Miracle-dz/UniFAR-FTUs huggingface dataset:
PhoMT: A High-Quality and Large-Scale Benchmark Dataset for Vietnamese-English Machine Translation

We introduce a high-quality and large-scale Vietnamese-English parallel dataset of 3.02M sentence pairs, which is 2.9M pairs larger than the benchmark Vietnamese-English machine translation corpus IWSLT15. We conduct experiments comparing strong neural baselines and well-known automatic translation engines on our dataset and find that in both automatic and human evaluations: the best performance is obtained by fine-tuning the pre-trained sequence-to-sequence denoising auto-encoder mBART. To our best knowledge, this is the first large-scale Vietnamese-English machine translation study. We hope our publicly available dataset and study can serve as a starting point for future research and applications on Vietnamese-English machine translation. We release our dataset at: https://github.com/VinAIResearch/PhoMT
Interactive rank testing by betting

In order to test if a treatment is perceptibly diﬀerent from a placebo in a randomized experiment with covariates, classical nonparametric tests based on ranks of observations/residuals have been employed (eg: by Rosenbaum), with ﬁnite-sample valid inference enabled via permutations. This paper proposes a diﬀerent principle on which to base inference: if — with access to all covariates and outcomes, but without access to any treatment assignments — one can form a ranking of the subjects that is suﬃciently nonrandom (eg: mostly treated followed by mostly control), then we can conﬁdently conclude that there must be a treatment eﬀect. Based on a more nuanced, quantiﬁable, version of this principle, we design an interactive test called i-bet: the analyst forms a single permutation of the subjects one element at a time, and at each step the analyst bets toy money on whether that subject was actually treated or not, and learns the truth immediately after. The wealth process forms a real-valued measure of evidence against the global causal null, and we may reject the null at level α if the wealth ever crosses 1 /α . Apart from providing a fresh “game-theoretic” principle on which to base the causal conclusion, the i-bet has other statistical and computational beneﬁts, for example (A) allowing a human to adaptively design the test statistic based on increasing amounts of data being revealed (along with any working causal models and prior knowledge), and (B) not requiring permutation resampling, instead noting that under the null, the wealth forms a nonnegative martingale, and the type-1 error control of the aforementioned decision rule follows from a tight inequality by Ville. Further, if the null is not rejected, new subjects can later be added and the test can be simply continued, without any corrections (unlike with permutation p-values). Numerical experiments demonstrate good power under various heterogeneous treatment eﬀects. We ﬁrst describe i-bet test for two-sample comparisons with unpaired data, and then adapt it to paired data, multi-sample comparison, and sequential settings; these may be viewed as interactive martingale variants of the Wilcoxon, Kruskal-Wallis, and Friedman tests.
Trapping Majorana zero modes in vortices of magnetic texture crystals coupled to nodal superconductors

We propose a mechanism for opening a full bulk energy gap and inducing vortex Majorana zero modes (MZMs) in nodal superconductors (SCs). We show that this becomes possible by coupling the nodal SC of interest to a magnetic texture crystal. The latter consists of superpositions of magnetic textures which repeat periodically in space according to suitable wave vectors that enable spin-ﬂip scattering between all pairs of nodes of the SC, and thus open a full gap in its bulk energy spectrum. In this event, MZMs can be trapped in spin or shift vortices introduced in the magnetic texture crystal. Our approach is generic and applies to nodal SCs of spin-singlet, -triplet, or -mixed type of pairing. Therefore it promises to ﬁnd application in a variety of nodal SCs, where the magnetic textures appear either spontaneously due to electron-electron interactions or are imposed by nanomagnets or become induced by coupling to a lattice of localized magnetic moments.
Hierarchical Few-Shot Generative Models

