Miracle-dz/UniFAR-FTUs huggingface dataset:
First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization

How can we train an assistive human-machine interface (e.g., an electromyography-based limb prosthesis) to translate a user's raw command signals into the actions of a robot or computer when there is no prior mapping, we cannot ask the user for supervision in the form of action labels or reward feedback, and we do not have prior knowledge of the tasks the user is trying to accomplish? The key idea in this paper is that, regardless of the task, when an interface is more intuitive, the user's commands are less noisy. We formalize this idea as a completely unsupervised objective for optimizing interfaces: the mutual information between the user's command signals and the induced state transitions in the environment. To evaluate whether this mutual information score can distinguish between effective and ineffective interfaces, we conduct an observational study on 540K examples of users operating various keyboard and eye gaze interfaces for typing, controlling simulated robots, and playing video games. The results show that our mutual information scores are predictive of the ground-truth task completion metrics in a variety of domains, with an average Spearman's rank correlation of 0.43. In addition to offline evaluation of existing interfaces, we use our unsupervised objective to learn an interface from scratch: we randomly initialize the interface, have the user attempt to perform their desired tasks using the interface, measure the mutual information score, and update the interface to maximize mutual information through reinforcement learning. We evaluate our method through a user study with 12 participants who perform a 2D cursor control task using a perturbed mouse, and an experiment with one user playing the Lunar Lander game using hand gestures. The results show that we can learn an interface from scratch, without any user supervision or prior knowledge of tasks, in under 30 minutes.
Action Recognition for American Sign Language

In this research, we present our ﬁndings to recognize American Sign Language from series of hand gestures. While most researches in literature focus only on static handshapes, our work target dynamic hand gestures. Since dynamic signs dataset are very few, we collect an initial dataset of 150 videos for 10 signs and an extension of 225 videos for 15 signs. We apply transfer learning models in combination with deep neural networks and background subtraction for videos in different temporal settings. Our primarily results show that we can get an accuracy of 0 . 86 and 0 . 71 using DenseNet201, LSTM with video sequence of 12 frames accordingly.
Certified Robustness Against Natural Language Attacks by Causal Intervention

Deep learning models have achieved great success in many fields, yet they are vulnerable to adversarial examples. This paper follows a causal perspective to look into the adversarial vulnerability and proposes Causal Intervention by Semantic Smoothing (CISS), a novel framework towards robustness against natural language attacks. Instead of merely fitting observational data, CISS learns causal effects p ( y | do ( x )) by smoothing in the latent semantic space to make robust predictions, which scales to deep architectures and avoids tedious construction of noise customized for specific attacks. CISS is provably robust against word substitution attacks, as well as empirically robust even when perturbations are strengthened by unknown attack algorithms. For example, on YELP, CISS surpasses the runner-up by 6.8% in terms of certified robustness against word substitutions, and achieves 80.7% empirical robustness when syntactic attacks are integrated.
Image Colorization using U-Net with Skip Connections and Fusion Layer on Landscape Images

We present a novel technique to automatically colorize grayscale images that combine the U-Net model and Fusion Layer features. This approach allows the model to learn the colorization of images from pre-trained U-Net. Moreover, the Fusion layer is applied to merge local information results dependent on small image patches with global priors of an entire image on each class, forming visually more compelling colorization results. Finally, we validate our approach with a user study evaluation and compare it against state-of-the-art, resulting in improvements.
On the Interpretability of Regularisation for Neural Networks Through Model Gradient Similarity

Most complex machine learning and modelling techniques are prone to over-fitting and may subsequently generalise poorly to future data. Artificial neural networks are no different in this regard and, despite having a level of implicit regularisation when trained with gradient descent, often require the aid of explicit regularisers. We introduce a new framework, Model Gradient Similarity (MGS), that (1) serves as a metric of regularisation, which can be used to monitor neural network training, (2) adds insight into how explicit regularisers, while derived from widely different principles, operate via the same mechanism underneath by increasing MGS, and (3) provides the basis for a new regularisation scheme which exhibits excellent performance, especially in challenging settings such as high levels of label noise or limited sample sizes.
Looking forward to forward physics at the CERN's LHC

