Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Impedance Control for Dexterous Interaction Using Fractal Impedance Controller with IK-Optimisation

Robust dynamic interactions are required to move robots in daily environments alongside humans. Optimisation and learning methods have been used to mimic and reproduce human movements. However, they are often not robust and their generalisation is limited. This work proposed a hierarchical control architecture for robot manipulators and provided capabilities of reproducing human-like motions during unknown interaction dynamics. Our results show that the reproduced end-effector trajectories can preserve the main characteristics of the initial human motion recorded via a motion capture system, and are robust against external perturbations. The data indicate that some detailed movements are hard to reproduce due to the physical limits of the hardware that cannot reach the same velocity recorded in human movements. Nevertheless, these technical problems can be addressed by using better hardware and our proposed algorithms can still be applied to produce imitated motions.
A Neural Tangent Kernel Perspective of Infinite Tree Ensembles

In practical situations, the tree ensemble is one of the most popular models along with neural networks. A soft tree is a variant of a decision tree. Instead of using a greedy method for searching splitting rules, the soft tree is trained using a gradient method in which the entire splitting operation is formulated in a differentiable form. Although ensembles of such soft trees have been used increasingly in recent years, little theoretical work has been done to understand their behavior. By considering an ensemble of infinite soft trees, this paper introduces and studies the Tree Neural Tangent Kernel (TNTK), which provides new insights into the behavior of the infinite ensemble of soft trees. Using the TNTK, we theoretically identify several non-trivial properties, such as global convergence of the training, the equivalence of the oblivious tree structure, and the degeneracy of the TNTK induced by the deepening of the trees.
FedCon: A Contrastive Framework for Federated Semi-Supervised Learning

Federated Semi-Supervised Learning (FedSSL) has gained rising attention from both academic and industrial researchers, due to its unique characteristics of co-training machine learning models with isolated yet unlabeled data. Most existing FedSSL methods focus on the classical scenario, i.e, the labeled and unlabeled data are stored at the client side. However, in real world applications, client users may not provide labels without any incentive. Thus, the scenario of labels at the server side is more practical. Since unlabeled data and labeled data are decoupled, most existing FedSSL approaches may fail to deal with such a scenario. To overcome this problem, in this paper, we propose FedCon, which introduces a new learning paradigm, i.e., contractive learning, to FedSSL. Experimental results on three datasets show that FedCon achieves the best performance with the contractive framework compared with state-of-the-art baselines under both IID and Non-IID settings. Besides, ablation studies demonstrate the characteristics of the proposed FedCon framework.
Extraction of energy from an extremal rotating electrovacuum black hole: Particle collisions in the equatorial plane

The collisional Penrose process received much attention when Banados, Silk and West (BSW) pointed out the possibility of test-particle collisions with arbitrarily high center-of-mass energy in the vicinity of the horizon of an extremally rotating black hole. However, the energy that can be extracted from the black hole in this promising, if simplified, scenario, called the BSW effect, turned out to be subject to unconditional upper bounds. And although such bounds were not found for the electrostatic variant of the process, this version is also astrophysically unfeasible, since it requires a maximally charged black hole. In order to deal with these deficiencies, we revisit the unified version of the BSW effect concerning collisions of charged particles in the equatorial plane of a rotating electrovacuum black hole spacetime. Performing a general analysis of energy extraction through this process, we explain in detail how the seemingly incompatible limiting cases arise. Furthermore, we demonstrate that the unconditional upper bounds on the extracted energy are absent for arbitrarily small values of the black hole electric charge. Therefore, our setup represents an intriguing simplified model for possible highly energetic processes happening around astrophysical black holes, which may spin fast but can have only a tiny electric charge induced via interaction with an external magnetic field.
Iceberg Hashing: Optimizing Many Hash-Table Criteria at Once

Despite being one of the oldest data structures in computer science, hash tables continue to be the focus of a great deal of both theoretical and empirical research. A central reason for this is that many of the fundamental properties that one desires from a hash table are difficult to achieve simultaneously; thus many variants offering different trade-offs have been proposed. This article introduces Iceberg hashing, a hash table that simultaneously offers the strongest known guarantees on a large number of core properties. Iceberg hashing supports constant-time operations while improving on the state of the art for space efficiency, cache efficiency, and low failure probability. Iceberg hashing is also the first hash table to support a load factor of up to 1 - o(1) while being stable, meaning that the position where an element is stored only ever changes when resizes occur. In fact, in the setting where keys are Θ (log n) bits, the space guarantees that Iceberg hashing offers, namely that it uses at most \(\log \binom{|U|}{n} + O(n \log \ \text{log} n)\) bits to store n items from a universe U, matches a lower bound by Demaine et al. that applies to any stable hash table. Iceberg hashing introduces new general-purpose techniques for some of the most basic aspects of hash-table design. Notably, our indirection-free technique for dynamic resizing, which we call waterfall addressing, and our techniques for achieving stability and very-high probability guarantees, can be applied to any hash table that makes use of the front-yard/backyard paradigm for hash table design.
Efficiently Identifying Task Groupings for Multi-Task Learning

