Miracle-dz/UniFAR-FTUs huggingface dataset:
Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill Discovery

Deep Reinforcement Learning (RL) has emerged as a powerful paradigm for training neural policies to solve complex control tasks. However, these policies tend to be overfit to the exact specifications of the task and environment they were trained on, and thus do not perform well when conditions deviate slightly or when composed hierarchically to solve even more complex tasks. Recent work has shown that training a mixture of policies, as opposed to a single one, that are driven to explore different regions of the state-action space can address this shortcoming by generating a diverse set of behaviors, referred to as skills, that can be collectively used to great effect in adaptation tasks or for hierarchical planning. This is typically realized by including a diversity term - often derived from information theory - in the objective function optimized by RL. However these approaches often require careful hyperparameter tuning to be effective. In this work, we demonstrate that less widely-used neuroevolution methods, specifically Quality Diversity (QD), are a competitive alternative to information-theory-augmented RL for skill discovery. Through an extensive empirical evaluation comparing eight state-of-the-art algorithms (four flagship algorithms from each line of work) on the basis of (i) metrics directly evaluating the skills' diversity, (ii) the skills' performance on adaptation tasks, and (iii) the skills' performance when used as primitives for hierarchical planning; QD methods are found to provide equal, and sometimes improved, performance whilst being less sensitive to hyperparameters and more scalable. As no single method is found to provide near-optimal performance across all environments, there is a rich scope for further research which we support by proposing future directions and providing optimized open-source implementations.
Measuring the Higgs boson mass with transverse mass variables

We provide a comparative study of the Higgs boson mass measurements based on two approaches to the dileptonic decay of $W$ bosons produced by the Higgs boson decay, one using the kinematic variable ${M}_{T}^{\mathrm{true}}$ and the other using the ${M}_{T2}$-assisted on-shell reconstruction of the invisible neutrino momenta. We find that these two approaches can determine the Higgs boson mass with a similar accuracy for both of the two main production mechanisms of the SM Higgs boson at the LHC, i.e. the gluon-gluon fusion and the weak vector boson fusion. We also notice that the Higgs signal distribution for the gluon-gluon fusion becomes narrower under the ${M}_{T2}$ cut, while the corresponding background distribution becomes flatter, indicating that one might be able to reduce the systematic uncertainties of mass measurement with an appropriate ${M}_{T2}$ cut.
Deep Inventory Management

This work provides a Deep Reinforcement Learning approach to solving a periodic review inventory control system with stochastic vendor lead times, lost sales, correlated demand, and price matching. While this dynamic program has historically been considered intractable, our results show that several policy learning approaches are competitive with or outperform classical methods. In order to train these algorithms, we develop novel techniques to convert historical data into a simulator. On the theoretical side, we present learnability results on a subclass of inventory control problems, where we provide a provable reduction of the reinforcement learning problem to that of supervised learning. On the algorithmic side, we present a model-based reinforcement learning procedure (Direct Backprop) to solve the periodic review inventory control problem by constructing a differentiable simulator. Under a variety of metrics Direct Backprop outperforms model-free RL and newsvendor baselines, in both simulations and real-world deployments.
Risk-averse optimal control of random elliptic variational inequalities

We consider a risk-averse optimal control problem governed by an elliptic variational inequality (VI) subject to random inputs. By deriving KKT-type optimality conditions for a penalised and smoothed problem and studying convergence of the stationary points with respect to the penalisation parameter, we obtain two forms of stationarity conditions. The lack of regularity with respect to the uncertain parameters and complexities induced by the presence of the risk measure give rise to new challenges unique to the stochastic setting. We also propose a path-following stochastic approximation algorithm using variance reduction techniques and demonstrate the algorithm on a modiﬁed benchmark problem.
Magnon bands in pyrochlore slabs with Heisenbergexchange and anisotropies.

The pyrochlore lattice is a versatile venue to probe the properties of magnetically ordered states induced or perturbed by anisotropic terms like the Dzyaloshinskii-Moriya interactions or single-ion anisotropy. Several such ordered states have been investigated recently as precursors of topological magnons and the associated surface states. In parallel, there has been recent progress in growing thin films of magnetic materials with this lattice structure along high symmetry directions of the lattice. In both cases, an account of the magnetic excitations of relevant Hamiltonians for finite slabs is a necessary step in the analysis of the physics of these systems. While the analysis of bulk magnons for these systems is quite common, a direct evaluation of the magnon spectra in the slab geometry, though required, is less frequently encountered. We study here magnon bands in the slab geometry for a class of spin models on the pyrochlore lattice with Heisenberg exchange, Dzyaloshinskii-Moriya interaction and spin-ice anisotropy. For a range of model parameters, for both ferromagnetic and antiferromagnetic exchange, we compute the classical ground states for different slab orientations and determine the spin wave excitations above them. We analyze the ferromagnetic splay phase, the all-in-all-out phase and a coplanar phase and evaluate magnon dispersions for slabs oriented perpendicular to the $[111]$, $[100]$ and $[110]$ directions. For all the phases considered, depending on the slab orientation, magnon band structures can be non-reciprocal and we highlight the differences in the three orientations from this point-of-view. Finally, we present details of the surface localized magnons for all the three slab orientations in the phases we study. For the ferromagnetic splay phase and the all-in-all-out phase we analyze surface states associated with point degeneracies or nodal lines in the bulk spectrum by computing the magnonic Berry curvature and Weyl charges or Chern numbers associated with it.
A Unified Encoder-Decoder Framework with Entity Memory