For decades, new physics searches in collider experiments have focused on the high-p_TpT region. However, it has recently become evident that the LHC physics potential has not been fully exploited. To be specific, forward collisions, which produce particles along the beamline with enormous rates, have been almost completely ignored. For all practical purposes, these collisions are a treasure trove of physics, containing the highest-energy neutrinos ever produced by humans, as well as possible evidence for dark matter, light and weakly-coupled particles, and new forces. In the upcoming LHC Run 3 the ForwArd Search ExpeRiment (FASER) and its cousin FASER\nuν will extend the LHC’s physics potential. A continuation of this forward physics program for the HL-LHC aims at the Forward Physics Facility (FPF), with larger scale experiments. In this report, I give an overview of the physics motivations for FASER, FASER\nuν, and the FPF experiments, including both Standard Model and beyond Standard Model physics.
TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Nature

Trust evaluation is critical for many applications such as cyber security, social communication, and recommender systems. Users and trust relationships among them can be seen as a graph. Graph neural networks (GNNs) show their powerful ability for analyzing graph-structural data. Very recently, existing work attempted to introduce the attributes and asymmetry of edges into GNNs for trust evaluation, while failed to capture some essential properties (e.g., the propagative and composable nature) of trust graphs. In this work, we propose a new GNN-based trust evaluation method named TrustGNN, which integrates smartly the propagative and composable nature of trust graphs into a GNN framework for better trust evaluation. Specifically, TrustGNN designs specific propagative patterns for different propagative processes of trust, and distinguishes the contribution of different propagative processes to create new trust. Thus, TrustGNN can learn comprehensive node embeddings and predict trust relationships based on these embeddings. Experiments on some widely-used real-world datasets indicate that TrustGNN significantly outperforms the state-of-the-art methods. We further perform analytical experiments to demonstrate the effectiveness of the key designs in TrustGNN.
Autoformalization with Large Language Models

Autoformalization is the process of automatically translating from natural language mathematics to formal specifications and proofs. A successful autoformalization system could advance the fields of formal verification, program synthesis, and artificial intelligence. While the long-term goal of autoformalization seemed elusive for a long time, we show large language models provide new prospects towards this goal. We make the surprising observation that LLMs can correctly translate a significant portion ($25.3\%$) of mathematical competition problems perfectly to formal specifications in Isabelle/HOL. We demonstrate the usefulness of this process by improving a previously introduced neural theorem prover via training on these autoformalized theorems. Our methodology results in a new state-of-the-art result on the MiniF2F theorem proving benchmark, improving the proof rate from $29.6\%$ to $35.2\%$.
MAVIPER: Learning Decision Tree Policies for Interpretable Multi-Agent Reinforcement Learning

Many recent breakthroughs in multi-agent reinforcement learning (MARL) require the use of deep neural networks, which are challenging for human experts to interpret and understand. On the other hand, existing work on interpretable reinforcement learning (RL) has shown promise in extracting more interpretable decision tree-based policies from neural networks, but only in the single-agent setting. To fill this gap, we propose the first set of algorithms that extract interpretable decision-tree policies from neural networks trained with MARL. The first algorithm, IVIPER, extends VIPER, a recent method for single-agent interpretable RL, to the multi-agent setting. We demonstrate that IVIPER learns high-quality decision-tree policies for each agent. To better capture coordination between agents, we propose a novel centralized decision-tree training algorithm, MAVIPER. MAVIPER jointly grows the trees of each agent by predicting the behavior of the other agents using their anticipated trees, and uses resampling to focus on states that are critical for its interactions with other agents. We show that both algorithms generally outperform the baselines and that MAVIPER-trained agents achieve better-coordinated performance than IVIPER-trained agents on three different multi-agent particle-world environments.
lpSpikeCon: Enabling Low-Precision Spiking Neural Network Processing for Efficient Unsupervised Continual Learning on Autonomous Agents

