Miracle-dz/UniFAR-FTUs huggingface dataset:
A Dilemma for Solomonoff Prediction

Abstract The framework of Solomonoff prediction assigns prior probability to hypotheses inversely proportional to their Kolmogorov complexity. There are two well-known problems. First, the Solomonoff prior is relative to a choice of universal Turing machine. Second, the Solomonoff prior is not computable. However, there are responses to both problems. Different Solomonoff priors converge with more and more data. Further, there are computable approximations to the Solomonoff prior. I argue that there is a tension between these two responses. This is because computable approximations to Solomonoff prediction do not always converge.
Solving the capacitated vehicle routing problem with timing windows using rollouts and MAX-SAT

The vehicle routing problem is a well known class of NP-hard combinatorial optimisation problems in literature. Traditional solution methods involve either carefully designed heuristics, or time-consuming metaheuristics. Recent work in reinforcement learning has been a promising alternative approach, but has found it difficult to compete with traditional methods in terms of solution quality. This paper proposes a hybrid approach that combines reinforcement learning, policy rollouts, and a satisfiability solver to enable a tunable tradeoff between computation times and solution quality. Results on a popular public data set show that the algorithm is able to produce solutions closer to optimal levels than existing learning based approaches, and with shorter computation times than meta-heuristics. The approach requires minimal design effort and is able to solve unseen problems of arbitrary scale without additional training. Furthermore, the methodology is generalisable to other combinatorial optimisation problems.
Edge Graph Neural Networks for Massive MIMO Detection

Massive Multiple-Input Multiple-Out (MIMO) detection is an important problem in modern wireless communication systems. While traditional Belief Propagation (BP) detectors perform poorly on loopy graphs, the recent Graph Neural Networks (GNNs)-based method can overcome the drawbacks of BP and achieve superior performance. Nevertheless, direct use of GNN ignores the importance of edge attributes and suffers from high computation overhead using a fully connected graph structure. In this paper, we propose an efficient GNN-inspired algorithm, called the Edge Graph Neural Network (EGNN), to detect MIMO signals. We first compute graph edge weights through channel correlation and then leverage the obtained weights as a metric to evaluate the importance of neighbors of each node. Moreover, we design an adaptive Edge Drop (ED) scheme to sparsify the graph such that computational cost can be significantly reduced. Experimental results demonstrate that our proposed EGNN achieves better or comparable performance to popular MIMO detection methods for different modulation schemes and costs the least detection time compared to GNN-based approaches.
On Provably Robust Meta-Bayesian Optimization

Bayesian optimization (BO) has become popular for sequential optimization of black-box functions. When BO is used to optimize a target function, we often have access to previous evaluations of potentially related functions. This begs the question as to whether we can leverage these previous experiences to accelerate the current BO task through meta-learning (meta-BO), while ensuring robustness against potentially harmful dissimilar tasks that could sabotage the convergence of BO. This paper introduces two scalable and provably robust meta-BO algorithms: robust meta-Gaussian process-upper confidence bound (RM-GP-UCB) and RM-GP-Thompson sampling (RM-GP-TS). We prove that both algorithms are asymptotically no-regret even when some or all previous tasks are dissimilar to the current task, and show that RM-GP-UCB enjoys a better theoretical robustness than RM-GP-TS. We also exploit the theoretical guarantees to optimize the weights assigned to individual previous tasks through regret minimization via online learning, which diminishes the impact of dissimilar tasks and hence further enhances the robustness. Empirical evaluations show that (a) RM-GP-UCB performs effectively and consistently across various applications, and (b) RM-GP-TS, despite being less robust than RM-GP-UCB both in theory and in practice, performs competitively in some scenarios with less dissimilar tasks and is more computationally efficient.
ALMA Reveals Extended Cool Gas and Hot Ionized Outflows in a Typical Star-forming Galaxy at Z = 7.13

