Miracle-dz/UniFAR-FTUs huggingface dataset:
Lyapunov based Stochastic Stability of Human-Machine Interaction: A Quantum Decision System Approach

In mathematical psychology, decision makers are modeled using the Lindbladian equations from quantum mechanics to capture important human-centric features such as order effects and violation of the sure thing principle. We consider human-machine interaction involving a quantum decision maker (human) and a controller (machine). Given a sequence of human decisions over time, how can the controller dynamically provide input messages to adapt these decisions so as to converge to a specific decision? We show via novel stochastic Lyapunov arguments how the Lindbladian dynamics of the quantum decision maker can be controlled to converge to a specific decision asymptotically. Our methodology yields a useful mathematical framework for human-sensor decision making. The stochastic Lyapunov results are also of independent interest as they generalize recent results in the literature.
Evaluating the Text-to-SQL Capabilities of Large Language Models

We perform an empirical evaluation of Text-to-SQL capabilities of the Codex language model. We find that, without any finetuning, Codex is a strong baseline on the Spider benchmark; we also analyze the failure modes of Codex in this setting. Furthermore, we demonstrate on the GeoQuery and Scholar benchmarks that a small number of in-domain examples provided in the prompt enables Codex to perform better than state-of-the-art models finetuned on such few-shot examples.
Quantum approximate optimization algorithm for qudit systems

A frequent starting point of quantum computation platforms are two-state quantum systems, i.e., qubits. However, in the context of integer optimization problems, relevant to scheduling optimization and operations research, it is often more resource-efficient to employ quantum systems with more than two basis states, so-called qudits. Here, we discuss the quantum approximate optimization algorithm (QAOA) for qudit systems. We illustrate how the QAOA can be used to formulate a variety of integer optimization problems such as graph coloring problems or electric vehicle (EV) charging optimization. In addition, we comment on the implementation of constraints and describe three methods to include these into a quantum circuit of a QAOA by penalty contributions to the cost Hamiltonian, conditional gates using ancilla qubits, and a dynamical decoupling strategy. Finally, as a showcase of qudit-based QAOA, we present numerical results for a charging optimization problem mapped onto a max-$k$-graph coloring problem. Our work illustrates the flexibility of qudit systems to solve integer optimization problems.
What makes useful auxiliary tasks in reinforcement learning: investigating the effect of the target policy

Auxiliary tasks have been argued to be useful for representation learning in reinforcement learning. Although many auxiliary tasks have been empirically shown to be effective for accelerating learning on the main task, it is not yet clear what makes useful auxiliary tasks. Some of the most promising results are on the pixel control, reward prediction, and the next state prediction auxiliary tasks; however, the empirical results are mixed, showing substantial improvements in some cases and marginal improvements in others. Careful investigations of how auxiliary tasks help the learning of the main task is necessary. In this paper, we take a step studying the effect of the target policies on the usefulness of the auxiliary tasks formulated as general value functions. General value functions consist of three core elements: 1) policy 2) cumulant 3) continuation function. Our focus on the role of the target policy of the auxiliary tasks is motivated by the fact that the target policy determines the behavior about which the agent wants to make a prediction and the state-action distribution that the agent is trained on, which further affects the main task learning. Our study provides insights about questions such as: Does a greedy policy result in bigger improvement gains compared to other policies? Is it best to set the auxiliary task policy to be the same as the main task policy? Does the choice of the target policy have a substantial effect on the achieved performance gain or simple strategies for setting the policy, such as using a uniformly random policy, work as well? Our empirical results suggest that: 1) Auxiliary tasks with the greedy policy tend to be useful. 2) Most policies, including a uniformly random policy, tend to improve over the baseline. 3) Surprisingly, the main task policy tends to be less useful compared to other policies.
Influence of music on empirical brain networks

We analyze the influence of music in a network of FitzHugh-Nagumo oscillators with empirical structural connectivity measured in healthy human subjects. We report an increase of coherence between the global dynamics in our network and the input signal induced by a specific music song. We show that the level of coherence depends crucially on the frequency band. We compare our results with experimental data, which also describe global neural synchronization between different brain regions in the gamma-band range and its increase just before transitions between different parts of the musical form (musical high-level events). The results also suggest a split in musical form-related brain synchronization between high brain frequencies, associated with neocortical activity, and low frequencies in the range of dance movements, associated with interactivity between cortical and subcortical regions. Finally, we discuss the general modalities of the influence of music on the human brain.
Monarch: Expressive Structured Matrices for Efficient and Accurate Training