A few-shot generative model should be able to generate data from a distribution by only observing a limited set of examples. In few-shot learning the model is trained on data from many sets from different distributions sharing some underlying properties such as sets of characters from different alphabets or sets of images of different type objects. We extend current latent variable models for sets to a fully hierarchical approach with an attention-based point to set-level aggregation and call our approach SCHA-VAE for Set-Context-Hierarchical-Aggregation Variational Autoencoder. We explore iterative data sampling, likelihood-based model comparison, and adaptation-free out of distribution generalization. Our results show that the hierarchical formulation better captures the intrinsic variability within the sets in the small data regime. With this work we generalize deep latent variable approaches to few-shot learning, taking a step towards large-scale few-shot generation with a formulation that readily can work with current state-of-the-art deep generative models.
Parallel Peeling of Bipartite Networks for Hierarchical Dense Subgraph Discovery

Wing and Tip decomposition are motif-based analytics for bipartite graphs that construct a hierarchy of butterfly (2,2-biclique) dense edge and vertex induced subgraphs, respectively. They have applications in several domains, including e-commerce, recommendation systems, document analysis, and others. Existing decomposition algorithms use a bottom-up approach that constructs the hierarchy in an increasing order of the subgraph density. They iteratively select the edges or vertices with minimum butterfly count peel, i.e., remove them along with their butterflies. The amount of butterflies in real-world bipartite graphs makes bottom-up peeling computationally demanding. Furthermore, the strict order of peeling entities results in a large number of sequentially dependent iterations. Consequently, parallel algorithms based on bottom up peeling incur heavy synchronization and poor scalability. In this article, we propose a novel Parallel Bipartite Network peelinG (PBNG) framework that adopts a two-phased peeling approach to relax the order of peeling, and in turn, dramatically reduce synchronization. The first phase divides the decomposition hierarchy into few partitions and requires little synchronization. The second phase concurrently processes all partitions to generate individual levels of the hierarchy and requires no global synchronization. The two-phased peeling further enables batching optimizations that dramatically improve the computational efficiency of PBNG. We empirically evaluate PBNG using several real-world bipartite graphs and demonstrate radical improvements over the existing approaches. On a shared-memory 36 core server, PBNG achieves up to 19.7× self-relative parallel speedup. Compared to the state-of-the-art parallel framework ParButterfly, PBNG reduces synchronization by up to 15,260× and execution time by up to 295×. Furthermore, it achieves up to 38.5× speedup over state-of-the-art algorithms specifically tuned for wing decomposition. Our source code is made available at https://github.com/kartiklakhotia/RECEIPT.
Analyzing the South China Sea Fishing Dispute as a Complex Game: Efficient Sample Allocation via a Response Surface Methodology

The South China Sea (SCS) is one of the most economically valuable natural resources on the planet, and as such has become a source of territorial disputes between its bordering nations. Among other things, states compete to harvest the multitude of fish species in the SCS. In an effort to gain a competitive advantage states have turned to increased maritime patrols, as well as the use of “maritime militias,” which are fishermen armed with martial assets to resist the influence of patrols. This conflict suggests a game of strategic resource allocation where states allocate patrols intelligently to earn the greatest possible rent. The game, however, is quite computationally challenging when considering its size (there are several distinct fisheries in the SCS), the nonlinear nature of biomass growth, and the influence of patrol allocations on costs imposed on fishermen. Further, uncertainty in player behavior attributed to modeling error requires a robust analysis to fully capture the dispute’s dynamics. To model such a complex scenario, this paper employs a response surface methodology (RSM) to assess optimal patrolling strategies and their impact on realized rents. The RSM is benchmarked against two simpler sampling strategies and is found to vastly outperform across a variety of instantiations of the model parameters.
A repeated measures approach to pooled and calibrated biomarker data