We present spatially resolved morphological properties of [C II] 158 μm, [O III] 88 μm, dust, and rest-frame ultraviolet (UV) continuum emission for A1689-zD1, a strongly lensed, sub-L* galaxy at z = 7.13, by utilizing deep Atacama Large Millimeter/submillimeter Array (ALMA) and Hubble Space Telescope (HST) observations. While the [O III] line and UV continuum are compact, the [C II] line is extended up to a radius of r ∼ 12 kpc. Using multi-band rest-frame far-infrared continuum data ranging from 52 to 400 μm, we find an average dust temperature and emissivity index of Tdust=41−14+17 K and β=1.7−0.7+1.1 , respectively, across the galaxy. We find slight differences in the dust continuum profiles at different wavelengths, which may indicate that the dust temperature decreases with distance. We map the star formation rate (SFR) via IR and UV luminosities and determine a total SFR of 37 ± 1M ⊙yr−1 with an obscured fraction of 87%. While the [O III] line is a good tracer of the SFR, the [C II] line shows deviation from the local L [C II]-SFR relations in the outskirts of the galaxy. Finally, we observe a clear difference in the line profile between [C II] and [O III], with significant residuals (∼5σ) in the [O III] line spectrum after subtracting a single Gaussian model. This suggests a possible origin of the extended [C II] structure from the cooling of hot ionized outflows. The extended [C II] and high-velocity [O III] emission may both contribute in part to the high L [O III]/L [C II] ratios recently reported in z > 6 galaxies.
Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch

Branch-and-bound is a systematic enumerative method for combinatorial optimization, where the performance highly relies on the variable selection strategy. State-of-the-art handcrafted heuristic strategies suffer from relatively slow inference time for each selection, while the current machine learning methods require a significant amount of labeled data. We propose a new approach for solving the data labeling and inference latency issues in combinatorial optimization based on the use of the reinforcement learning (RL) paradigm. We use imitation learning to bootstrap an RL agent and then use Proximal Policy Optimization (PPO) to further explore global optimal actions. Then, a value network is used to run Monte-Carlo tree search (MCTS) to enhance the policy network. We evaluate the performance of our method on four different categories of combinatorial optimization problems and show that our approach performs strongly compared to the state-of-the-art machine learning and heuristics based methods.
Four Years of FAccT: A Reflexive, Mixed-Methods Analysis of Research Contributions, Shortcomings, and Future Prospects

Fairness, Accountability, and Transparency (FAccT) for socio-technical systems has been a thriving area of research in recent years. An ACM conference bearing the same name has been the central venue for scholars in this area to come together, provide peer feedback to one another, and publish their work. This reflexive study aims to shed light on FAccT’s activities to date and identify major gaps and opportunities for translating contributions into broader positive impact. To this end, we utilize a mixed-methods research design. On the qualitative front, we develop a protocol for reviewing and coding prior FAccT papers, tracing their distribution of topics, methods, datasets, and disciplinary roots. We also design and administer a questionnaire to reflect the voices of FAccT community members and affiliates on a wide range of topics. On the quantitative front, we use the full text and citation network associated with prior FAccT publications to provide further evidence about topics and values represented in FAccT. We organize the findings from our analysis into four main dimensions: the themes present in FAccT scholarship, the values that underpin the work, the impact of the contributions both within academic circles and beyond, and the practices and informal norms of the community that has formed around FAccT. Finally, our work identifies several suggestions on directions for change, as voiced by community members.
An Attack Resilient PUF-based Authentication Mechanism for Distributed Systems