Entities, as important carriers of real-world knowledge, play a key role in many NLP tasks.We focus on incorporating entity knowledge into an encoder-decoder framework for informative text generation. Existing approaches tried to index, retrieve, and read external documents as evidence, but they suffered from a large computational overhead. In this work, we propose an encoder-decoder framework with an entity memory, namely EDMem. The entity knowledge is stored in the memory as latent representations, and the memory is pre-trained on Wikipedia along with encoder-decoder parameters. To precisely generate entity names, we design three decoding methods to constrain entity generation by linking entities in the memory. EDMem is a unified framework that can be used on various entity-intensive question answering and generation tasks. Extensive experimental results show that EDMem outperforms both memory-based auto-encoder models and non-memory encoder-decoder models.
The hidden quasar nucleus of a WISE-selected, hyperluminous, dust-obscured galaxy at z ~ 2.3

We present the first X-ray spectrum of a hot dust-obscured galaxy (DOG), namely W1835+4355 at z similar to 2.3. Hot DOGs represent a very rare population of hyperluminous (>= 10(47) erg s(-1)), dust-enshrouded objects at z >= 2 recently discovered in the WISE All Sky Survey. The 40 ks XMM-Newton spectrum reveals a continuum as flat (Gamma similar to 0.8) as typically seen in heavily obscured AGN. This, along with the presence of strong Fe K alpha emission, clearly suggests a reflection-dominated spectrum due to Compton-thick absorption. In this scenario, the observed luminosity of L2-10 similar to 2 x 10(44) erg s(-1) is a fraction (<10%) of the intrinsic one, which is estimated to be greater than or similar to 5 x 10(45) erg s(-1) by using several proxies. The Herschel data allow us to constrain the SED up to the sub-mm band, providing a reliable estimate of the quasar contribution (similar to 75%) to the IR luminosity as well as the amount of star formation (similar to 2100 M-circle dot yr(-1)). Our results thus provide additional pieces of evidence that associate Hot DOGs with an exceptionally dusty phase during which luminous quasars and massive galaxies co-evolve and a very efficient and powerful AGN-driven feedback mechanism is predicted by models.
The Role of Lifetime Exposures across Cognitive Domains in Barbados Using Data From the SABE Study

This study characterized the effects of aging on individual cognitive domains and how sex, job type, and years of education alter the age effect on older adults from Barbados. This was an analysis of the cross-sectional data collected as part of the SABE Study (Health, Well-being and Ageing) in 2006. The loss of a single point in each of the individual cognitive domains assessed using the mini-mental state exam served as dependent variables. Independent variables included age, sex, years of education, job type, and the interactions with age in a series of logistic regression analyses. The study aimed to identify which factors altered the effect of age on cognitive performance and which directly affected performance. Results demonstrated that the effect of age differed across the cognitive domains. In addition, sex, education, and job type all differentially affected cognitive performance in an additive, formative manner. The most consistent finding was that high years of education coupled with employment requiring mostly mental effort was the best combination for maintaining high levels of cognitive performance in late life. The results demonstrate that adverse age effects on cognitive performance may be minimized or delayed through modifiable lifetime exposures in the people of Barbados.
Q-LSTM LANGUAGE MODEL - DECENTRALIZED QUANTUM MULTILINGUAL PRE-TRAINED LANGUAGE MODEL FOR PRIVACY PROTECTION

Large-scale language models are trained on a massive amount of natural language data that might encode or reﬂect our private information. With careful manipulation, malicious agents can reverse engineer the training data even if data sanitation and differential privacy algorithms were involved in the pre-training process. In this work, we propose a de-centralized training framework to address privacy concerns in training large-scale language models. The framework consists of a cloud quantum language model built with Variational Quantum Classiﬁers (VQC) for sentence embedding and a local Long-Short Term Memory (LSTM) model. We use both intrinsic evaluation (loss, perplexity) and extrinsic evaluation (downstream sentiment analysis task) to evaluate the performance of our quantum language model. Our quantum model was comparable to its classical counterpart on all the above metrics. We also perform ablation studies to look into the effect of the size of VQC and the size of training data on the performance of the model. Our approach solves privacy concerns without sacriﬁcing downstream task performance. The intractability of quantum operations on classical hardware ensures the conﬁdentiality of the training data and makes it impossible to be recovered by any adversary.
Accelerated Molecular Vibrational Decay and Suppressed Electronic Nonlinearities in Plasmonic Cavities through Coherent Raman Scattering