Participant‐level meta‐analysis across multiple studies increases the sample size for pooled analyses, thereby improving precision in effect estimates and enabling subgroup analyses. For analyses involving biomarker measurements as an exposure of interest, investigators must first calibrate the data to address measurement variability arising from usage of different laboratories and/or assays. In practice, the calibration process involves reassaying a random subset of biospecimens from each study at a central laboratory and fitting models that relate the study‐specific “local” and central laboratory measurements. Previous work in this area treats the calibration process from the perspective of measurement error techniques and imputes the estimated central laboratory value among individuals with only a local laboratory measurement. In this work, we propose a repeated measures method to calibrate biomarker measurements pooled from multiple studies with study‐specific calibration subsets. We account for correlation between measurements made on the same person and between measurements made at the same laboratory. We demonstrate that the repeated measures approach provides valid inference, and compare it to existing calibration approaches grounded in measurement error techniques in an example describing the association between circulating vitamin D and stroke.
A rank-adaptive higher-order orthogonal iteration algorithm for truncated Tucker decomposition

We propose a novel rank-adaptive higher-order orthogonal iteration (HOOI) algorithm to compute the truncated Tucker decomposition of higher-order tensors with a given error tolerance, and prove that the method is locally optimal and monotonically convergent. A series of numerical experiments related to both synthetic and real-world tensors are carried out to show that the proposed rank-adaptive HOOI algorithm is advantageous in terms of both accuracy and efficiency. Some further analysis on the HOOI algorithm and the classical alternating least squares method are presented to further understand why rank adaptivity can be introduced into the HOOI algorithm and how it works.
Multi-armed Bandit Algorithm against Strategic Replication

We consider a multi-armed bandit problem in which a set of arms is registered by each agent, and the agent receives reward when its arm is selected. An agent might strategically submit more arms with replications, which can bring more reward by abusing the bandit algorithm's exploration-exploitation balance. Our analysis reveals that a standard algorithm indeed fails at preventing replication and suffers from linear regret in time $T$. We aim to design a bandit algorithm which demotivates replications and also achieves a small cumulative regret. We devise Hierarchical UCB (H-UCB) of replication-proof, which has $O(\ln T)$-regret under any equilibrium. We further propose Robust Hierarchical UCB (RH-UCB) which has a sublinear regret even in a realistic scenario with irrational agents replicating careless. We verify our theoretical findings through numerical experiments.
Charged and CP-violating Kink Solutions in the Two Higgs Doublet Model

Minimal extensions of the Standard Model (SM), such as the so-called Two Higgs Doublet Model (2HDM), can possess several accidental discrete symmetries whose spontaneous breakdown in the early universe usually lead to the formation of domain walls. We extend an earlier work [33] on this topic by studying in more detail the analytic properties of electrically charged and CP-violating kink solutions in the Z2symmetric 2HDM. We derive the complete set of equations of motion that describe the 1D spatial profile of both the 2HDM vacuum parameters and the would-be Goldstone bosons G1,2,3 of the SM. These equations are then solved numerically using the gradient flow technique, and the results of our analysis are presented in different parametrizations of the Higgs doublets. In particular, we show analytically how an electrically charged profile should arise in 1D kink solutions when asymmetric boundary conditions are imposed on the Goldstone mode G2 at spatial infinities, i.e. as x→ ±∞. If asymmetric boundary conditions are selected at x→ ±∞ for the Goldstone mode G3 or the longitudinal mode θ corresponding to a would-be massive photon, the derived kink solutions are then shown to exhibit CP violation. Possible cosmological implications of the electrically charged and CP-violating domain walls in the 2HDM are discussed.
Towards Blockchain-enabled Open Architectures for Scalable Digital Asset Platforms