In most PUF-based authentication schemes, a central server is usually engaged to verify the response of the device’s PUF to challenge bit-streams. However, the server availability may be intermittent in practice. To tackle such an issue, this paper proposes a new protocol for supporting distributed authentication while avoiding vulnerability to information leakage where CRPs could be retrieved from hacked devices and collectively used to model the PUF. The main idea is to provision for scrambling the challenge bit-stream in a way that is dependent on the verifier. The scrambling pattern varies per authentication round for each device and independently across devices. In essence, the scrambling function becomes node- and packetspecific and the response received by two verifiers of one device for the same challenge bit-stream could vary. Thus, neither the scrambling function can be reverted, nor the PUF can be modeled even by a collusive set of malicious nodes. The validation results using data of an FPGA-based implementation demonstrate the effectiveness of our approach in thwarting PUF modeling attacks by collusive actors. We also discuss the approach resiliency against impersonation, Sybil, and reverse engineering attacks.
Solving Invariant Generation for Unsolvable Loops

Automatically generating invariants, key to computer-aided analysis of probabilistic and deterministic programs and compiler optimisation, is a challenging open problem. Whilst the problem is in general undecidable, the goal is settled for restricted classes of loops. For the class of solvable loops, introduced by Kapur and Rodr´ıguez-Carbonell in 2004, one can automatically compute invariants from closed-form solutions of recurrence equations that model the loop behaviour. In this paper we establish a technique for invariant synthesis for loops that are not solvable, termed unsolvable loops. Our approach automatically partitions the program variables and identiﬁes the so-called defective variables that characterise unsolvability. We further present a novel technique that automatically synthesises polynomials, in the defective variables, that admit closed-form solutions and thus lead to polynomial loop invariants. Our implementation and experiments demonstrate both the feasibility and applicability of our approach to both deterministic and probabilistic programs.
Learning Behavior Representations Through Multi-Timescale Bootstrapping

Natural behavior consists of dynamics that are both un-predictable, can switch suddenly, and unfold over many different timescales. While some success has been found in building representations of behavior under constrained or simpliﬁed task-based conditions, many of these models cannot be applied to free and naturalistic settings due to the fact that they assume a single scale of temporal dynamics. In this work, we introduce Bootstrap Across Multiple Scales (BAMS) , a multi-scale representation learning model for behavior: we combine a pooling module that aggregates features extracted over encoders with different temporal receptive ﬁelds, and design a set of latent objectives to bootstrap the representations in each respective space to encourage disentanglement across different timescales. We ﬁrst apply our method on a dataset of quadrupeds navigating in different terrain types, and show that our model captures the temporal complexity of behavior. We then apply our method to the MABe 2022 Multi-agent behavior challenge, where our model ranks 3rd overall and 1st on two subtasks, and show the importance of incorporating multi-timescales when analyzing behavior.
Individual addressing of trapped ion qubits with geometric phase gates

We propose a new scheme for individual addressing of trapped ion qubits, selecting them via their motional frequency. We show that geometric phase gates can perform single-qubit rotations using the coherent interference of spin-independent and (global) spin-dependent forces. The spin-independent forces, which can be generated via localised electric fields, increase the gate speed while reducing its sensitivity to motional decoherence, which we show analytically and numerically. While the scheme applies to most trapped ion experimental setups, we numerically simulate a specific laser-free implementation, showing cross-talk errors below $10^{-6}$ for reasonable parameters.
SpecNet2: Orthogonalization-free spectral embedding by neural networks

Spectral methods which represent data points by eigenvectors of kernel matrices or graph Laplacian matrices have been a primary tool in unsupervised data analysis. In many application scenarios, parametrizing the spectral embedding by a neural network that can be trained over batches of data samples gives a promising way to achieve automatic out-of-sample extension as well as computational scalability. Such an approach was taken in the original paper of SpectralNet (Shaham et al. 2018), which we call SpecNet1. The current paper introduces a new neural network approach, named SpecNet2, to compute spectral embedding which optimizes an equivalent objective of the eigen-problem and removes the orthogonalization layer in SpecNet1. SpecNet2 also allows separating the sampling of rows and columns of the graph affinity matrix by tracking the neighbors of each data point through the gradient formula. Theoretically, we show that any local minimizer of the new orthogonalization-free objective reveals the leading eigenvectors. Furthermore, global convergence for this new orthogonalization-free objective using a batch-based gradient descent method is proved. Numerical experiments demonstrate the improved performance and computational efficiency of SpecNet2 on simulated data and image datasets.
Variance Reduction for Policy-Gradient Methods via Empirical Variance Minimization