Large neural networks excel in many domains, but they are expensive to train and fine-tune. A popular approach to reduce their compute or memory requirements is to replace dense weight matrices with structured ones (e.g., sparse, low-rank, Fourier transform). These methods have not seen widespread adoption (1) in end-to-end training due to unfavorable efficiency--quality tradeoffs, and (2) in dense-to-sparse fine-tuning due to lack of tractable algorithms to approximate a given dense weight matrix. To address these issues, we propose a class of matrices (Monarch) that is hardware-efficient (they are parameterized as products of two block-diagonal matrices for better hardware utilization) and expressive (they can represent many commonly used transforms). Surprisingly, the problem of approximating a dense weight matrix with a Monarch matrix, though nonconvex, has an analytical optimal solution. These properties of Monarch matrices unlock new ways to train and fine-tune sparse and dense models. We empirically validate that Monarch can achieve favorable accuracy-efficiency tradeoffs in several end-to-end sparse training applications: speeding up ViT and GPT-2 training on ImageNet classification and Wikitext-103 language modeling by 2x with comparable model quality, and reducing the error on PDE solving and MRI reconstruction tasks by 40%. In sparse-to-dense training, with a simple technique called"reverse sparsification,"Monarch matrices serve as a useful intermediate representation to speed up GPT-2 pretraining on OpenWebText by 2x without quality drop. The same technique brings 23% faster BERT pretraining than even the very optimized implementation from Nvidia that set the MLPerf 1.1 record. In dense-to-sparse fine-tuning, as a proof-of-concept, our Monarch approximation algorithm speeds up BERT fine-tuning on GLUE by 1.7x with comparable accuracy.
Provable concept learning for interpretable predictions using variational inference

In safety critical applications, practitioners are reluctant to trust neural networks when no interpretable explanations are available. Many attempts to provide such explanations revolve around pixel level attributions or use previously known concepts. In this paper we aim to provide explanations by provably identifying high-level, previously unknown concepts . To this end, we pro-pose a probabilistic modeling framework to derive (C)oncept (L)earning and (P)rediction (CLAP) – a VAE-based classiﬁer that uses visually interpretable concepts as linear predictors. Assuming that the data generating mechanism involves predictive concepts, we prove that our method is able to identify them while attaining optimal classiﬁcation accuracy. We use synthetic experiments for validation, and also show that on real-world (PlantVillage and ChestXRay) datasets, CLAP eﬀectively discovers interpretable factors for classifying diseases.
Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation

We consider unsupervised domain adaptation (UDA), where labeled data from a source domain (e.g., photographs) and unlabeled data from a target domain (e.g., sketches) are used to learn a classifier for the target domain. Conventional UDA methods (e.g., domain adversarial training) learn domain-invariant features to improve generalization to the target domain. In this paper, we show that contrastive pre-training, which learns features on unlabeled source and target data and then fine-tunes on labeled source data, is competitive with strong UDA methods. However, we find that contrastive pre-training does not learn domain-invariant features, diverging from conventional UDA intuitions. We show theoretically that contrastive pre-training can learn features that vary subtantially across domains but still generalize to the target domain, by disentangling domain and class information. Our results suggest that domain invariance is not necessary for UDA. We empirically validate our theory on benchmark vision datasets.
DFNet: Enhance Absolute Pose Regression with Direct Feature Matching

We introduce a camera relocalization pipeline that combines absolute pose regression (APR) and direct feature matching. By incorporating exposure-adaptive novel view synthesis, our method successfully addresses photometric distortions in outdoor environments that existing photometric-based methods fail to handle. With domain-invariant feature matching, our solution improves pose regression accuracy using semi-supervised learning on unlabeled data. In particular, the pipeline consists of two components: Novel View Synthesizer and DFNet. The former synthesizes novel views compensating for changes in exposure and the latter regresses camera poses and extracts robust features that close the domain gap between real images and synthetic ones. Furthermore, we introduce an online synthetic data generation scheme. We show that these approaches effectively enhance camera pose estimation both in indoor and outdoor scenes. Hence, our method achieves a state-of-the-art accuracy by outperforming existing single-image APR methods by as much as 56%, comparable to 3D structure-based methods.
Classical dynamics for loop gravity: The two-vertex model

The study of toy models in loop quantum gravity (LQG), deﬁned as truncations of the full theory, is relevant to both the development of the LQG phenomenology, in cosmology and astrophysics, and the progress towards the resolution of the open issues of the theory, in particular the implementation of the dynamics. Here, we study the dynamics of spin network states of quantum geometry deﬁned on the family of graphs consisting in 2 vertices linked by an arbitrary number of edges, or 2-vertex model in short. A symmetry reduced sector of this model –to isotropic and homogeneous geometries– was successfully studied in the past, where interesting cosmological insights were found. We now study the evolution of the classical trajectories for this system in the general case, for arbitrary number of edges with random initial conﬁgurations. We use the spinorial formalism and its clear interpretation of spin networks in terms of discrete twisted geometries, with the quantum 3d space made of superpositions of polyhedra glued together by faces of equal area. Remarkably, oscillatory and divergent regimes are found with a universal dependence on the coupling constants of the Hamiltonian and independent of the initial spinors or the number of edges. Furthermore, we explore the evolution of the associated polyhedra as well as their volumes and areas.
Effect of Balancing Data Using Synthetic Data on the Performance of Machine Learning Classifiers for Intrusion Detection in Computer Networks

