing data leakage and yielding encouraging results for future work.
Are We There Yet? A Review on Existing Perceptual Theory and Experiment Support for Visualization Recommendation Systems

—A growing body of research focuses on helping users explore complex datasets faster by automatically suggesting visualization designs of possible interest. However, existing visualization recommendation systems only enumerate, rank, and recommend a small group of visualization designs. Our goal is to understand whether there is enough theoretical and experimental knowledge in current literature to inform visualization recommendation systems to assess the entire visualization design space. Thus, in this paper, we present a literature review comparing and ranking the quality of visualization designs in visual perception and human performance. We structure our review by ﬁrst deﬁning the visualization design space where visualizations must be compared to recommend effective visualization designs. We then perform the review by using a comprehensive schema to record the theoretical and experimental results of visualization comparison, which can also be used to guide the future construction of visualization recommendation systems. To analyze the literature coverage, we develop an interactive tool that can help explore current literature coverage of visualization comparison and identify gaps efﬁciently and effectively. Based on our ﬁndings, we highlight new opportunities and challenges for the community in working towards a comprehensive visualization ranking for informing visualization recommendation systems.
The ALMA Survey of 70 μm Dark High-mass Clumps in Early Stages (ASHES). IV. Star Formation Signatures in G023.477

With a mass of ∼1000 M ⊙ and a surface density of ∼0.5 g cm−2, G023.477+0.114, also known as IRDC 18310-4, is an infrared dark cloud (IRDC) that has the potential to form high-mass stars and has been recognized as a promising prestellar clump candidate. To characterize the early stages of high-mass star formation, we have observed G023.477+0.114 as part of the Atacama Large Millimeter/submillimeter Array (ALMA) Survey of 70 μm Dark High-mass Clumps in Early Stages. We have conducted ∼1. ″2 resolution observations with ALMA at 1.3 mm in dust continuum and molecular line emission. We have identified 11 cores, whose masses range from 1.1 to 19.0 M ⊙. Ignoring magnetic fields, the virial parameters of the cores are below unity, implying that the cores are gravitationally bound. However, when magnetic fields are included, the prestellar cores are close to virial equilibrium, while the protostellar cores remain sub-virialized. Star formation activity has already started in this clump. Four collimated outflows are detected in CO and SiO. H2CO and CH3OH emission coincide with the high-velocity components seen in the CO and SiO emission. The outflows are randomly oriented for the natal filament and the magnetic field. The position-velocity diagrams suggest that episodic mass ejection has already begun even in this very early phase of protostellar formation. The masses of the identified cores are comparable to the expected maximum stellar mass that this IRDC could form (8–19 M ⊙). We explore two possibilities on how IRDC G023.477+0.114 could eventually form high-mass stars in the context of theoretical scenarios.
Multi-party Semi-quantum Secret Sharing Protocol based on Measure-flip and Reflect Operations

The idea of semi-quantum provides a theoretical solution for the shortage of quantum resources and the high cost of quantum devices. This paper proposes a multi-party semi-quantum secret sharing protocol, where the quantum party can share specific messages with the classical parties, and the shared messages can only be recovered by the classical parties working together. By utilizing the $(n+1)$-qubit GHZ states and the operations of measure-flip and reflect, the proposed protocol can use the measured and reflected qubits as the secret keys instead of only the measured qubits in the previous MSQSS protocols. This provides an idea to improve the qubit efficiency when extending semi quantum protocol to the multi-party case. Compared with similar studies, the proposed protocol has merits in quantum resources, qubit efficiency, and shared messages types. Finally, our protocol can withstand most typical external and internal attacks after security analysis.
Tracing the Mott-Hubbard transition in one-dimensional Hubbard models without Umklapp scattering

We apply the density-matrix renormalization group (DMRG) method to a one-dimensional Hubbard model that lacks Umklapp scattering and thus provides an ideal case to study the MottHubbard transition analytically and numerically. The model has a linear dispersion and displays a metal-to-insulator transition when the Hubbard interaction U equals the band width, Uc = W , where the single-particle gap opens linearly, ∆(U ≥ W ) = U − W . The simple nature of the elementary excitations permits to determine numerically with high accuracy the critical interaction strength and the gap function in the thermodynamic limit. The jump discontinuity of the momentum distribution nk at the Fermi wave number kF = 0 cannot be used to locate accurately Uc from finite-size systems. However, the slope of nk at the band edges, kB = ±π, reveals the formation of a single-particle bound state which can be used to determine Uc reliably from nk using accurate finite-size data.
Finetuned Language Models Are Zero-Shot Learners