Molecular vibrations and their dynamics are of outstanding importance for electronic and thermal transport in nanoscale devices as well as for molecular catalysis. The vibrational dynamics of <100 molecules are studied through three-colour time-resolved coherent anti-Stokes Raman spectroscopy (trCARS) using plasmonic nanoantennas. This isolates molecular signals from four-wave mixing (FWM), while using exceptionally low nanowatt powers to avoid molecular damage via single-photon lock-in detection. FWM is found to be strongly suppressed in nm-wide plasmonic gaps compared to plasmonic nanoparticles. The ultrafast vibrational decay rates of biphenyl-4-thiol molecules are accelerated ten-fold by a transient rise in local non-equilibrium temperature excited by enhanced, pulsed optical fields within these plasmonic nanocavities. Separating the contributions of vibrational population decay and dephasing carefully explores the vibrational decay channels of these tightly confined molecules. Such extreme plasmonic enhancement within nanogaps opens up prospects for measuring single-molecule vibrationally-coupled dynamics and diverse molecular optomechanics phenomena.
Reheating in a brane monodromy inflation model

We study reheating in a recently proposed brane ``monodromy inflation'' model in which the inflaton is the position of a D4 brane on a ``twisted torus''. Specifically, we study the repeated collisions between the D4 brane and a D6 brane (on which the Standard Model fields are assumed to be localized) at a fixed position along the monodromy direction as the D4 brane rolls down its potential. We find that there is no trapping of the rolling D4 brane until it reaches the bottom of its potential, and that reheating is entirely described by the last brane encounter. Previous collisions have negligible effect on the brane velocity and hence on the reheat temperature. In the context of our setup, reheating is efficient and the reheat temperature is therefore high.
CommsVAE: Learning the brain's macroscale communication dynamics using coupled sequential VAEs

Communication within or between complex systems is commonplace in the natural sciences and fields such as graph neural networks. The brain is a perfect example of such a complex system, where communication between brain regions is constantly being orchestrated. To analyze communication, the brain is often split up into anatomical regions that each perform certain computations. These regions must interact and communicate with each other to perform tasks and support higher-level cognition. On a macroscale, these regions communicate through signal propagation along the cortex and along white matter tracts over longer distances. When and what types of signals are communicated over time is an unsolved problem and is often studied using either functional or structural data. In this paper, we propose a non-linear generative approach to communication from functional data. We address three issues with common connectivity approaches by explicitly modeling the directionality of communication, finding communication at each timestep, and encouraging sparsity. To evaluate our model, we simulate temporal data that has sparse communication between nodes embedded in it and show that our model can uncover the expected communication dynamics. Subsequently, we apply our model to temporal neural data from multiple tasks and show that our approach models communication that is more specific to each task. The specificity of our method means it can have an impact on the understanding of psychiatric disorders, which are believed to be related to highly specific communication between brain regions compared to controls. In sum, we propose a general model for dynamic communication learning on graphs, and show its applicability to a subfield of the natural sciences, with potential widespread scientific impact.
IDPL: Intra-subdomain adaptation adversarial learning segmentation method based on Dynamic Pseudo Labels

Unsupervised domain adaptation(UDA) has been applied to image semantic segmentation to solve the problem of domain offset. However, in some difficult categories with poor recognition accuracy, the segmentation effects are still not ideal. To this end, in this paper, Intra-subdomain adaptation adversarial learning segmentation method based on Dynamic Pseudo Labels(IDPL) is proposed. The whole process consists of 3 steps: Firstly, the instance-level pseudo label dynamic generation module is proposed, which fuses the class matching information in global classes and local instances, thus adaptively generating the optimal threshold for each class, obtaining high-quality pseudo labels. Secondly, the subdomain classifier module based on instance confidence is constructed, which can dynamically divide the target domain into easy and difficult subdomains according to the relative proportion of easy and difficult instances. Finally, the subdomain adversarial learning module based on self-attention is proposed. It uses multi-head self-attention to confront the easy and difficult subdomains at the class level with the help of generated high-quality pseudo labels, so as to focus on mining the features of difficult categories in the high-entropy region of target domain images, which promotes class-level conditional distribution alignment between the subdomains, improving the segmentation performance of difficult categories. For the difficult categories, the experimental results show that the performance of IDPL is significantly improved compared with other latest mainstream methods.
Towards anisotropic cosmology in group field theory