Attacks on computer networks have increased significantly in recent days, due in part to the availability of sophisticated tools for launching such attacks as well as the thriving underground cyber-crime economy to support it. Over the past several years, researchers in academia and industry used machine learning (ML) techniques to design and implement Intrusion Detection Systems (IDSes) for computer networks. Many of these researchers used datasets collected by various organizations to train ML classifiers for detecting intrusions. In many of the datasets used in training ML classifiers in such systems, data are imbalanced (i.e., not all classes had equal number of samples). ML classifiers trained with such imbalanced datasets may produce unsatisfactory results. Traditionally, researchers used over-sampling and under-sampling for balancing data in datasets to overcome this problem. In this work, in addition to random over-sampling, we also used a synthetic data generation method, called Conditional Generative Adversarial Network (CTGAN), to balance data and study their effect on the performance of various widely used ML classifiers. To the best of our knowledge, no one else has used CTGAN to generate synthetic samples to balance intrusion detection datasets. Based on extensive experiments using widely used datasets NSL-KDD and UNSW-NB15, we found that training ML classifiers on datasets balanced with synthetic samples generated by CTGAN increased their prediction accuracy by up to 8% and improved their MCC score by up to 13%, compared to training the same ML classifiers over imbalanced datasets. We also show that this approach consistently performs better than some of the recently proposed state-of-the-art IDSes on both datasets. Our experiments also demonstrate that the accuracy of some ML classifiers trained over datasets balanced with random over-sampling decline compared to the same ML classifiers trained over original imbalanced dataset.
Bursts of cooperation triggered by external stimuli in violent and other uncertain situations

Individuals are reluctant to cooperate for public goods if others freeride on their eﬀorts. Cooperation can be fostered by mechanisms that make beneﬁts exceed costs, such as norms with sanctions, but these take time to develop and may be ineﬀective for impromptu chal-lenges such as defense against or defeat of opponents. Nevertheless, cooperation can be triggered by situational stimuli, for example the sight of victims or provocations by opponents. An Ising spinglass model demonstrates that at a critical level of stimuli, few accidental cooperators entail a burst of cooperation among the rest. If the proportion of defectors is above the critical threshold, however, bursts do not occur, and only a few group members cooperate. The distinc-tion between bursts and ﬁzzles is strongly supported by video data of street violence. Ultimately, the model is generalized by incorporating the usual mechanisms, and can then explain cooperation without stimuli.
Characterization of a MEMS-Fabricated Mixing Device

This paper discusses the fabrication and the characterization of a MEMS-based micromixer. Active perturbations provided by a pressure source/sink system are used to enhance the mixing between two fluid streams. From our preliminary test, it appears that an effective mixing is possible through the generation of chaotic-like distortions at the interface between two streams. A numerical simulation based on a kinematical model reveals a similar behavior under the same condition.
Newsroom convergence in Saudi Press organisations : a qualitative study into four newsrooms of traditional newspapers

Abstract This is the first study of its kind about newsroom convergence and multi-skilled journalists in Saudi newspaper organisations and aimed to fill a gap in the literature about this particular issue in the Saudi context. The study investigated transformations, implications and consequences of technological convergence at four Saudi traditional newspaper organisations; Al-Madina, Alriyadh, Alyaum, and Alwatan. This thesis has explored the particular impacts of online journalistic production in traditional newspaper organisations to identify changes and perhaps challenges occurring in newspaper newsrooms. The study used the observation method in the four newsrooms and in-depth interviews with open ended questions with 60 professionals. The findings confirmed that there are ongoing transformations in the newsrooms. Yet, these transformations are challenged by regulatory, business, and cultural forces. Alyaum was the only newsroom to introduce new integrated newsroom. Journalists are observing and using new communication technologies in the workplace. However, there are difficulties in this process such as tensions in the newsrooms and shortage of qualified and trained journalists in the Saudi media market especially, multiskilled journalists. Despite embracing online and digital technology in news production and disruption, the four Saudi newspapers are still prioritising the traditional print side as it is generating more than 95 % of the annual revenue. Yet, the full integration of newsrooms between online and print newsrooms does not exist in Saudi press organisations.
Analytic beamformer transformation for transfer learning in motion-onset visual evoked potential decoding

