Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-resource Bilingual Dialect Lexicon Induction with Large Language Models

Bilingual word lexicons map words in one language to their synonyms in another language. Numerous papers have explored bilingual lexicon induction (BLI) in high-resource scenarios, framing a typical pipeline that consists of two steps: (i) unsupervised bitext mining and (ii) unsupervised word alignment. At the core of those steps are pre-trained large language models (LLMs). In this paper we present the analysis of the BLI pipeline for German and two of its dialects, Bavarian and Alemannic. This setup poses a number of unique challenges, attributed to the scarceness of resources, relatedness of the languages and lack of standardization in the orthography of dialects. We analyze the BLI outputs with respect to word frequency and the pairwise edit distance. Finally, we release an evaluation dataset consisting of manual annotations for 1K bilingual word pairs labeled according to their semantic similarity.
Analyzing FOMC Minutes: Accuracy and Constraints of Language Models

This research article analyzes the language used in the official statements released by the Federal Open Market Committee (FOMC) after its scheduled meetings to gain insights into the impact of FOMC official statements on financial markets and economic forecasting. The study reveals that the FOMC is careful to avoid expressing emotion in their sentences and follows a set of templates to cover economic situations. The analysis employs advanced language modeling techniques such as VADER and FinBERT, and a trial test with GPT-4. The results show that FinBERT outperforms other techniques in predicting negative sentiment accurately. However, the study also highlights the challenges and limitations of using current NLP techniques to analyze FOMC texts and suggests the potential for enhancing language models and exploring alternative approaches.
Personalized State Anxiety Detection: An Empirical Study with Linguistic Biomarkers and A Machine Learning Pipeline

Individuals high in social anxiety symptoms often exhibit elevated state anxiety in social situations. Research has shown it is possible to detect state anxiety by leveraging digital biomarkers and machine learning techniques. However, most existing work trains models on an entire group of participants, failing to capture individual differences in their psychological and behavioral responses to social contexts. To address this concern, in Study 1, we collected linguistic data from N=35 high socially anxious participants in a variety of social contexts, finding that digital linguistic biomarkers significantly differ between evaluative vs. non-evaluative social contexts and between individuals having different trait psychological symptoms, suggesting the likely importance of personalized approaches to detect state anxiety. In Study 2, we used the same data and results from Study 1 to model a multilayer personalized machine learning pipeline to detect state anxiety that considers contextual and individual differences. This personalized model outperformed the baseline’s F1-score by 28.0%. Results suggest that state anxiety can be more accurately detected with personalized machine learning approaches, and that linguistic biomarkers hold promise for identifying periods of state anxiety in an unobtrusive way.
Road Genome: A Topology Reasoning Benchmark for Scene Understanding in Autonomous Driving

Understanding the complex traﬃc environment is crucial for self-driving vehicles. Existing benchmarks in autonomous driving mainly cast scene understanding as perception problems, e.g., perceiving lanelines with vanilla detection or segmentation methods. As such, we argue that the perception pipeline provides limited information for autonomous vehicles to drive in the right way, especially without the aid of high-deﬁnition (HD) map. For instance, following the wrong traﬃc signal at a complicated crossroad would lead to a catastrophic incident. By introducing Road Genome (OpenLane-V2) , we intend to shift the community’s attention and take a step further beyond perception - to the task of topology reasoning for scene structure. The goal of Road Genome is to understand the scene structure by investigating the relationship of perceived entities among traﬃc elements and lanes. Built on top of prevailing datasets, the newly minted benchmark comprises 2,000 sequences of multi-view images captured from diverse real-world scenarios. We annotate data with high-quality manual checks in the loop. Three subtasks compromise the gist of Road Genome, including the 3D lane detection inherited from OpenLane. We have/will host Challenges in the upcoming future at top-tiered venues.
Cyber Security in Smart Manufacturing (Threats, Landscapes Challenges)

Industry 4.0 is a blend of the hyper-connected digital industry within two world of Information Technology (IT) and Operational Technology (OT). With this amalgamate opportunity, smart manufacturing involves production assets with the manufacturing equipment having its own intelligence, while the system-wide intelligence is provided by the cyber layer. However Smart manufacturing now becomes one of the prime targets of cyber threats due to vulnerabilities in the existing process of operation. Since smart manufacturing covers a vast area of production industries from cyber physical system to additive manufacturing, to autonomous vehicles, to cloud based IIoT (Industrial IoT), to robotic production, cyber threat stands out with this regard questioning about how to connect manufacturing resources by network, how to integrate a whole process chain for a factory production etc. Cybersecurity confidentiality, integrity and availability expose their essential existence for the proper operational thread model known as digital thread ensuring secure manufacturing. In this work, a literature survey is presented from the existing threat models, attack vectors and future challenges over the digital thread of smart manufacturing.
Giant Planet Engulfment by Evolved Giant Stars: Light Curves, Asteroseismology, and Survivability