In cosmological group field theory (GFT) models for quantum gravity coupled to a massless scalar field the total volume, seen as a function of the scalar field, follows the classical Friedmann dynamics of a flat Friedmann–Lemaître–Robertson–Walker Universe at low energies while resolving the Big Bang singularity at high energies. An open question is how to generalise these results to other homogeneous cosmologies. Here we take the first steps towards studying anisotropic Bianchi models in GFT, based on the introduction of a new anisotropy observable analogous to the β variables in Misner’s parametrisation. In a classical Bianchi I spacetime, β behaves as a massless scalar field and can be used as a (gravitational) relational clock. We construct a GFT model for which in an expanding Universe β initially behaves like its classical analogue before ‘decaying’ showing a previously studied isotropisation. We support numerical results in GFT by analytical approximations in a toy model. One possible outcome of our work is a definition of relational dynamics in GFT that does not require matter.
A Closer Look at Hardware-Friendly Weight Quantization

Quantizing a Deep Neural Network (DNN) model to be used on a custom accelerator with efficient fixed-point hardware implementations, requires satisfying many stringent hardware-friendly quantization constraints to train the model. We evaluate the two main classes of hardware-friendly quantization methods in the context of weight quantization: the traditional Mean Squared Quantization Error (MSQE)-based methods and the more recent gradient-based methods. We study the two methods on MobileNetV1 and MobileNetV2 using multiple empirical metrics to identify the sources of performance differences between the two classes, namely, sensitivity to outliers and convergence instability of the quantizer scaling factor. Using those insights, we propose various techniques to improve the performance of both quantization methods - they fix the optimization instability issues present in the MSQE-based methods during quantization of MobileNet models and allow us to improve validation performance of the gradient-based methods by 4.0% and 3.3% for MobileNetV1 and MobileNetV2 on ImageNet respectively.
Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practice

Recent years have seen growing interest among both researchers and practitioners in user-engaged approaches to algorithm auditing, which directly engage users in detecting problematic behaviors in algorithmic systems. However, we know little about industry practitioners’ current practices and challenges around user-engaged auditing, nor what opportunities exist for them to better leverage such approaches in practice. To investigate, we conducted a series of interviews and iterative co-design activities with practitioners who employ user-engaged auditing approaches in their work. Our findings reveal several challenges practitioners face in appropriately recruiting and incentivizing user auditors, scaffolding user audits, and deriving actionable insights from user-engaged audit reports. Furthermore, practitioners shared organizational obstacles to user-engaged auditing, surfacing a complex relationship between practitioners and user auditors. Based on these findings, we discuss opportunities for future HCI research to help realize the potential (and mitigate risks) of user-engaged auditing in industry practice.
SPECTRUM-DRIVEN PLANETARY DEGLACIATION DUE TO INCREASES IN STELLAR LUMINOSITY

Distant planets in globally ice-covered, “snowball,” states may depend on increases in their host stars' luminosity to become hospitable for surface life. Using a general circulation model, we simulated the equilibrium climate response of a planet to a range of instellations from an F-, G-, or M-dwarf star. The range of instellation that permits both complete ice cover and at least partially ice-free climate states is a measure of the climate hysteresis that a planet can exhibit. An ice-covered planet with high climate hysteresis would show a higher resistance to the initial loss of surface ice coverage with increases in instellation, and abrupt, extreme ice loss once deglaciation begins. Our simulations indicate that the climate hysteresis depends sensitively on the host star spectral energy distribution. Under fixed CO2 conditions, a planet orbiting an M-dwarf star exhibits a smaller climate hysteresis, requiring smaller instellation to initiate deglaciation than planets orbiting hotter, brighter stars. This is due to the higher absorption of near-infrared radiation by ice on the surfaces and greenhouse gases and clouds in the atmosphere of an M-dwarf planet. Increases in atmospheric CO2 further lower the climate hysteresis, as M-dwarf snowball planets exhibit a larger radiative response than G-dwarf snowball planets for the same increase in CO2. For a smaller hysteresis, planets near the outer edge of the habitable zone will thaw earlier in their evolutionary history, and will experience a less abrupt transition out of global ice cover.
Approximate Methods for Bayesian Computation

Rich data generating mechanisms are ubiquitous in this age of information and require complex statistical models to draw meaningful inference. While Bayesian analysis has seen enormous development in the last 30 years, benefitting from the impetus given by the successful application of Markov chain Monte Carlo (MCMC) sampling, the combination of big data and complex models conspire to produce significant challenges for the traditional MCMC algorithms. We review modern algorithmic developments addressing the latter and compare their performance using numerical experiments. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 10 is March 2023. Please see http://www.annualreviews. org/page/journal/pubdates for revised estimates.