Recent advances have shown that Spiking Neural Network (SNN)-based systems can efficiently perform unsuper-vised continual learning due to their bio-plausible learning rule, e.g., Spike-Timing-Dependent Plasticity (STDP). Such learning capabilities are especially beneficial for use cases like autonomous agents (e.g., robots and UAVs) that need to continuously adapt to dynamically changing scenarios/environments, where new data gathered directly from the environment may have novel features that should be learned online. Current state-of-the-art works employ high-precision weights (i.e., 32 bit) for both training and inference phases, which pose high memory and energy costs thereby hindering efficient embedded implementations of such systems for battery-driven mobile autonomous systems. On the other hand, precision reduction may jeopardize the quality of unsupervised continual learning due to information loss. Towards this, we propose lpSpikeCon, a novel methodology to enable low-precision SNN processing for efficient unsupervised continual learning on resource-constrained autonomous agents/systems. Our lpSpikeCon methodology employs the following key steps: (1) analyzing the impacts of training the SNN model under unsuper-vised continual learning settings with reduced weight precision on the inference accuracy; (2) leveraging this study to identify SNN parameters that have a significant impact on the inference accuracy; and (3) developing an algorithm for searching the respective SNN parameter values that improve the quality of unsupervised continual learning. The experimental results show that our lpSpikeCon can reduce weight memory of the SNN model by 8x (i.e., by judiciously employing 4-bit weights) for performing online training with unsupervised continual learning and achieve no accuracy loss in the inference phase, as compared to the baseline model with 32-bit weights across different network sizes.
Low Resource Style Transfer via Domain Adaptive Meta Learning

Text style transfer (TST) without parallel data has achieved some practical success. However, most of the existing unsupervised text style transfer methods suffer from (i) requiring massive amounts of non-parallel data to guide transferring different text styles. (ii) colossal performance degradation when fine-tuning the model in new domains. In this work, we propose DAML-ATM (Domain Adaptive Meta-Learning with Adversarial Transfer Model), which consists of two parts: DAML and ATM. DAML is a domain adaptive meta-learning approach to learn general knowledge in multiple heterogeneous source domains, capable of adapting to new unseen domains with a small amount of data.Moreover, we propose a new unsupervised TST approach Adversarial Transfer Model (ATM), composed of a sequence-to-sequence pre-trained language model and uses adversarial style training for better content preservation and style transfer.Results on multi-domain datasets demonstrate that our approach generalizes well on unseen low-resource domains, achieving state-of-the-art results against ten strong baselines.
Existence and multiplicity of solutions for a class of Kirchhoff type $(\Phi_1,\Phi_2)$-Laplacian system with locally super-linear condition in $\mathbb{R}^N$

: We investigate the existence and multiplicity of weak solutions for a nonlinear Kirchhoﬀ type quasilinear elliptic system on the whole space R N . We assume that the nonlinear term satisﬁes the locally super-( m 1 , m 2 ) condition, that is, lim | ( u,v ) |→ + ∞ F ( x,u,v ) | u | m 1 + | v | m 2 = + ∞ for a.e. x ∈ G where G is a domain in R N , which is weaker than the well-known Ambrosseti-Rabinowitz condition and the naturally global restriction, lim | ( u,v ) |→ + ∞ F ( x,u,v ) | u | m 1 + | v | m 2 = + ∞ for a.e. x ∈ R N . We obtain that system has at least one weak solution by using the classical Mountain Pass Theorem. To a certain extent, our theorems extend the results of Tang-Lin-Yu [Journal of Dynamics and Diﬀerential Equations, 2019, 31(1): 369-383]. Moreover, under the above naturally global restriction, we obtain that system has inﬁnitely many weak solutions of high energy by using the Symmetric Mountain Pass Theorem, which is diﬀerent from those results of Wang-Zhang-Fang [Journal of Nonlinear Sciences and Applications, 2017, 10(7): 3792-3814] even if we consider the system on the bounded domain with Dirichlet boundary condition.
Differentially Private AUC Computation in Vertical Federated Learning