Multi-task learning can leverage information learned by one task to benefit the training of other tasks. Despite this capacity, naively training all tasks together in one model often degrades performance, and exhaustively searching through combinations of task groupings can be prohibitively expensive. As a result, efficiently identifying the tasks that would benefit from training together remains a challenging design question without a clear solution. In this paper, we suggest an approach to select which tasks should train together in multi-task learning models. Our method determines task groupings in a single run by training all tasks together and quantifying the effect to which one task's gradient would affect another task's loss. On the large-scale Taskonomy computer vision dataset, we find this method can decrease test loss by 10.0% compared to simply training all tasks together while operating 11.6 times faster than a state-of-the-art task grouping method.
What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers

GPT-3 shows remarkable in-context learning ability of large-scale language models (LMs) trained on hundreds of billion scale data. Here we address some remaining issues less reported by the GPT-3 paper, such as a non-English LM, the performances of different sized models, and the effect of recently introduced prompt optimization on in-context learning. To achieve this, we introduce HyperCLOVA, a Korean variant of 82B GPT-3 trained on a Korean-centric corpus of 560B tokens. Enhanced by our Korean-specific tokenization, HyperCLOVA with our training configuration shows state-of-the-art in-context zero-shot and few-shot learning performances on various downstream tasks in Korean. Also, we show the performance benefits of prompt-based learning and demonstrate how it can be integrated into the prompt engineering pipeline. Then we discuss the possibility of materializing the No Code AI paradigm by providing AI prototyping capabilities to non-experts of ML by introducing HyperCLOVA studio, an interactive prompt engineering interface. Lastly, we demonstrate the potential of our methods with three successful in-house applications.
Differentially Private Federated Learning with Laplacian Smoothing

Federated learning aims to protect data privacy by collaboratively learning a model without sharing private data among users. However, an adversary may still be able to infer the private training data by attacking the released model. Differential privacy provides a statistical protection against such attacks at the price of significantly degrading the accuracy or utility of the trained models. In this paper, we investigate a utility enhancement scheme based on Laplacian smoothing for differentially private federated learning (DP-Fed-LS), where the parameter aggregation with injected Gaussian noise is improved in statistical precision without losing privacy budget. Our key observation is that the aggregated gradients in federated learning often enjoy a type of smoothness, i.e. sparsity in the graph Fourier basis with polynomial decays of Fourier coefficients as frequency grows, which can be exploited by the Laplacian smoothing efficiently. Under a prescribed differential privacy budget, convergence error bounds with tight rates are provided for DP-Fed-LS with uniform subsampling of heterogeneous Non-IID data, revealing possible utility improvement of Laplacian smoothing in effective dimensionality and variance reduction, among others. Experiments over MNIST, SVHN, and Shakespeare datasets show that the proposed method can improve model accuracy with DP-guarantee and membership privacy under both uniform and Poisson subsampling mechanisms.
The Peculiar Chemical Pattern of the WASP-160 Binary System: Signatures of Planetary Formation and Evolution?

Wide binary stars with similar components hosting planets provide a favorable opportunity for exploring the star–planet chemical connection. We perform a detailed characterization of the solar-type stars in the WASP-160 binary system. No planet has been reported yet around WASP-160A, while WASP-160B is known to host a transiting Saturn-mass planet, WASP-160B b. For this planet, we also derive updated properties from both literature and new observations. Furthermore, using TESS photometry, we constrain the presence of transiting planets around WASP-160A and additional ones around WASP-160B. The stellar characterization includes, for the first time, the computation of high-precision differential atmospheric and chemical abundances of 25 elements based on high-quality Gemini-GRACES spectra. Our analysis reveals evidence of a correlation between the differential abundances and the condensation temperatures of the elements. In particular, we find both a small but significant deficit of volatiles and an enhancement of refractory elements in WASP-160B relative to WASP-160A. After WASP-94, this is the second stellar pair among the shortlist of planet-hosting binaries showing this kind of peculiar chemical pattern. Although we discuss several plausible planet formation and evolution scenarios for WASP-160A and B that could explain the observed chemical pattern, none of them can be conclusively accepted or rejected. Future high-precision photometric and spectroscopic follow-up, as well as high-contrast imaging observations, of WASP-160A and B might provide further constraints on the real origin of the detected chemical differences.
IndoBERTweet: A Pretrained Language Model for Indonesian Twitter with Effective Domain-Specific Vocabulary Initialization