About ten percent of Sun-like (1–2 M ⊙) stars will engulf a 1–10 M J planet as they expand during the red giant branch (RGB) or asymptotic giant branch (AGB) phase of their evolution. Once engulfed, these planets experience a strong drag force in the star’s convective envelope and spiral inward, depositing energy and angular momentum. For these mass ratios, the inspiral takes ∼10–102 yr (∼102–103 orbits); the planet undergoes tidal disruption at a radius of ∼1 R ⊙. We use the Modules for Experiments in Stellar Astrophysics (MESA) software instrument to track the stellar response to the energy deposition while simultaneously evolving the planetary orbit. For RGB stars, as well as AGB stars with M p ≲ 5 M J planets, the star responds quasi-statically but still brightens measurably on a timescale of years. In addition, asteroseismic indicators, such as the frequency spacing or rotational splitting, differ before and after engulfment. For AGB stars, engulfment of an M p ≳ 5 M J planet drives supersonic expansion of the envelope, causing a bright, red, dusty eruption similar to a “luminous red nova. ” Based on the peak luminosity, color, duration, and expected rate of these events, we suggest that engulfment events on the AGB could be a significant fraction of low-luminosity red novae in the Galaxy. We do not find conditions where the envelope is ejected prior to the planet’s tidal disruption, complicating the interpretation of short-period giant planets orbiting white dwarfs as survivors of common envelope evolution.
Regret-Minimizing Double Oracle for Extensive-Form Games

By incorporating regret minimization, double oracle methods have demonstrated rapid convergence to Nash Equilibrium (NE) in normal-form games and extensive-form games, through algorithms such as online double oracle (ODO) and extensive-form double oracle (XDO), respectively. In this study, we further examine the theoretical convergence rate and sample complexity of such regret minimization-based double oracle methods, utilizing a unified framework called Regret-Minimizing Double Oracle. Based on this framework, we extend ODO to extensive-form games and determine its sample complexity. Moreover, we demonstrate that the sample complexity of XDO can be exponential in the number of information sets $|S|$, owing to the exponentially decaying stopping threshold of restricted games. To solve this problem, we propose the Periodic Double Oracle (PDO) method, which has the lowest sample complexity among regret minimization-based double oracle methods, being only polynomial in $|S|$. Empirical evaluations on multiple poker and board games show that PDO achieves significantly faster convergence than previous double oracle algorithms and reaches a competitive level with state-of-the-art regret minimization methods.
Farm3D: Learning Articulated 3D Animals by Distilling 2D Diffusion

We present Farm3D, a method for learning category-specific 3D reconstructors for articulated objects, relying solely on"free"virtual supervision from a pre-trained 2D diffusion-based image generator. Recent approaches can learn a monocular network that predicts the 3D shape, albedo, illumination, and viewpoint of any object occurrence, given a collection of single-view images of an object category. However, these approaches heavily rely on manually curated clean training data, which are expensive to obtain. We propose a framework that uses an image generator, such as Stable Diffusion, to generate synthetic training data that are sufficiently clean and do not require further manual curation, enabling the learning of such a reconstruction network from scratch. Additionally, we incorporate the diffusion model as a score to enhance the learning process. The idea involves randomizing certain aspects of the reconstruction, such as viewpoint and illumination, generating virtual views of the reconstructed 3D object, and allowing the 2D network to assess the quality of the resulting image, thus providing feedback to the reconstructor. Unlike work based on distillation, which produces a single 3D asset for each textual prompt, our approach yields a monocular reconstruction network capable of outputting a controllable 3D asset from any given image, whether real or generated, in a single forward pass in a matter of seconds. Our network can be used for analysis, including monocular reconstruction, or for synthesis, generating articulated assets for real-time applications such as video games.
Not Only Generative Art: Stable Diffusion for Content-Style Disentanglement in Art Analysis

The duality of content and style is inherent to the nature of art. For humans, these two elements are clearly different: content refers to the objects and concepts in the piece of art, and style to the way it is expressed. This duality poses an important challenge for computer vision. The visual appearance of objects and concepts is modulated by the style that may reflect the author’s emotions, social trends, artistic movement, etc., and their deep comprehension undoubtfully requires to handle both. A promising step towards a general paradigm for art analysis is to disentangle content and style, whereas relying on human annotations to cull a single aspect of artworks has limitations in learning semantic concepts and the visual appearance of paintings. We thus present GOYA, a method that distills the artistic knowledge captured in a recent generative model to disentangle content and style. Experiments show that synthetically generated images sufficiently serve as a proxy of the real distribution of artworks, allowing GOYA to separately represent the two elements of art while keeping more information than existing methods.
IoT-based Wearables: A comprehensive Survey