Federated learning has gained great attention recently as a privacy-enhancing tool to jointly train a machine learning model by multiple parties. As a sub-category, vertical federated learning (vFL) focuses on the scenario where features and labels are split into different parties. The prior work on vFL has mostly studied how to protect label privacy during model training. However, model evaluation in vFL might also lead to potential leakage of private label information. One mitigation strategy is to apply label differential privacy (DP) but it gives bad estimations of the true (non-private) metrics. In this work, we propose two evaluation algorithms that can more accurately compute the widely used AUC (area under curve) metric when using label DP in vFL. Through extensive experiments, we show our algorithms can achieve more accurate AUCs compared to the baselines.
EDIN: An End-to-end Benchmark and Pipeline for Unknown Entity Discovery and Indexing

Existing work on Entity Linking mostly assumes that the reference knowledge base is complete, and therefore all mentions can be linked. In practice this is hardly ever the case, as knowledge bases are incomplete and because novel concepts arise constantly. We introduce the temporally segmented Unknown Entity Discovery and Indexing (EDIN)-benchmark where unknown entities, that is entities not part of the knowledge base and without descriptions and labeled mentions, have to be integrated into an existing entity linking system. By contrasting EDIN with zero-shot entity linking, we provide insight on the additional challenges it poses. Building on dense-retrieval based entity linking, we introduce the end-to-end EDIN-pipeline that detects, clusters, and indexes mentions of unknown entities in context. Experiments show that indexing a single embedding per entity unifying the information of multiple mentions works better than indexing mentions independently.
Effects of antireflective coatings on scanning performance of millimetre-wave lenses

This study examines the beam scanning performance of the antireflective coatings designed on millimetre-wave integrated lens antennas with dense material. Dielectric lens antennas fabricated with a dense dielectric material allow good power transfer efficiency through the lens and enable fabrication of low-cost and compact-size lens antennas. However, using dense dielectric material causes strong multiple internal reflection behaviour inside the lens antenna. These multiple reflections deteriorate not only the return loss but also the radiation characteristics. Wide angle scanning performance is limited due to off-axis aberrations. Effects of strong internal reflections can be reduced considerably using one or more dielectric matching layers coated at the top of the antenna. A 77 GHz extended hemispherical lens antenna made of alumina is coated with different types of solutions for antireflection layers, namely, conventional quarter-wave transformer, multiple layer transformer, linear and circular corrugations and their performance in beam scanning applications is observed. The lens is illuminated by an aperture coupled microstrip antenna designed to radiate into alumina at 77 GHz. A frequency-dependent characterisation of the beam pattern clearly showed the existence and impact of internal reflections on beam scanning performance for 77 GHz automotive radar antennas.
Typical Lightning Protection System for Wind Turbines

Wind turbines are tall structures with heights exceeding 120m. They are usually installed on high altitudes or isolated areas. In these areas, lightning activity could be high and the installation conditions wind turbines are vulnerable to lightning strikes. Furthermore, these areas do not have ideal soil conditions and the efficiency of wind turbine lightning protection system (LPS) can be affected. Additionally, the LPS shall be compatible with IEC61400-24 as minimum; furthermore, the earthing system shall keep step and touch voltages within the limits defined in IEEE80-2000. Considering the high amounts of wind turbines installed annually and also the limited project time-schedules, it is needed to have a typical LPS for wind turbines that complies with the relevant standards; based on this, wind turbines are armoured against lightning strikes. In case of non-effective LPS, damages can be caused in vital wind turbine components like blades which can lead to downtime and loss of energy production. This paper will present a typical LPS for wind turbines. This system can be applied in every site condition up to a pre-defined level, provided that it respects local legislation and can protect the structure in a reliable way. This proposal does not replace the site specific design of LPS but can be used as basis for further adaption in local conditions. Sokratis Pastromas, Ioannis Naxakis, Elftheria Pyrgioti Typical Lightning Protection System for Wind Turbines 185
Input Signal Waveforms for Maximal Injection-Locking Range –An Application to CMOS Ring Oscillators–