We present IndoBERTweet, the first large-scale pretrained model for Indonesian Twitter that is trained by extending a monolingually-trained Indonesian BERT model with additive domain-specific vocabulary. We focus in particular on efficient model adaptation under vocabulary mismatch, and benchmark different ways of initializing the BERT embedding layer for new word types. We find that initializing with the average BERT subword embedding makes pretraining five times faster, and is more effective than proposed methods for vocabulary adaptation in terms of extrinsic evaluation over seven Twitter-based datasets.
Latent space projection predictive inference

Given a reference model that includes all the available variables, projection predictive inference replaces its posterior with a constrained projection including only a subset of all variables. We extend projection predictive inference to enable computationally efficient variable and structure selection in models outside the exponential family. By adopting a latent space projection predictive perspective we are able to: 1) propose a unified and general framework to do variable selection in complex models while fully honouring the original model structure, 2) properly identify relevant structure and retain posterior uncertainties from the original model, and 3) provide an improved approach also for non-Gaussian models in the exponential family. We demonstrate the superior performance of our approach by thoroughly testing and comparing it against popular variable selection approaches in a wide range of settings, including realistic data sets. Our results show that our approach successfully recovers relevant terms and model structure in complex models, selecting less variables than competing approaches for realistic datasets.
Probing the radial acceleration relation and the strong equivalence principle with the Coma cluster ultra-diffuse galaxies

The tight radial acceleration relation (RAR) obeyed by rotationally supported disk galaxies is one of the most successful a priori predictions of the modiﬁed Newtonian dynamics (MOND) paradigm on galaxy scales. Another important consequence of MOND as a classical modiﬁcation of gravity is that the strong equivalence principle (SEP) – which requires the dynamics of a small, free-falling, self-gravitating system not to depend on the external gravitational ﬁeld in which it is embedded – should be broken. Multiple tentative detections of this so-called external ﬁeld e ﬀ ect (EFE) of MOND have been made in the past, but the systems that should be most sensitive to it are galaxies with low internal gravitational accelerations residing in galaxy clusters within a strong external ﬁeld. Here, we show that ultra-di ﬀ use galaxies (UDGs) in the Coma cluster do lie on the RAR, and that their velocity dispersion proﬁles are in full agreement with isolated MOND predictions, especially when including some degree of radial anisotropy. However, including a breaking of the SEP via the EFE seriously deteriorates this agreement. We discuss various possibilities to explain this within the context of MOND, including a combination of tidal heating and higher baryonic masses. We also speculate that our results could mean that the EFE is screened in cluster UDGs. The fact that this would happen precisely within galaxy clusters, where classical MOND fails, could be especially relevant to the nature of the residual MOND missing mass in clusters of galaxies.
Sixteen Years of Phishing User Studies: What Have We Learned?

Several previous studies have investigated user susceptibility to phishing attacks. A thorough meta-analysis or systematic review is required to gain a better understanding of these findings and to assess the strength of evidence for phishing susceptibility of a subpopulation, e.g., older users. We aim to determine whether an effect exists; another aim is to determine whether the effect is positive or negative and to obtain a single summary estimate of the effect. OBJECTIVES: We systematically review the results of previous user studies on phishing susceptibility and conduct a meta-analysis. METHOD: We searched four online databases for English studies on phishing. We included all user studies in phishing detection and prevention, whether they proposed new training techniques or analyzed users’ vulnerability. FINDINGS: A careful analysis reveals some discrepancies between the findings. More than half of the studies that analyzed the effect of age reported no statistically significant relationship between age and users’ performance. Some studies reported older people performed better while some reported the opposite. A similar finding holds for the gender difference. The meta-analysis shows: 1) a significant relationship between participants’ age and their susceptibility 2) females are more susceptible than males 3) users training significantly improves their detection ability.
EfficientCLIP: Efficient Cross-Modal Pre-training by Ensemble Confident Learning and Language Modeling