A substantial amount of growth is being achieved by businesses through IoT-based services. The emergent of small electronic devices capable of computing, which are commonly known as wearables in IoT domain has proven to have huge impact in people's life. Theses wearables are capable of collecting vital information about a person's activities and behaviours regularly. This makes them suitable for many applications in health monitoring, fitness, sports, education and some industry related applications. To this end, in this paper, we aim to provide a general review on IoT-based wearables, the sensors adopted for several categorized wearables, the communication technologies adopted and the most widely adopted data processing techniques for wearables. Furthermore, we present the challenges faced for wide adoption of wearables and the future research directions.
GREAT Score: Global Robustness Evaluation of Adversarial Perturbation using Generative Models

Current studies on adversarial robustness mainly focus on aggregating local robustness results from a set of data samples to evaluate and rank different models. However, the local statistics may not well represent the true global robustness of the underlying unknown data distribution. To address this challenge, this paper makes the first attempt to present a new framework, called GREAT Score , for global robustness evaluation of adversarial perturbation using generative models. Formally, GREAT Score carries the physical meaning of a global statistic capturing a mean certified attack-proof perturbation level over all samples drawn from a generative model. For finite-sample evaluation, we also derive a probabilistic guarantee on the sample complexity and the difference between the sample mean and the true mean. GREAT Score has several advantages: (1) Robustness evaluations using GREAT Score are efficient and scalable to large models, by sparing the need of running adversarial attacks. In particular, we show high correlation and significantly reduced computation cost of GREAT Score when compared to the attack-based model ranking on RobustBench (Croce,et. al. 2021). (2) The use of generative models facilitates the approximation of the unknown data distribution. In our ablation study with different generative adversarial networks (GANs), we observe consistency between global robustness evaluation and the quality of GANs. (3) GREAT Score can be used for remote auditing of privacy-sensitive black-box models, as demonstrated by our robustness evaluation on several online facial recognition services.
Utilitarian Theorems and Equivalence of Utility Theories

In this paper, we consider an environment in which the utilitarian theorem for the NM utility function derived by Harsanyi and the utilitarian theorem for Alt's utility function derived by Harvey hold simultaneously, and prove that the NM utility function coincides with Alt's utility function under this setup. This result is so paradoxical that we must presume that at least one of the utilitarian theorems contains a strong assumption. We examine the assumptions one by one and conclude that one of Harsanyi's axioms is strong.
Aiding reinforcement learning for set point control

While reinforcement learning has made great improvements, state-of-the-art algorithms can still struggle with seemingly simple set-point feedback control problems. One reason for this is that the learned controller may not be able to excite the system dynamics well enough initially, and therefore it can take a long time to get data that is informative enough to learn for good control. The paper contributes by augmentation of reinforcement learning with a simple guiding feedback controller, for example, a proportional controller. The key advantage in set point control is a much improved excitation that improves the convergence properties of the reinforcement learning controller significantly. This can be very important in real-world control where quick and accurate convergence is needed. The proposed method is evaluated with simulation and on a real-world double tank process with promising results.
A Method for Affine Invariant Feature Extraction with High Quality for Wide Baseline Stereo Image Matching

Wide baseline stereo image matching is a challenging problem in image processing and computer vision. For the purpose of wide baseline stereo image matching, a filter strategy of affine invariant feature based on information content and spatial distribution constraints is put forward. Moreover, a new feature extraction algorithm integrating MSER, SIFT and this filter method is discussed, in order to realize high quality (high information content, good spatial distribution, high repeatability) affine invariant feature extraction. The experiments by using of actual wide baseline images sequence with different texture features prove that this method guarantees much better repeatability, which benefits the subsequent stereo image matching.
Optimal radio labelings of the Cartesian product of the generalized Peterson graph and tree

A radio labeling of a graph $G$ is a function $f : V(G) \rightarrow \{0,1,2,\ldots\}$ such that $|f(u)-f(v)| \geq diam(G) + 1 - d(u,v)$ for every pair of distinct vertices $u,v$ of $G$. The radio number of $G$, denoted by $rn(G)$, is the smallest number $k$ such that $G$ has radio labeling $f$ with max$\{f(v):v \in V(G)\} = k$. In this paper, we give a lower bound for the radio number for the Cartesian product of the generalized Petersen graph and tree. We present two necessary and sufficient conditions, and three other sufficient conditions to achieve the lower bound. Using these results, we determine the radio number for the Cartesian product of the Peterson graph and stars.
PHYSICS PROGRAMME OF NEXT MICE STEP IV