Policy-gradient methods in Reinforcement Learning(RL) are very universal and widely applied in practice but their performance suffers from the high variance of the gradient estimate. Several procedures were proposed to reduce it including actor-critic(AC) and advantage actor-critic(A2C) methods. Recently the approaches have got new perspective due to the introduction of Deep RL: both new control variates(CV) and new sub-sampling procedures became available in the setting of complex models like neural networks. The vital part of CV-based methods is the goal functional for the training of the CV, the most popular one is the least-squares criterion of A2C. Despite its practical success, the criterion is not the only one possible. In this paper we for the ﬁrst time investigate the performance of the one called Empirical Variance(EV). We observe in the experiments that not only EV-criterion performs not worse than A2C but sometimes can be considerably better. Apart from that, we also prove some theoretical guarantees of the actual variance reduction under very general assumptions and show that A2C least-squares goal functional is an upper bound for EV goal. Our experiments indicate that in terms of variance reduction EV-based methods are much better than A2C and allow stronger variance reduction. charts representing mean rewards in Unlock environment, normalized by the mean reward of the REINFORCE. The results are averaged over 20 runs. The resulting curves are smoothed with sliding window of size 25.
Deep Variational Implicit Processes

Implicit processes (IPs) are a generalization of Gaussian processes (GPs). IPs may lack a closed-form expression but are easy to sample from. Examples include, among others, Bayesian neural networks or neural samplers. IPs can be used as priors over functions, resulting in flexible models with well-calibrated prediction uncertainty estimates. Methods based on IPs usually carry out function-space approximate inference, which overcomes some of the difficulties of parameter-space approximate inference. Nevertheless, the approximations employed often limit the expressiveness of the final model, resulting, e.g., in a Gaussian predictive distribution, which can be restrictive. We propose here a multi-layer generalization of IPs called the Deep Variational Implicit process (DVIP). This generalization is similar to that of deep GPs over GPs, but it is more flexible due to the use of IPs as the prior distribution over the latent functions. We describe a scalable variational inference algorithm for training DVIP and show that it outperforms previous IP-based methods and also deep GPs. We support these claims via extensive regression and classification experiments. We also evaluate DVIP on large datasets with up to several million data instances to illustrate its good scalability and performance.
Resistive wall tearing mode disruptions in DIII-D and ITER tokamaks

Disruptions are a serious problem in tokamaks, in which thermal and magnetic energy confinement is lost. This paper uses data from the DIII-D experiment, theory, and simulations to demonstrate that resistive wall tearing modes (RWTMs) produce the thermal quench (TQ) in a typical locked mode shot. Analysis of the linear RWTM dispersion relation shows the parameter dependence of the growth rate, particularly on the resistive wall time. Linear simulations of the locked mode equilibrium show that it is unstable with a resistive wall and stable with an ideally conducting wall. Nonlinear simulations demonstrate that the RWTM grows to sufficient amplitude to cause a complete thermal quench. The RWTM growth time is proportional to the thermal quench time. The nonlinearly saturated RWTM magnetic perturbation amplitude agrees with experimental measurements. The onset condition is that the q = 2 rational surface is sufficiently close to the resistive wall. Collectively, this identifies the RWTM as the cause of the TQ. In ITER, RWTMs will produce long TQ times compared to present-day experiments. ITER disruptions may be significantly more benign than previously predicted.
CERT: Continual Pre-Training on Sketches for Library-Oriented Code Generation