This paper explores a simple method for improving the zero-shot learning abilities of language models. We show that instruction tuning -- finetuning language models on a collection of tasks described via instructions -- substantially improves zero-shot performance on unseen tasks. We take a 137B parameter pretrained language model and instruction-tune it on over 60 NLP tasks verbalized via natural language instruction templates. We evaluate this instruction-tuned model, which we call FLAN, on unseen task types. FLAN substantially improves the performance of its unmodified counterpart and surpasses zero-shot 175B GPT-3 on 20 of 25 tasks that we evaluate. FLAN even outperforms few-shot GPT-3 by a large margin on ANLI, RTE, BoolQ, AI2-ARC, OpenbookQA, and StoryCloze. Ablation studies reveal that number of finetuning datasets, model scale, and natural language instructions are key to the success of instruction tuning.
Extreme events in transitional turbulence

Transitional localized turbulence in shear flows is known to either decay to an absorbing laminar state or to proliferate via splitting. The average passage times from one state to the other depend super-exponentially on the Reynolds number and lead to a crossing Reynolds number above which proliferation is more likely than decay. In this paper, we apply a rare-event algorithm, Adaptative Multilevel Splitting, to the deterministic Navier–Stokes equations to study transition paths and estimate large passage times in channel flow more efficiently than direct simulations. We establish a connection with extreme value distributions and show that transition between states is mediated by a regime that is self-similar with the Reynolds number. The super-exponential variation of the passage times is linked to the Reynolds number dependence of the parameters of the extreme value distribution. Finally, motivated by instantons from Large Deviation theory, we show that decay or splitting events approach a most-probable pathway. This article is part of the theme issue ‘Mathematical problems in physical fluid dynamics (part 2)’.
LightAutoML: AutoML Solution for a Large Financial Services Ecosystem

We present an AutoML system called LightAutoML developed for a large European financial services company and its ecosystem satisfying the set of idiosyncratic requirements that this ecosystem has for AutoML solutions. Our framework was piloted and deployed in numerous applications and performed at the level of the experienced data scientists while building high-quality ML models significantly faster than these data scientists. We also compare the performance of our system with various general-purpose open source AutoML solutions and show that it performs better for most of the ecosystem and OpenML problems. We also present the lessons that we learned while developing the AutoML system and moving it into production.
Do Prompt-Based Models Really Understand the Meaning of Their Prompts?

Recently, a boom of papers has shown extraordinary progress in zero-shot and few-shot learning with various prompt-based models. It is commonly argued that prompts help models to learn faster in the same way that humans learn faster when provided with task instructions expressed in natural language. In this study, we experiment with over 30 prompts manually written for natural language inference (NLI). We find that models can learn just as fast with many prompts that are intentionally irrelevant or even pathologically misleading as they do with instructively “good” prompts. Further, such patterns hold even for models as large as 175 billion parameters (Brown et al., 2020) as well as the recently proposed instruction-tuned models which are trained on hundreds of prompts (Sanh et al., 2021). That is, instruction-tuned models often produce good predictions with irrelevant and misleading prompts even at zero shots. In sum, notwithstanding prompt-based models’ impressive improvement, we find evidence of serious limitations that question the degree to which such improvement is derived from models understanding task instructions in ways analogous to humans’ use of task instructions.
Fairness, Flexibility and the Far Right: Understanding the Relationship between Populism, Social Spending and Labor Markets

The large increase in economic inequality and the dismantling of the welfare state in Western democracies has been connected to the rise of populist parties. If populist voting is explained by fear and labor market insecurity and if people care more about procedural fairness than inequality of economic outcomes, national income inequality should be less important than other factors in explaining vote shares of populist parties. Using election results from 33 European countries over the 1980-2018 period, two different classifications of populist parties and three different measures of government/welfare state size, we find no relationship between country-level economic inequality, as measured by the disposable income Gini, and either right-wing or left-wing populism. An alternative hypothesis that right-wing populism is dampened by labor market flexibility and social spending is developed and shown to have empirical support.
Financing Intermediate Inputs and Misallocation: Evidence from China’s Firm-Level Data