Today there is considerable interest in deploying blockchains and decentralized ledger technology as a means to address the deficiencies of current financial and digital asset infrastructures. The focal point of attention in many projects on digital asset and cryptocurrency is centered around blockchain systems and smart contracts. Many projects seek to make the blockchain as the centerpiece of the new decentralized world of finance. However, several roadblocks and challenges currently face this predominant blockchain-centric view. In this paper we argue that the proper and correct perspective on decentralized economy should be one that is asset-centric, where the goal should be the consistent lifecycle management of assets in the real-world with their digital representation on the blockchain. We introduce the notion of the digital twin to capture the relationship between a real-world asset and its on-chain representation. A digital twin container is utilized to permit off-chain state persistence and on-chain state traceability, where the container can be deployed on the blockchain as well as on traditional application servers. The digital twin container becomes the bridge between legacy infrastructures and the newly emergent blockchain infrastructures, permitting legacy systems to interoperate consistently with blockchain systems. We believe this asset-centric view to be the correct evolutionary direction for the nascent field of blockchains and decentralized ledger technology.
Where were my keys? - Aggregating Spatial-Temporal Instances of Objects for Efficient Retrieval over Long Periods of Time

Robots equipped with situational awareness can help humans efficiently find their lost objects by leveraging spatial and temporal structure. Existing approaches to video and image retrieval do not take into account the unique constraints imposed by a moving camera with a partial view of the environment. We present a Detection-based 3-level hierarchical Association approach, D3A, to create an efficient query-able spatial-temporal representation of unique object instances in an environment. D3A performs online incremental and hierarchical learning to identify keyframes that best represent the unique objects in the environment. These keyframes are learned based on both spatial and temporal features and once identified their corresponding spatial-temporal information is organized in a key-value database. D3A allows for a variety of query patterns such as querying for objects with/without the following: 1) specific attributes, 2) spatial relationships with other objects, and 3) time slices. For a given set of 150 queries, D3A returns a small set of candidate keyframes (which occupy only 0.17% of the total sensory data) with 81.98\% mean accuracy in 11.7 ms. This is 47x faster and 33% more accurate than a baseline that naively stores the object matches (detections) in the database without associating spatial-temporal information.
Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation

Recently, using automatic configuration tuning to improve the performance of modern database management systems (DBMSs) has attracted increasing interest from the database community. This is embodied with a number of systems featuring advanced tuning capabilities being developed. However, it remains a challenge to select the best solution for database configuration tuning, considering the large body of algorithm choices. In addition, beyond the applications on database systems, we could find more potential algorithms designed for configuration tuning. To this end, this paper provides a comprehensive evaluation of configuration tuning techniques from a broader perspective, hoping to better benefit the database community. In particular, we summarize three key modules of database configuration tuning systems and conduct extensive ablation studies using various challenging cases. Our evaluation demonstrates that the hyper-parameter optimization algorithms can be borrowed to further enhance the database configuration tuning. Moreover, we identify the best algorithm choices for different modules. Beyond the comprehensive evaluations, we offer an efficient and unified database configuration tuning benchmark via surrogates that reduces the evaluation cost to a minimum, allowing for extensive runs and analysis of new techniques.
Off-Policy Evaluation in Partially Observed Markov Decision Processes

We consider off-policy evaluation of dynamic treatment rules under sequential ignorability, given an assumption that the underlying system can be modeled as a partially observed Markov decision process (POMDP). We propose an estimator, partial history importance weighting, and show that it can consistently estimate the stationary mean rewards of a target policy given long enough draws from the behavior policy. We provide an upper bound on its error that decays polynomially in the number of observations (i.e., the number of trajectories times their length), with an exponent that depends on the overlap of the target and behavior policies, and on the mixing time of the underlying system. Furthermore, we show that this rate of convergence is minimax given only our assumptions on mixing and overlap. Our results establish that off-policy evaluation in POMDPs is strictly harder than off-policy evaluation in (fully observed) Markov decision processes, but strictly easier than model-free off-policy evaluation.
Impacts of inverse magnetic catalysis on screening masses of neutral pions and sigma mesons in hot and magnetized quark matter