Code generation is a longstanding challenge, aiming to generate a code snippet based on a natural language description. Usually, expensive text-code paired data is essential for training a code generation model. Recently, thanks to the success of pre-training techniques, large language models are trained on large unlabelled code corpora and perform well in generating code. In this paper, we investigate how to leverage an unlabelled code corpus to train a model for library-oriented code generation. Since it is a common practice for programmers to reuse third-party libraries, in which case the text-code paired data are harder to obtain due to the huge number of libraries. We observe that library-oriented code snippets are more likely to share similar code sketches. Hence, we present CERT with two steps: a sketcher generates the sketch, then a generator fills the details in the sketch. Both the sketcher and generator are continually pre-trained upon a base model using unlabelled data. Also, we carefully craft two benchmarks to evaluate library-oriented code generation named PandasEval and NumpyEval. Experimental results have shown the impressive performance of CERT. For example, it surpasses the base model by an absolute 15.67% improvement in terms of pass@1 on PandasEval. Our work is available at https://github.com/microsoft/PyCodeGPT.
The Maximum Linear Arrangement for trees under projectivity and planarity

The Maximum Linear Arrangement problem (MaxLA) consists of finding a mapping π from the n vertices of a graph G to distinct consecutive integers that maximizes Dπ(G) = ∑ uv∈E(G) |π(u) − π(v)|. In this setting, vertices are considered to lie on a horizontal line and edges are drawn as semicircles above the line. There exist variants of MaxLA in which the arrangements are constrained. In the planar variant edge crossings are forbidden. In the projective variant for rooted trees arrangements are planar and the root cannot be covered by any edge. Here we present O(n)-time and O(n)-space algorithms that solve Planar and Projective MaxLA for trees. We also prove several properties of maximum projective and planar arrangements.
Sandwich cellularity and a version of cell theory

We explain how the theory of sandwich cellular algebras can be seen as a version of cell theory for algebras. We apply this theory to many examples such as Hecke algebras, and various monoid and diagram algebras. asymptotic Hecke algebras associated to intersections of left and right cells as e.g. in [Lus87]. The sandwich cellular bases are versions of Kazhdan–Lusztig bases in the theory of sandwich cellularity. The most important theorem regarding sandwich cellular algebras is H -reduction , see Theorem 2A.17. H -reduction classiﬁes simple modules of sandwich cellular algebras by using their cells and the sandwiched algebras. In the theory of monoids H -reduction is knows as the celebrated Cliﬀord–Munn–Ponizovski˘ı theorem that classiﬁes simple modules of monoids by Green’s J classes and the simple modules of the H -groups, see [GMS09] or [Ste16] for modern expositions. In the theory of Kazhdan–Lusztig cells the corresponding theorem does not have a name (as far as we know), and is weaker than the Cliﬀord–Munn–Ponizovski˘ı theorem, see Section 3 for details. A variant of the H -reduction for sandwich cellular algebras is the theorem with the same name in categoriﬁcation, see e.g. [MMMZ20] and [MMM + 21], where we got its name from. Note however that this categorical H -reduction is similar in spirit but diﬀerent in nature, which becomes evident when comparing [MMM + 19] with Section 3.
Creating and Operating Areas With Reduced Electromagnetic Field Exposure Thanks to Reconfigurable Intelligent Surfaces

Mobile network operators must provide a target quality of service (QoS) within a target coverage area. Each generation of networks from the 2nd to the 5th has reached higher QoS targets and coverage area sizes. However, the deployment of new networks is sometimes challenged by Electromagnetic Field (EMF) exposure constraints. In this paper, to take into account these constraints, we assess the novel and recent concept of "Reduced EMF Exposure Area". Such an area would be created and operated by a mobile network operator upon the request of its customers. In such an area, customers keep enjoying high data rate internet access while getting a reduced EMF exposure. To ensure this EMF exposure reduction, we propose to deploy Reconfigurable Intelligent Surfaces (RIS) connected to the mobile network and exploit a joint RIS-M-MIMO uplink beamforming scheme. We use our ray-tracing-based simulation tool to visualize and characterize the Reduced EMF Exposure Area in a challenging environment in terms of propagation. Our simulations show that an operator can create and operate such an area under the condition that it carefully places the RIS in the environment.