Miracle-dz/UniFAR-FTUs huggingface dataset:
Verb argument structure overgeneralisations for the English intransitive and transitive constructions: grammaticality judgments and production priming

We used a multi-method approach to investigate how children avoid (or retreat from) argument structure overgeneralisation errors (e.g., *You giggled me). Experiment 1 investigated how semantic and statistical constraints (preemption and entrenchment) influence children’s and adults’ judgments of the grammatical acceptability of 120 verbs in transitive and intransitive sentences. Experiment 2 used syntactic priming to elicit overgeneralisation errors from children (aged 5–6) to investigate whether the same constraints operate in production. For judgments, the data showed effects of preemption, entrenchment, and semantics for all ages. For production, only an effect of preemption was observed, and only for transitivisation errors with intransitive-only verbs (e.g., *The man laughed the girl). We conclude that preemption, entrenchment, and semantic effects are real, but are obscured by particular features of the present production task.
Deep Reinforcement Learning for Finance and the Efficient Market Hypothesis

Is there an informational gain by training a Deep Reinforcement Learning agent for automated stock trading using other time series than the one to be traded? In this work, we implement a DRL algorithm in a solid framework within a model-free and actor-critic approach and learn it with 21 global Multi Assets to predict and trade on the S&amp;P 500. The Efficient Market Hypothesis sets out that it is impossible to gather more information from the broader input. We demand to learn a DRL agent on this index with and without the additional information of these several Multi Assets to determine if the agent could capture invisible dependencies to end up with an informational gain and a better performance. <br>The aim of this work is not to tune the hyperparameters of a DRL agent; several papers already exist on this subject. Nevertheless, we use a proven setup as model architecture. We take a Multi Layer Perceptron (short: MLP) as the neural network architecture with two hidden layers and 64 neurons each layer. The activation function used is the hyperbolic tangent. Further, Proximal Policy Optimization (short: PPO) is used as the policy for simple implementation and enabling a continuous state space. To deal with uncertainties of neural nets, we learn 100 agents for each scenario and compared both results. Neither the Sharpe ratios nor the cumulative returns are better in the more complex approach with the additional information of the Multi Assets, and even the single approach performed marginally better. However, we demonstrate that the complexly learned agent delivers less scattering over the 100 simulations in terms of the risk-adjusted returns, so there is an informational gain due to Multi Assets. A DRL agent learned with additional information delivers more robust results compared to the taken risk. We deliver valuable results for the further development of Deep Reinforcement Learning and provide a unique and resourceful approach.
The Effect of Self-Control and Financial Literacy on Impulse Borrowing: Experimental Evidence

This paper examines the effect of reduced self-control on impulsive borrowing in a laboratory experiment. We manipulate self-control using an ego depletion task and show that it is effective. Following the ego depletion task, participants can anonymously buy hot drinks on credit. We find no significant average effects, but find that treated individuals that have low financial literacy are more likely to borrow impulsively. We complement our experimental analysis with survey evidence that suggests that people with low self-control have more problems with the repayment of consumer debt. This relationship is, in line with the experimental results, weaker for individuals with high financial literacy.
A Closed-form Pricing Solution for Options on Assets with Pricing Errors

This paper examines the effects that pricing errors in the underlying asset have on options prices, <br>their Greeks, and their implied risk neutral densities. Pricing errors can be viewed as a random <br>proportional transaction cost. When pricing errors are information-unrelated, options prices are <br>unambiguously higher than the Black-Scholes case and increasing in the pricing error variance. <br>Hedging volatility is higher and the optimal exercise price for American put options is decreased. <br>The option implied risk-neutral density and option Greeks are materially affected, which leads to <br>suboptimal risk management and hedging when pricing errors are not accounted for.
The Impact of Uncertainty on Investment: Empirical Challenges and a New Estimator

This paper proposes a new method for examining the impact on a firm’s investment of uncertainty reflected in its stock-return volatility. We simultaneously address the endogeneity of uncertainty and mismeasurement in Tobin’s q, but earlier empirical work often neglects one of the two issues. Our nonparametric estimates further suggest that the relation between investment and uncertainty is significantly decreasing and strongly concave. This result contrasts with the existing literature that widely adopts linear regressions. Ignoring nonlinearity or measurement error in q can lead to a substantial estimation bias. However, the bias due to the endogeneity of uncertainty is small.
Capital Markets and Short-Termism: Evidence from the Investment Behavior of Public and Private Firms.