a) E-mail: htanaka@uec.ac. jp ことが必要になる. 従来の解析手法では,発振器の弱 非線形性や入出力波形が正弦波に近いという前提が必 要であった [6]~[12]. しかし,例えば多段の CMOS リングオシレータやパルス入力を含む注入信号源の多 様化(例として [13])に対応するためには,より一般 的かつ適用範囲の広い解析の枠組みが必要になってき ている. これと独立に,非線形振動論や非線形物理の 分野では,発振器の発振状態を,その発振位相につい て閉じた微分方程式に縮約する理論が構築され,従来 のアドラーの方程式の一般化に相当する枠組みが得ら れている(例として [14]). その結果,CMOSリング オシレータを含む強い非線形性をもつ発振器の位相ノ イズ [15], [16]や注入同期 [17]~[20]についての解析が 可能となっている. このアドラーの方程式の一般化に より,この数年で注入同期の性能を最大化する理論的 枠組みも整備され [21], [22],注入同期の性能向上限界 の存在も明らかになっている [23], [24]. 本論文は,上記の理論的成果を援用し,実用上有用 な発振器の引き込み周波数帯を最大化する入力信号の 設計が可能であることをはじめて示すものである. す なわち CMOS リングオシレータを対象として,諸々 の現実的制約条件のもとに,その引き込み周波数帯
Insights into the Genetic Spatial Structure of Nicaraguan Weedy Rice and Control of Its Seed Spread.

BACKGROUND The genetic backgrounds and occurrence patterns of weedy rice (WR, Oryza sativa) are highly diverse, and so are the challenges facing its control among countries. WR control is difficult because it is similar to cultivated rice and manual removal is one of the few options of control. Understanding the ecology of WR will aid efforts to break its life cycle and establish long-term management strategies under both irrigated and rainfed systems. RESULTS Nicaraguan WR (NWR) plants were genetically closer to the AUS and Indica pools according to genetic distance. A map of admixture coefficients suggested a pattern of long-distance dispersal and spread of NWR across Nicaragua, which has likely been facilitated by commercial activities and sharing of harvesting equipment between border cities or important trading ports and inland regions. Moreover, the NWR plants from the soil seedbank in irrigated regions showed different habitats and lower grain number per panicle compared with plants spread by seed-mediated contamination. In addition, grain indexes showed that the length-width ratio was a better indicator than awn length for distinguishing between NWR and Nicaraguan Indica cultivars. CONCLUSION Analysis of the population structure and habitats of NWR revealed five clusters derived from seed-mediated contamination in rainfed upland regions, plants from the soil seedbank in irrigated double cropping regions, and pollen-mediated contamination across both regions. Field weed management before harvesting and seed purification based on length-width ratio can be conducted to improve the efficiency of long-term control of WR in Nicaragua. This article is protected by copyright. All rights reserved.
Solid Fuel, Secondhand Smoke, and Lung Cancer Mortality: A Prospective Cohort of 323,794 Chinese Never-Smokers

Rationale Household air pollution and secondhand tobacco smoke are known carcinogens for lung cancer, but large-scale estimates of the relationship with lung cancer mortality are lacking. Objectives Using the large-scale cohort China Kadoorie Biobank, we prospectively investigated associations between these two risk factors and lung cancer death among never-smokers. Methods The Biobank recruited 512,715 adults aged 30–79 years from 10 regions in China during 2004–2008. Self-reported never-smoking participants were followed up to December 31, 2016, with linkage to mortality data. Total duration of exposure to household air pollution was calculated from self-reported domestic solid fuel use. Exposure to secondhand tobacco smoke was ascertained using exposure at home and/or other places. Hazard ratios and 95% confidence intervals for associations between these two exposures and lung cancer death were estimated using Cox regression, adjusting for key confounders. Measurements and Main Results There were 979 lung cancer deaths among 323,794 never-smoking participants without a previous cancer diagnosis during 10.2 years of follow-up. There was a log-linear positive association between exposure to household air pollution and lung cancer death, with a 4% increased risk per 5-year increment of exposure (hazard ratio = 1.04; 95% confidence interval = 1.01–1.06; P trend = 0.0034), and participants with 40.1–50.0 years of exposure had the highest risk compared with the never-exposed (hazard ratio = 1.53; 95% confidence interval = 1.13–2.07). The association was largely consistent across various subgroups. No significant association was found between secondhand smoke and lung cancer death. Conclusions This cohort study provides new prospective evidence suggesting that domestic solid fuel use is associated with lung cancer death among never-smokers.