The International Muon Ionization Cooling Experiment is progressing towards a full demonstration of the feasibility of ionization cooling technology decisive for neutrino physics and muon colliders. Step IV should provide the first precise measurements of emittances and first evidence of cooling. The components required for Step IV, including spectrometer solenoids, muon trackers and absorber-FC (focus coil) modules have been assembled with data collection expected in 2015. The physics programme of this Step will be described in detail, which includes LiH and a few other promising absorber materials of different shapes.
Decouple Graph Neural Networks: Train Multiple Simple GNNs Simultaneously Instead of One

Graph neural networks (GNN) suffer from severe inefficiency. It is mainly caused by the exponential growth of node dependency with the increase of layers. It extremely limits the application of stochastic optimization algorithms so that the training of GNN is usually time-consuming. To address this problem, we propose to decouple a multi-layer GNN as multiple simple modules for more efficient training, which is comprised of classical forward training (FT)and designed backward training (BT). Under the proposed framework, each module can be trained efficiently in FT by stochastic algorithms without distortion of graph information owing to its simplicity. To avoid the only unidirectional information delivery of FT and sufficiently train shallow modules with the deeper ones, we develop a backward training mechanism that makes the former modules perceive the latter modules. The backward training introduces the reversed information delivery into the decoupled modules as well as the forward information delivery. To investigate how the decoupling and greedy training affect the representational capacity, we theoretically prove that the error produced by linear modules will not accumulate on unsupervised tasks in most cases. The theoretical and experimental results show that the proposed framework is highly efficient with reasonable performance.
A primer on getting neologisms from foreign languages to under-resourced languages

Mainly due to lack of support, most under-resourced languages have a reduced lexicon in most realms and domains of increasing importance, then their speakers need to significantly augment it. Although neologisms should arise from the languages themselves, external sources are widely accepted. However, we dispute the"common sense"of using the imposed official languages, which are highly probably a legacy of colonialism, as the only source, and we propose to introduce neologisms from any language as long as these neologisms"sound like"native words of the target languages.
Leveraging Static Analysis for Bug Repair

We propose a method combining machine learning with a static analysis tool (i.e. Infer) to automatically repair source code. Machine Learning methods perform well for producing idiomatic source code. However, their output is sometimes difficult to trust as language models can output incorrect code with high confidence. Static analysis tools are trustable, but also less flexible and produce non-idiomatic code. In this paper, we propose to fix resource leak bugs in IR space, and to use a sequence-to-sequence model to propose fix in source code space. We also study several decoding strategies, and use Infer to filter the output of the model. On a dataset of CodeNet submissions with potential resource leak bugs, our method is able to find a function with the same semantics that does not raise a warning with around 97% precision and 66% recall.
Learning Sparse and Low-Rank Priors for Image Recovery via Iterative Reweighted Least Squares Minimization

We introduce a novel optimization algorithm for image recovery under learned sparse and low-rank constraints, which we parameterize as weighted extensions of the $\ell_p^p$-vector and $\mathcal S_p^p$ Schatten-matrix quasi-norms for $0\! <p\! \le1$, respectively. Our proposed algorithm generalizes the Iteratively Reweighted Least Squares (IRLS) method, used for signal recovery under $\ell_1$ and nuclear-norm constrained minimization. Further, we interpret our overall minimization approach as a recurrent network that we then employ to deal with inverse low-level computer vision problems. Thanks to the convergence guarantees that our IRLS strategy offers, we are able to train the derived reconstruction networks using a memory-efficient implicit back-propagation scheme, which does not pose any restrictions on their effective depth. To assess our networks' performance, we compare them against other existing reconstruction methods on several inverse problems, namely image deblurring, super-resolution, demosaicking and sparse recovery. Our reconstruction results are shown to be very competitive and in many cases outperform those of existing unrolled networks, whose number of parameters is orders of magnitude higher than that of our learned models.
Achieving Goals using Reward Shaping and Curriculum Learning

Real-time control for robotics is a popular research area in the reinforcement learning community. Through the use of techniques such as reward shaping, researchers have managed to train online agents across a multitude of domains. Despite these advances, solving goal-oriented tasks still requires complex architectural changes or hard constraints to be placed on the problem. In this article, we solve the problem of stacking multiple cubes by combining curriculum learning, reward shaping, and a high number of efficiently parallelized environments. We introduce two curriculum learning settings that allow us to separate the complex task into sequential sub-goals, hence enabling the learning of a problem that may otherwise be too difficult. We focus on discussing the challenges encountered while implementing them in a goal-conditioned environment. Finally, we extend the best configuration identified on a higher complexity environment with differently shaped objects.