This paper studies the misallocation of intermediate inputs in China’s firm-level data, eliminating which would increase the average industry’s gross output by 2.93%. We document evidence of pre-pay and financial frictions on intermediate inputs behind the misallocation. We build these frictions into a standard industry dynamics model along with adjustment costs and financial frictions on capital. Counterfactual experiments show that intermediate input frictions, primarily financial frictions, account for 10.8% of China’s gross output misallocation. Value-added gains from reallocating capital, labor, and intermediate inputs are smaller than those obtained when firms are modeled as value-added producers, both in the model and the data.
Company Reporting: Female Board Representation and the Use of Gender-diversity Language

It is generally understood that women are under-represented in the boards of UK firms, with homogeneous boards increasingly perceived as being unfair. An organisation’s true gender diversity values, and the influence that women on boards have upon them, may be discernible from the language used in firms’ annual reports. This study, quantitatively examining data from fifty UK FTSE-listed firms from across ten sectors, finds that there is a moderately positive correlation between the number of women on the boards of firms and the use of gender diverse terminology in those same firms’ annual reports. This study also provides descriptive statistics on the gender composition of executive boards, including the commonly reported Blau diversity index value, and of the gender diversity language used in the sampled reports. The findings support arguments that, as more women enter the boardroom, they positively affect reporting quality, and possibly the firm and its values more widely, and that their contributions are not marginalised. However, if reporting narratives are primarily used as a stakeholder impression management tactic, then the results of this study suggest that women on boards may be complicit in such strategies.
Optimal Taxation and Investment -Specific Technological Change Taxação ótima e progresso tecnológico específico do investimento

In this paper, we look at the relationship between Investment Specific Technological Change (ISTC) and optimal level of labor income progressivity. We develop an incomplete markets overlapping generations model that matches relevant features of the US economy and find that the observed drop in the relative price of investment since the 1980’s leads optimal progressivity to increase. This result hinges on ISTC increasing the wage premium through an increase in the variance of the permanent component of labor income. This result is supported by recent findings in the literature that highlight the increasing role of the permanent component of labor income in the observed increase in income inequality.
Co- and pro-speech integration: The parsing hypothesis*

A growing body of research investigates the semantic and pragmatic contributions of co-speech gestures (gestures coinciding with spoken utterances) and pro-speech gestures (gestures occupying the role of normally spoken constituents). A critical observation about such gestures is that their semantic content is integrated into the meanings of spoken utterances: gestures can perform much of the same kinds of semantic work as speech, including scoping under spoken logical operators. One possible way of effecting this semantic integration is by treating gestures as part of the grammar: gestures are semantically integrated because they appear in syntactic representations (the Grammatical Hypothesis (GH)). However, I argue that GH requires some strange and undesirable stipulations about the nature and function of the grammar, in particular when considering the substantial variety of meaningful content beyond gestures that can be similarly integrated. I propose an alternative to GH, the Parsing Hypothesis (PH), according to which gestures are not a part of the grammar of a language like English; instead, it is the human parser that is responsible for integrating coand pro-speech content. I then provide a toy formal grammar and parser illustrating what a PH-friendly theory of coand pro-speech integration could look like.
NVMExplorer: A Framework for Cross-Stack Comparisons of Embedded Non-Volatile Memories

The current computing landscape is dominated by data-intensive applications, making data movement one of the most prominent performance bottlenecks. With repeated off-chip memory access to DRAM driving up power, and SRAM technology scaling and leakage power limiting the efficiency of embedded memories, there is a need for new memory systems that can enable denser, more energy-efficient future on-chip storage. The actively expanding field of emerging, embeddable non-volatile memory (eNVM) technologies is providing many potential candidates to satisfy this need. However, eNVM cell technologies are in vastly different stages of development and introduce distinct trade-offs in terms of density, read, write, and reliability characteristics.We present NVMExplorer (http://nvmexplorer.seas.harvard.edu/): a cross-stack design space exploration framework to compare and evaluate future on-chip memory solutions with system constraints and application-level impacts in-the-loop. This work uses NVMExplorer to evaluate eNVM-based storage for a range of application and system contexts including machine learning on the edge, graph analytics, and general purpose cache. Additionally, NVMExplorer provides an interactive and easily navigable set of data visualizations, which allow users to quickly answer their specific questions regarding eNVMs, filter according to system and application constraints, and efficiently iterate and refine the design space.
Hypochlorous acid solution is a potent antiviral agent against SARS‐CoV‐2