We show that, contrary to popular belief, capital markets do not drive short-termism. By comparing public and private European firms in countries with different institutional infrastructures, we show that being listed in the stock market plays a positive role in a firm’s investment policies, which is inconsistent with the notion of short-termism. Additionally, the degree to which public markets encourage investment varies positively with countries’ institutional quality. We reconcile the differences between our findings and large sample U.S. results by showing that highly leveraged firms are over-represented in Sageworks’ U.S. private firm data.
Minimal theory of massive gravity in the light of CMB data and the 
S8
 tension

We investigate the Minimal Theory of Massive Gravity (MTMG) in the light of different observational data sets which are in tension within the $\Lambda$CDM cosmology. In particular, we analyze MTMG model, for the first time, with the Planck-CMB data, and how these precise measurements affect the free parameters of the theory. The MTMG model can affect the CMB power spectrum at large angular scales and cause a suppression on the amplitude of the matter power spectrum. We find that on adding Planck-CMB data, the graviton has a small, positive, but non-zero mass at 68\% confidence level, and from this perspective, we show that the tension between redshift space distortions measurements and Planck-CMB data in the parametric space $S_8 - \Omega_m$ can be resolved within the MTMG scenario. Through a robust and accurate analysis, we find that the $H_0$ tension between the CMB and the local distance ladder measurements still remains but can be reduced to $\sim3.5\sigma$ within the MTMG theory. The MTMG is very well consistent with the CMB observations, and undoubtedly, it can serve as a viable candidate amongst other modified gravity theories.
Robustness and Consistency in Linear Quadratic Control with Predictions

We study the problem of learning-augmented predictive linear quadratic control. Our goal is to design a controller that balances consistency, which measures the competitive ratio when predictions are accurate, and robustness, which bounds the competitive ratio when predictions are inaccurate. We propose a novel λ - conﬁdent controller and prove that it maintains a competitive ratio upper bound of 1 + min { O ( λ 2 ε ) + O (1 − λ ) 2 , O (1) + O ( λ 2 ) } where λ ∈ [0 , 1] is a trust parameter set based on the conﬁdence in the predictions, and ε is the prediction error. Further, we design a self-tuning policy that adaptively learns the trust parameter λ with a regret that depends on ε and the variation of perturbations and predictions.
Behavioral Priors and Dynamics Models: Improving Performance and Domain Transfer in Offline RL

Offline Reinforcement Learning (RL) aims to extract near-optimal policies from imperfect offline data without additional environment interactions. Extracting policies from diverse offline datasets has the potential to expand the range of applicability of RL by making the training process safer, faster, and more streamlined. We investigate how to improve the performance of offline RL algorithms, its robustness to the quality of offline data, as well as its generalization capabilities. To this end, we introduce Offline Model-based RL with Adaptive Behavioral Priors (MABE). Our algorithm is based on the finding that dynamics models, which support within-domain generalization, and behavioral priors, which support cross-domain generalization, are complementary. When combined together, they substantially improve the performance and generalization of offline RL policies. In the widely studied D4RL offline RL benchmark, we find that MABE achieves higher average performance compared to prior model-free and model-based algorithms. In experiments that require cross-domain generalization, we find that MABE outperforms prior methods. Our website is available at https://sites.google. com/berkeley.edu/mabe .
A Hands-on Comparison of DNNs for Dialog Separation Using Transfer Learning from Music Source Separation