We investigate the screening masses of neutral pions and sigma mesons in hot and magnetized quark matter in the framework of a two-flavor lattice-improved Nambu–Jona-Lasinio (NJL) model with a magnetic field dependent coupling constant, which is determined by utilizing the results from lattice QCD simulations. Since such model can well reproduce inverse magnetic catalysis (IMC), by comparing with the standard NJL model, we systemically analyze the impacts of IMC on the temperature and magnetic field dependences of the longitudinal and transverse screening masses of the chiral partners, i.e. π and σ mesons, as well as the screening mass differences between them. Particularly, it is found that the eB dependences of two alternative (pseudo)critical temperatures for the chiral transition defined by σ−π meson screening mass differences are consistent with that defined by the quark condensate.
TODSum: Task-Oriented Dialogue Summarization with State Tracking

Previous dialogue summarization datasets mainly focus on open-domain chitchat dialogues, while summarization datasets for the broadly used task-oriented dialogue haven't been explored yet. Automatically summarizing such task-oriented dialogues can help a business collect and review needs to improve the service. Besides, previous datasets pay more attention to generate good summaries with higher ROUGE scores, but they hardly understand the structured information of dialogues and ignore the factuality of summaries. In this paper, we introduce a large-scale public Task-Oriented Dialogue Summarization dataset, TODSum, which aims to summarize the key points of the agent completing certain tasks with the user. Compared to existing work, TODSum suffers from severe scattered information issues and requires strict factual consistency, which makes it hard to directly apply recent dialogue summarization models. Therefore, we introduce additional dialogue state knowledge for TODSum to enhance the faithfulness of generated summaries. We hope a better understanding of conversational content helps summarization models generate concise and coherent summaries. Meanwhile, we establish a comprehensive benchmark for TODSum and propose a state-aware structured dialogue summarization model to integrate dialogue state information and dialogue history. Exhaustive experiments and qualitative analysis prove the effectiveness of dialogue structure guidance. Finally, we discuss the current issues of TODSum and potential development directions for future work.
SatGen -- II. Assessing the impact of a disc potential on subhalo populations

The demographics of dark matter substructure depend sensitively on the nature of dark matter. Optimally leveraging this probe requires accurate theoretical predictions regarding the abundance of subhaloes. These predictions are hampered by artificial disruption in numerical simulations, by large halo-to-halo variance, and by the fact that the results depend on the baryonic physics of galaxy formation. In particular, numerical simulations have shown that the formation of a central disc can drastically reduce the abundance of substructure compared to a dark matter-only simulation, which has been attributed to enhanced destruction of substructure due to disc shocking. We examine the impact of discs on substructure using the semi-analytical subhalo model SatGen, which accurately models the tidal evolution of substructure free of the numerical disruption that still hampers $N$-body simulations. Using a sample of 10,000 merger trees of Milky Way-like haloes, we study the demographics of subhaloes that are evolved under a range of composite halo-disc potentials with unprecedented statistical power. We find that the overall subhalo abundance is relatively insensitive to properties of the disc aside from its total mass. For a disc that contains $5\%$ of $M_\mathrm{vir}$, the mean subhalo abundance within $r_\mathrm{vir}$ is suppressed by ${\lesssim}10\%$ relative to the no-disc case, a difference that is dwarfed by halo-to-halo variance. For the same disc mass, the abundance of subhaloes within 50 kpc is reduced by ${\sim}30\%$. We argue that the disc mainly drives excess mass loss for subhaloes with small pericentric radii and that the impact of disc shocking is negligible.
No News is Good News: A Critique of the One Billion Word Benchmark

The One Billion Word Benchmark is a dataset derived from the WMT 2011 News Crawl, commonly used to measure language modeling ability in natural language processing. We train models solely on Common Crawl web scrapes partitioned by year, and demonstrate that they perform worse on this task over time due to distributional shift. Analysis of this corpus reveals that it contains several examples of harmful text, as well as outdated references to current events. We suggest that the temporal nature of news and its distribution shift over time makes it poorly suited for measuring language modeling ability, and discuss potential impact and considerations for researchers building language models and evaluation datasets.