A novel coronavirus, termed severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) suddenly appeared in Wuhan, China, and has caused pandemic. In this study, we evaluated antiviral activity of purified hypochlorous acid (HClO) against coronaviruses such as SARS‐CoV‐2 and transmissible gastroenteritis virus (TGEV) responsible for pig diseases.
Dual-Camera Super-Resolution with Aligned Attention Modules

We present a novel approach to reference-based super-resolution (RefSR) with the focus on dual-camera super-resolution (DCSR), which utilizes reference images for high-quality and high-fidelity results. Our proposed method generalizes the standard patch-based feature matching with spatial alignment operations. We further explore the dual-camera super-resolution that is one promising application of RefSR, and build a dataset that consists of 146 image pairs from the main and telephoto cameras in a smartphone. To bridge the domain gaps between real-world images and the training images, we propose a self-supervised domain adaptation strategy for real-world images. Extensive experiments on our dataset and a public benchmark demonstrate clear improvement achieved by our method over state of the art in both quantitative evaluation and visual comparisons. Our code and data are available at https://tengfei-wang.github. io/Dual-Camera-SR/index.html.
Entanglement Robustness in Trace Decreasing Quantum Dynamics

Trace decreasing dynamical maps are as physical as trace preserving ones; however, they are much less studied. Here we overview how the quantum Sinkhorn theorem can be successfully applied to find a two-qubit entangled state which has the strongest robustness against local noises and losses of quantum information carriers. We solve a practically relevant problem of finding an optimal initial encoding to distribute entangled polarized qubits through communication lines with polarization dependent losses and extra depolarizing noise. The longest entanglement lifetime is shown to be attainable with a state that is not maximally entangled.Quanta 2021; 10: 15–21.
Complete characterization of multi-channel single photon ionization

Ionization of atoms and molecules by absorption of a light pulse results in electron wavepackets carrying information on the atomic or molecular structure as well as on the dynamics of the ionization process. These wavepackets can be described as a coherent sum of waves of given angular momentum, called partial waves, each characterized by an amplitude and a phase. The complete characterization of the individual angular momentum components is experimentally challenging, requiring the analysis of the interference between partial waves both in energy and angle. Using a two-photon interferometry technique based on extreme ultraviolet attosecond and infrared femtosecond pulses, we characterize the individual partial wave components in the photoionization of the 2p shell in neon. The study of the phases of the angular momentum channels allows us to unravel the influence of short-range, correlation and centrifugal effects. This approach enables the complete reconstruction of photoionization electron wavepackets in time and space, providing insight into the photoionization dynamics.
Lie higher derivations of arbitrary triangular algebras

Motivated by the works of Wang [Y. Wang, Lie (Jordan) derivations of arbitrary triangular algebras, Aequationes Mathematicae, 93 (2019), 1221-1229] and Moafian et al. [F. Moafian and H. R. Ebrahimi Vishki, Lie higher derivations on triangular algebras revisited, Filomat, 30(12) (2016), 3187-3194. ], we shall study Lie higher derivations of arbitrary triangular algebras. In fact, it is shown that every Lie higher derivation on an arbitrary triangular algebra is proper, using the notion of maximal left (right) ring of quotients. 2010 Mathematics Subject Classification: 16W25, 15A78, 16R60
Improved Approximation Schemes for Dominating Set Problems in Unit Disk Graphs

We present two (exponentially) faster PTAS’s for dominating set problems in unit disk graphs. Given a geometric representation of a unit disk graph, our PTAS’s that ﬁnd (1+ (cid:15) )-approximate solutions to the Minimum Dominating Set (MDS) and the Minimum Connected Dominating Set (MCDS) of the input graph in time n O (1 /(cid:15) ) . This can be compared to the best known n O (1 /(cid:15) log1 /(cid:15) ) -time PTAS by Nieberg and Hurink [WAOA’05] for MDS that only uses graph structures and an n O (1 /(cid:15) 2 ) -time PTAS for MCDS by Zhang, Gao, Wu, and Du [J Glob Optim’09]. Our key ingredients are improved dynamic programming algorithms that depend exponentially on more essential 1-dimensional “widths” of the problems.