This paper describes a hands-on comparison on using state-of-the-art music source separation deep neural networks (DNNs) before and after task-specific fine-tuning for separating speech content from non-speech content in broadcast audio (i.e., dialog separation). The music separation models are selected as they share the number of channels (2) and sampling rate (44.1 kHz or higher) with the considered broadcast content, and vocals separation in music is considered as a parallel for dialog separation in the target application domain. These similarities are assumed to enable transfer learning between the tasks. Three models pre-trained on music (Open-Unmix, Spleeter, and Conv-TasNet) are considered in the experiments, and fine-tuned with real broadcast data. The performance of the models is evaluated before and after fine-tuning with computational evaluation metrics (SI-SIRi, SI-SDRi, 2f-model), as well as with a listening test simulating an application where the non-speech signal is partially attenuated, e.g., for better speech intelligibility. The evaluations include two reference systems specifically developed for dialog separation. The results indicate that pre-trained music source separation models can be used for dialog separation to some degree, and that they benefit from the fine-tuning, reaching a performance close to task-specific solutions.
Anyonic correlation functions in Chern-Simons matter theories

Using a novel relation between the parity even and odd parts of a correlator, we show that the 3-point function of conserved or weakly broken currents in 3d CFT can be obtained from just the free fermion (FF) or the free boson (FB) theory. In the special case of large $N$ Chern-Simons matter theories, we obtain the correlator in terms of a coupling constant dependent ``anyonic phase"factor. This anyonic phase factor was previously obtained in the $2\rightarrow 2$ exact S-matrix result and is consistent with strong-weak duality. By varying the coupling constant the CFT correlator interpolates nicely between the same in the FF and FB theories.
Mungojerrie: Reinforcement Learning of Linear-Time Objectives

Reinforcement learning synthesizes controllers without prior knowledge of the system. At each timestep, a reward is given. The controllers optimize the discounted sum of these rewards. Applying this class of algorithms requires designing a reward scheme, which is typically done manually. The designer must ensure that their intent is accurately captured. This may not be trivial, and is prone to error. An alternative to this manual programming, akin to programming directly in assembly, is to specify the objective in a formal language and have it"compiled"to a reward scheme. Mungojerrie (https://plv.colorado.edu/mungojerrie/) is a tool for testing reward schemes for $\omega$-regular objectives on finite models. The tool contains reinforcement learning algorithms and a probabilistic model checker. Mungojerrie supports models specified in PRISM and $\omega$-automata specified in HOA.
A Two-stage Multi-modal Affect Analysis Framework for Children with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a developmental disorder that influences the communication and social behavior of a person in a way that those in the spectrum have difficulty in perceiving other people's facial expressions, as well as presenting and communicating emotions and affect via their own faces and bodies. Some efforts have been made to predict and improve children with ASD's affect states in play therapy, a common method to improve children's social skills via play and games. However, many previous works only used pre-trained models on benchmark emotion datasets and failed to consider the distinction in emotion between typically developing children and children with autism. In this paper, we present an open-source two-stage multi-modal approach leveraging acoustic and visual cues to predict three main affect states of children with ASD's affect states (positive, negative, and neutral) in real-world play therapy scenarios, and achieved an overall accuracy of 72:40%. This work presents a novel way to combine human expertise and machine intelligence for ASD affect recognition by proposing a two-stage schema.
Application of CNN to a fine segmented scintillator detector for a single particle and neutrino-nucleon event

This paper presents studies on application of convolutional neural network (CNN) to GEANT4 optical simulation data generated with a scintillator detector subdivided into 1 cubic cm, which is designed for the long-baseline neutrino experiment. Classification of interaction, regression of momentum, and segmentation of hits are demonstrated for single particle and neutrino-nucleon interaction events with well established CNN architectures by feeding reconstructed 2D projection images. In the study it is shown that the application of CNN to the 1 cm subdivided scintillator detector can provide a factor about 2 better momentum resolution compared to a standard method, as well as a classification capability of about 94% for the single particle and 70% for the neutrino-nucleon interaction events. Cross-section analyses with CNN is also shown to be feasible.
Automatic Analysis of the Emotional Content of Speech in Daylong Child-Centered Recordings from a Neonatal Intensive Care Unit

