Reviewerly/exHarmony huggingface dataset:
Characteristics and Distribution of Some Radiation Parameters over Nigeria

This study examined the distribution and characteristics of Surface Solar Radiation Downward (SSRD), sunshine duration, cloudiness index, relative humidity, temperature, and wind speed from the archive of the NASA Power project between the years 2010 and 2020 over Nigeria using 37 stations. The datasets were subjected to monthly and seasonal analysis over three climatic zones in Nigeria. The results show that the SSRD of the Sahel is generally higher than that of the Savannah and Guinea Coast. Sunshine duration was found to increase seasonally and from south to north. From the extreme edge of the coastal region in the southern part of Nigeria to its far northern region, the mean sunshine duration hour increases northward from about 2075 hours over the coastal region of the south to about 3525 hours in the north. Relative humidity fluctuates between 70-90%, which peaks in August and declined back to its minimum (70%) in December. Observed over all seasons, relative humidity decreases northward from the far south near the Atlantic Ocean to the far north of Nigeria. This is in opposition to the pattern of 2m temperature observed, that is, the 2 m temperature spatial pattern increases from the south to the north. In terms of sky conditions classification, over Nigeria, the cloudiness index is generally less than 0.45. Over the Guinea Coast cloudiness index is observed to be higher than in Savannah and the Sahel zones in all the months with the lowest index of 0.42 in March and the highest index of 0.45 in September. However, the northeastern part of Nigeria is observed to be majorly cloudier in JJA and SON seasons than the far southern region of the country. However, wind speed is observed to be significantly lower than 2.7 m/s over the Guinea Coast compared to other zones. Relatively high and strong winds were noticed to be widespread over the extreme part of the northeastern and northwestern parts of Nigeria in all the seasons, making the region more suitable for wind energy generation.
Rethinking Query, Key, and Value Embedding in Vision Transformer under Tiny Model Constraints

A vision transformer (ViT) is the dominant model in the computer vision field. Despite numerous studies that mainly focus on dealing with inductive bias and complexity, there remains the problem of finding better transformer networks. For example, conventional transformer-based models usually use a projection layer for each query (Q), key (K), and value (V) embedding before multi-head self-attention. Insufficient consideration of semantic $Q, K$, and $V$ embedding may lead to a performance drop. In this paper, we propose three types of structures for $Q$, $K$, and $V$ embedding. The first structure utilizes two layers with ReLU, which is a non-linear embedding for $Q, K$, and $V$. The second involves sharing one of the non-linear layers to share knowledge among $Q, K$, and $V$. The third proposed structure shares all non-linear layers with code parameters. The codes are trainable, and the values determine the embedding process to be performed among $Q$, $K$, and $V$. Hence, we demonstrate the superior image classification performance of the proposed approaches in experiments compared to several state-of-the-art approaches. The proposed method achieved $71.4\%$ with a few parameters (of $3.1M$) on the ImageNet-1k dataset compared to that required by the original transformer model of XCiT-N12 ($69.9\%$). Additionally, the method achieved $93.3\%$ with only $2.9M$ parameters in transfer learning on average for the CIFAR-10, CIFAR-100, Stanford Cars datasets, and STL-10 datasets, which is better than the accuracy of $92.2\%$ obtained via the original XCiT-N12 model.
Classical and quantum dynamic programming for Subset-Sum and variants

Subset-Sum is an NP-complete problem where one must decide if a multiset of $n$ integers contains a subset whose elements sum to a target value $m$. The best known classical and quantum algorithms run in time $\tilde{O}(2^{n/2})$ and $\tilde{O}(2^{n/3})$, respectively, based on the well-known meet-in-the-middle technique. Here we introduce a novel dynamic programming data structure with applications to Subset-Sum and a number of variants, including Equal-Sums (where one seeks two disjoint subsets with the same sum), 2-Subset-Sum (a relaxed version of Subset-Sum where each item in the input set can be used twice in the summation), and Shifted-Sums, a generalization of both of these variants, where one seeks two disjoint subsets whose sums differ by some specified value. Given any modulus $p$, our data structure can be constructed in time $O(np)$, after which queries can be made in time $O(n)$ to the lists of subsets summing to a same value modulo $p$. We use this data structure to give new $\tilde{O}(2^{n/2})$ and $\tilde{O}(2^{n/3})$ classical and quantum algorithms for Subset-Sum, not based on the meet-in-the-middle method. We then use the data structure in combination with variable time amplitude amplification and a quantum pair finding algorithm, extending quantum element distinctness and claw finding algorithms to the multiple solutions case, to give an $O(2^{0.504n})$ quantum algorithm for Shifted-Sums, an improvement on the best known $O(2^{0.773n})$ classical running time. We also study Pigeonhole Equal-Sums and Pigeonhole Modular Equal-Sums, where the existence of a solution is guaranteed by the pigeonhole principle. For the former problem we give classical and quantum algorithms with running time $\tilde{O}(2^{n/2})$ and $\tilde{O}(2^{2n/5})$, respectively. For the more general modular problem we give a classical algorithm which also runs in time $\tilde{O}(2^{n/2})$.
Inferring untrained complex dynamics of delay systems using an adapted  echo state network

Caused by finite signal propagation velocities, many complex systems feature time delays that may induce high-dimensional chaotic behavior and make forecasting intricate. Here, we propose an echo state network adaptable to the physics of systems with arbitrary delays. After training the network to forecast a system with a unique and sufficiently long delay, it already learned to predict the system dynamics for all other delays. A simple adaptation of the network's topology allows us to infer untrained features such as high-dimensional chaotic attractors, bifurcations, and even multistabilities, that emerge with shorter and longer delays. Thus, the fusion of physical knowledge of the delay system and data-driven machine learning yields a model with high generalization capabilities and unprecedented prediction accuracy.
Strong Cospectrality and Twin Vertices in Weighted Graphs