While large scale pre-training has achieved great achievements in bridging the gap between vision and language, it still faces several challenges. First, the cost for pre-training is expensive. Second, there is no efficient way to handle the data noise which degrades model performance. Third, previous methods only leverage limited image-text paired data, while ignoring richer single-modal data, which may result in poor generalization to single-modal downstream tasks. In this work, we propose an EfficientCLIP method via Ensemble Confident Learning to obtain a less noisy data subset. Extra rich non-paired single-modal text data is used for boosting the generalization of text branch. We achieve the state-of-the-art performance on Chinese cross-modal retrieval tasks with only 1/10 training resources compared to CLIP and WenLan, while showing excellent generalization to single-modal tasks, including text retrieval and text classification.
Testing the Strong Equivalence Principle. II. Relating the External Field Effect in Galaxy Rotation Curves to the Large-scale Structure of the Universe

Theories of modified gravity generically violate the strong equivalence principle, so that the internal dynamics of a self-gravitating system in freefall depends on the strength of the external gravitational field (the external field effect). We fit rotation curves (RCs) from the SPARC database with a model inspired by Milgromian dynamics (MOND), which relates the outer shape of an RC to the external Newtonian field from the large-scale baryonic matter distribution through a dimensionless parameter e N. We obtain a > 4σ statistical detection of the external field effect (i.e. e N > 0 on average), confirming previous results. We then locate the SPARC galaxies in the cosmic web of the nearby universe and find a striking contrast in the fitted e N values for galaxies in underdense versus overdense regions. Galaxies in an underdense region between 22 and 45 Mpc from the celestial axis in the northern sky have RC fits consistent with e N ≃ 0, while those in overdense regions adjacent to the CfA2 Great Wall and the Perseus−Pisces Supercluster return e N that are a factor of two larger than the median for SPARC galaxies. We also calculate independent estimates of e N from galaxy survey data and find that they agree with the e N inferred from the RCs within the uncertainties, the chief uncertainty being the spatial distribution of baryons not contained in galaxies or clusters.
Polarized solitons in higher-spin wave dark matter

We first show that the effective non-relativistic theory of gravitationally interacting, massive integer-spin fields (spin-$0$, $1$, and $2$ in particular) is described by a $2s+1$ component Schr\"{o}dinger-Poisson action, where $s$ is the spin of the field. We then construct $s+1$ distinct, gravitationally supported solitons in this non-relativistic theory from identically polarized plane waves. Such solitons are extremally polarized, with macroscopically large spin, but no orbital angular momentum. These $s+1$ solitons form a basis set, out of which partially polarized solitons can be constructed. All such solitons are ground states, have a spherically symmetric energy density but not field configurations. We discuss how solitons in higher-spin fields can be distinguished from scalar solitons, and potential gravitational and non-gravitational probes of them.
Neural Machine Translation Quality and Post-Editing Performance

We test the natural expectation that using MT in professional translation saves human processing time. The last such study was carried out by Sanchez-Torron and Koehn (2016) with phrase-based MT, artificially reducing the translation quality. In contrast, we focus on neural MT (NMT) of high quality, which has become the state-of-the-art approach since then and also got adopted by most translation companies. Through an experimental study involving over 30 professional translators for English -> Czech translation, we examine the relationship between NMT performance and post-editing time and quality. Across all models, we found that better MT systems indeed lead to fewer changes in the sentences in this industry setting. The relation between system quality and post-editing time is however not straightforward and, contrary to the results on phrase-based MT, BLEU is definitely not a stable predictor of the time or final output quality.
Examining Cross-lingual Contextual Embeddings with Orthogonal Structural Probes

State-of-the-art contextual embeddings are obtained from large language models available only for a few languages. For others, we need to learn representations using a multilingual model. There is an ongoing debate on whether multilingual embeddings can be aligned in a space shared across many languages. The novel Orthogonal Structural Probe (Limisiewicz and Mareček, 2021) allows us to answer this question for specific linguistic features and learn a projection based only on mono-lingual annotated datasets. We evaluate syntactic (UD) and lexical (WordNet) structural information encoded inmBERT’s contextual representations for nine diverse languages. We observe that for languages closely related to English, no transformation is needed. The evaluated information is encoded in a shared cross-lingual embedding space. For other languages, it is beneficial to apply orthogonal transformation learned separately for each language. We successfully apply our findings to zero-shot and few-shot cross-lingual parsing.