Researchers have recently started to study how the emotional speech heard by young infants can affect their developmental outcomes. As a part of this research, hundreds of hours of daylong recordings from preterm infants' audio environments were collected from two hospitals in Finland and Estonia in the context of so-called APPLE study. In order to analyze the emotional content of speech in such a massive dataset, an automatic speech emotion recognition (SER) system is required. However, there are no emotion labels or existing indomain SER systems to be used for this purpose. In this paper, we introduce this initially unannotated large-scale real-world audio dataset and describe the development of a functional SER system for the Finnish subset of the data. We explore the effectiveness of alternative state-of-the-art techniques to deploy a SER system to a new domain, comparing cross-corpus generalization, WGAN-based domain adaptation, and active learning in the task. As a result, we show that the best-performing models are able to achieve a classification performance of 73.4% unweighted average recall (UAR) and 73.2% UAR for a binary classification for valence and arousal, respectively. The results also show that active learning achieves the most consistent performance compared to the two alternatives.
MHNF: Multi-Hop Heterogeneous Neighborhood Information Fusion Graph Representation Learning

The attention mechanism enables graph neural networks (GNNs) to learn the attention weights between the target node and its one-hop neighbors, thereby improving the performance further. However, most existing GNNs are oriented toward homogeneous graphs, and in which each layer can only aggregate the information of one-hop neighbors. Stacking multilayer networks introduces considerable noise and easily leads to over smoothing. We propose here a multihop heterogeneous neighborhood information fusion graph representation learning method (MHNF). Specifically, we propose a hybrid metapath autonomous extraction model to efficiently extract multihop hybrid neighbors. Then, we formulate a hop-level heterogeneous information aggregation model, which selectively aggregates different-hop neighborhood information within the same hybrid metapath. Finally, a hierarchical semantic attention fusion model (HSAF) is constructed, which can efficiently integrate different-hop and different-path neighborhood information. In this fashion, this paper solves the problem of aggregating multihop neighborhood information and learning hybrid metapaths for target tasks. This mitigates the limitation of manually specifying metapaths. In addition, HSAF can extract the internal node information of the metapaths and better integrate the semantic information present at different levels. Experimental results on real datasets show that MHNF achieves the best or competitive performance against state-of-the-art baselines with only a fraction of 1/10 <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math><alternatives><mml:math><mml:mo>∼</mml:mo></mml:math><inline-graphic xlink:href="zhu-ieq1-3186158.gif"/></alternatives></inline-formula> 1/100 parameters and computational budgets. Our code is publicly available at <uri>https://github.com/PHD-lanyu/MHNF</uri>.
Rhodium-Catalyzed Cyclocarbonylation of Ketimines via C–H Bond Activation

A novel rhodium-catalyzed oxidative cyclocarbonylation of ketimines via cleavage of two C–H bonds was established, which provided a direct and reliable method for the synthesis of a wide range of 3-methyleneisoindolin-1-ones with mostly moderate yields. Preliminary experimental mechanistic studies and DFT calculations revealed that this reaction proceeds via imine–enamine tautomerization, N–H cleavage, C–H bond activation, CO insertion, and reductive elimination. The mechanism studies further ruled out an isolated cyclometalated rhodium complex being involved in the present reaction, which was different from many other documented rhodium-catalyzed C–H cyclization reactions.
Poisoning and Backdooring Contrastive Learning

Multimodal contrastive learning methods like CLIP train on noisy and uncurated training datasets. This is cheaper than labeling datasets manually, and even improves out-of-distribution robustness. We show that this practice makes backdoor and poisoning attacks a significant threat. By poisoning just 0.01% of a dataset (e.g., just 300 images of the 3 million-example Conceptual Captions dataset), we can cause the model to misclassify test images by overlaying a small patch. Targeted poisoning attacks, whereby the model misclassifies a particular test input with an adversarially-desired label, are even easier requiring control of 0.0001% of the dataset (e.g., just three out of the 3 million images). Our attacks call into question whether training on noisy and uncurated Internet scrapes is desirable.
SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies

Generalization has been a long-standing challenge for reinforcement learning (RL). Visual RL, in particular, can be easily distracted by irrelevant factors in high-dimensional observation space. In this work, we consider robust policy learning which targets zero-shot generalization to unseen visual environments with large distributional shift. We propose SECANT, a novel self-expert cloning technique that leverages image augmentation in two stages to decouple robust representation learning from policy optimization. Specifically, an expert policy is first trained by RL from scratch with weak augmentations. A student network then learns to mimic the expert policy by supervised learning with strong augmentations, making its representation more robust against visual variations compared to the expert. Extensive experiments demonstrate that SECANT significantly advances the state of the art in zero-shot generalization across 4 challenging domains. Our average reward improvements over prior SOTAs are: DeepMind Control (+26.5%), robotic manipulation (+337.8%), vision-based autonomous driving (+47.7%), and indoor object navigation (+15.8%). Code release and video are available at https://linxifan.github. io/secant-site/.
Towards bio-inspired unsupervised representation learning for indoor aerial navigation

Aerial navigation in GPS-denied, indoor environments, is still an open challenge. Drones can perceive the environment from a richer set of viewpoints, while having more stringent compute and energy constraints than other autonomous platforms. To tackle that problem, this research displays a biologically inspired deep-learning algorithm for simultaneous localization and mapping (SLAM) and its application in a drone navigation system. We propose an unsupervised representation learning method that yields low-dimensional latent state descriptors, that mitigates the sensitivity to perceptual aliasing, and works on power-efficient, embedded hardware. The designed algorithm is evaluated on a dataset collected in an indoor warehouse environment, and initial results show the feasibility for robust indoor aerial navigation.
Lost in Interpreting: Speech Translation from Source or Interpreter?

Interpreters facilitate multi-lingual meetings but the affordable set of languages is often smaller than what is needed. Automatic simultaneous speech translation can extend the set of provided languages. We investigate if such an automatic system should rather follow the original speaker, or an interpreter to achieve better translation quality at the cost of increased delay. To answer the question, we release Europarl Simultaneous Interpreting Corpus (ESIC), 10 hours of recordings and transcripts of European Parliament speeches in English, with simultaneous interpreting into Czech and German. We evaluate quality and latency of speaker-based and interpreter-based spoken translation systems from English to Czech. We study the differences in implicit simplification and summarization of the human interpreter compared to a machine translation system trained to shorten the output to some extent. Finally, we perform human evaluation to measure information loss of each of these approaches.
A Ramsey–Turán theory for tilings in graphs

For a k$$ k $$ ‐vertex graph F$$ F $$ and an n$$ n $$ ‐vertex graph G$$ G $$ , an F$$ F $$ ‐tiling in G$$ G $$ is a collection of vertex‐disjoint copies of F$$ F $$ in G$$ G $$ . For r∈ℕ$$ r\in \mathbb{N} $$ , the r$$ r $$ ‐independence number of G$$ G $$ , denoted αr(G)$$ {\alpha}_r(G) $$ , is the largest size of a Kr$$ {K}_r $$ ‐free set of vertices in G$$ G $$ . In this article, we discuss Ramsey–Turán‐type theorems for tilings where one is interested in minimum degree and independence number conditions (and the interaction between the two) that guarantee the existence of optimal F$$ F $$ ‐tilings. Our results unify and generalise previous results of Balogh–Molla–Sharifzadeh [Random Struct. Algoritm. 49 (2016), no. 4, 669–693], Nenadov–Pehova [SIAM J. Discret. Math. 34 (2020), no. 2, 1001–1010] and Balogh–McDowell–Molla–Mycroft [Comb. Probab. Comput. 27 (2018), no. 4, 449–474] on the subject.