Objective. While decoders of electroencephalography-based event-related potentials (ERPs) are routinely tailored to the individual user to maximize performance, developing them on populations for individual usage has proven much more challenging. We propose the analytic beamformer transformation (ABT) to extract phase and/or magnitude information from spatiotemporal ERPs in response to motion-onset stimulation. Approach. We have tested ABT on 52 motion-onset visual evoked potential (mVEP) datasets from 26 healthy subjects and compared the classification accuracy of support vector machine (SVM), spatiotemporal beamformer (stBF) and stepwise linear discriminant analysis (SWLDA) when trained on individual subjects and on a population thereof. Main results. When using phase- and combined phase/magnitude information extracted by ABT, we show significant improvements in accuracy of population-trained classifiers applied to individual users (p < 0.001). We also show that 450 epochs are needed for a correct functioning of ABT, which corresponds to 2 min of paradigm stimulation. Significance. We have shown that ABT can be used to create population-trained mVEP classifiers using a limited number of epochs. We expect this to pertain to other ERPs or synchronous stimulation paradigms, allowing for a more effective, population-based training of visual BCIs. Finally, as ABT renders recordings across subjects more structurally invariant, it could be used for transfer learning purposes in view of plug-and-play BCI applications.
Export Performance of Indian SMEs in Textile Sector: Role of Knowledge Based Resources

In the highly globalized world, SMEs too are actively participating in international business, expanding their sales of goods and services across borders, to different geographical markets. Process of internationalization requires commitment of additional resource. The resource based theory says that for sustained competitive advantage resources should be valuable, rare, inimitable, and non-substitutable. Resources are scarce. Resources and capabilities can be divided into various categories. This study examines the role of knowledge based resources (technological resources and marketing resources) on export performance of SMEs in the Indian textile sector. Data has been taken from the Prowess data base of Center of Monitoring Indian Economy (CMIE). The results show that marketing resources support export performance. The data also highlights the fact that Indian SMEs are making little investment in R&D. Two highly debated areas, size and age have been taken as control variables. Both these variables support the theory of rapid internationalization and that size does not matter, in today's world. This study not only advances the role of resources in internationalization process of SMEs but also provided implications for policy makers and managers. Research on Indian SMEs is limited. Similar future research on SMES may help in developing a parsimonious model.
Changes of breath volatile organic compounds in healthy volunteers following segmental and inhalation endotoxin challenge

It is still unclear how airway inflammation affects the breath volatile organic compounds (VOCs) profile in exhaled air. We therefore analyzed breath following well-defined pulmonary endotoxin (lipopolysaccharide, LPS) challenges. Breath was collected from ten healthy non-smoking subjects at eight time points before and after segmental and whole lung LPS inhalation challenge. Four Tenax-TA® adsorption tubes were simultaneously loaded from an aluminum reservoir cylinder and independently analyzed by two research groups using gas chromatography—mass spectrometry. Airway inflammation was assessed in bronchoalveolar lavage (BAL) and in sputum after segmental and inhaled LPS challenge, respectively. Segmental LPS challenge significantly increased the median (interquartile range, IQR) percentage of neutrophils in BAL from 3.0 (4.2) % to 64.0 (7.3) %. The inhalation challenge increased sputum neutrophils from 33.9 (26.8) % to 78.3 (13.5) %. We observed increases in breath aldehydes at both time points after segmental and inhaled LPS challenge. These results were confirmed by an independent laboratory. The longitudinal breath analysis also revealed distinct VOC patterns related to environmental exposures, clinical procedures, and to metabolic changes after food intake. Changes in breath aldehydes suggest a relationship to LPS induced inflammation compatible with lipid peroxidation processes within the lung. Findings from our longitudinal data highlight the need for future studies to better consider the potential impact of the multiple VOCs from detergents, hygiene or lifestyle products a subject is continuously exposed to. We suspect that this very individual ‘owncloud’ exposure is contributing to an increased variability of breath aldehydes, which might limit a use as inflammatory markers in daily clinical practice.
Parochial cooperation in wild chimpanzees: a model to explain the evolution of parochial altruism

Parochial altruism, taking individual costs to benefit the in-group and harm the out-group, has been proposed as one of the mechanisms underlying the human ability of large-scale cooperation. How parochial altruism has evolved remains unclear. In this review paper, we formulate a parochial cooperation model in small-scale groups and examine the model in wild chimpanzees. As suggested for human parochial altruism, we review evidence that the oxytocinergic system and in-group cooperation and cohesion during out-group threat are integral parts of chimpanzee collective action during intergroup competition. We expand this model by suggesting that chimpanzee parochial cooperation is supported by the social structure of chimpanzee groups which enables repeated interaction history and established social ties between co-operators. We discuss in detail the role of the oxytocinergic system in supporting parochial cooperation, a pathway that appears integral already in chimpanzees. The reviewed evidence suggests that prerequisites of human parochial altruism were probably present in the last common ancestor between Pan and Homo. This article is part of the theme issue ‘Intergroup conflict across taxa’.