We explore algebraic and spectral properties of weighted graphs containing twin vertices that are useful in quantum state transfer. We extend the notion of adjacency strong cospectrality to arbitrary Hermitian matrices, with focus on the generalized adjacency matrix and the generalized normalized adjacency matrix. We then determine necessary and sufficient conditions such that a pair of twin vertices in a weighted graph exhibits strong cospectrality with respect to the above-mentioned matrices. We also generalize known results about equitable and almost equitable partitions, and use these to determine which joins of the form $X\vee H$, where $X$ is either the complete or empty graph, exhibit strong cospectrality.
Topology identification of autonomous quantum dynamical networks

Topology identification comprises reconstructing the interaction Hamiltonian of a quantum network by properly processing measurements of its density operator within a fixed time interval. It finds application in several quantum technology contexts, ranging from quantum communication to quantum computing or sensing. In this paper, we provide analytical conditions for the solvability of the topology identification problem for autonomous quantum dynamical networks. The solvability condition is then converted in an algorithm for quantum network reconstruction that is easily implementable on standard computer facilities. The obtained algorithm is tested for Hamiltonian reconstruction on numerical examples based on the quantum walks formalism.
A Way of Fast Calculating Lepton Magnetic Moments in Quantum  Electrodynamics

A new method of divergence subtraction in Feynman parametric integrals is presented. The method is suitable for calculating the lepton anomalous magnetic moments (AMM) in quantum electrodynamics (QED). The subtraction procedure eliminates all divergences before integration and leads to a finite Feynman parametric integral for each individual Feynman diagram. It is based on a forest formula with linear operators applied to the Feynman amplitudes of ultraviolet-divergent subdiagrams. The formula is similar to BPHZ; the difference is only in the linear operators used and in the way of combining them. The subtraction is equivalent to the on-shell renormalization from the beginning: for obtaining the final result we should only sum up the contributions of all Feynman diagrams after subtraction. The developed method is an improvement of the method presented by the author in 2016. The modification is specifically designed for calculating the contributions dependent on the relations of particle masses. In comparison with the old version, the new subtraction formula does not contain redundant terms and possesses some flexibility that can be used for improving the precision of calculations. Numerical test results are presented up to four loops.
Deep learning via message passing algorithms based on belief propagation

Message-passing algorithms based on the Belief Propagation (BP) equations constitute a well-known distributed computational scheme. It is exact on tree-like graphical models and has also proven to be effective in many problems defined on graphs with loops (from inference to optimization, from signal processing to clustering). The BP-based scheme is fundamentally different from stochastic gradient descent (SGD), on which the current success of deep networks is based. In this paper, we present and adapt to mini-batch training on GPUs a family of BP-based message-passing algorithms with a reinforcement field that biases distributions towards locally entropic solutions. These algorithms are capable of training multi-layer neural networks with discrete weights and activations with performance comparable to SGD-inspired heuristics (BinaryNet) and are naturally well-adapted to continual learning. Furthermore, using these algorithms to estimate the marginals of the weights allows us to make approximate Bayesian predictions that have higher accuracy than point-wise solutions.
Improving Local Effectiveness for Global robust training

Despite its popularity, deep neural networks are easily fooled. To alleviate this deficiency, researchers are actively developing new training strategies, which encourage models that are robust to small input perturbations. Several successful robust training methods have been proposed. However, many of them rely on strong adversaries, which can be prohibitively expensive to generate when the input dimension is high and the model structure is complicated. We adopt a new perspective on robustness and propose a novel training algorithm that allows a more effective use of adversaries. Our method improves the model robustness at each local ball centered around an adversary and then, by combining these local balls through a global term, achieves overall robustness. We demonstrate that, by maximizing the use of adversaries via focusing on local balls, we achieve high robust accuracy with weak adversaries. Specifically, our method reaches a similar robust accuracy level to the state of the art approaches trained on strong adversaries on MNIST, CIFAR-10 and CIFAR-100. As a result, the overall training time is reduced. Furthermore, when trained with strong adversaries, our method matches with the current state of the art on MNIST and outperforms them on CIFAR-10 and CIFAR-100.
Variational Quantum Eigensolver in Compressed Space for  Nearest-Neighbour Quadratic Fermionic Hamiltonians

Variational quantum eigensolvers (VQE) are one of the possible tasks that can be performed in noisy intermediate-scale quantum (NISQ) computers. While one of the limitations of NISQ platforms is their restricted number of available qubits, the theory of compressable matchgate circuits can be used to circumvent this limitation for certain types of Hamiltonians. We show how VQE algorithms can be used to find the ground state of quadratic fermionic Hamiltonians, providing an expressible ansatz in a logarithmic number of qubits. In particular, for systems of $n$ orbitals encoded to 2-local qubit models with nearest neighbour interactions, the ground state energy can be evaluated with $O\left(\log n\right)$ sets of measurements. This result is invariant of the dimensions in which the $n$ sites are arranged.
RKHS-SHAP: Shapley Values for Kernel Methods

Feature attribution for kernel methods is often heuristic and not individualised for each prediction. To address this, we turn to the concept of Shapley values~(SV), a coalition game theoretical framework that has previously been applied to different machine learning model interpretation tasks, such as linear models, tree ensembles and deep networks. By analysing SVs from a functional perspective, we propose \textsc{RKHS-SHAP}, an attribution method for kernel machines that can efficiently compute both \emph{Interventional} and \emph{Observational Shapley values} using kernel mean embeddings of distributions. We show theoretically that our method is robust with respect to local perturbations - a key yet often overlooked desideratum for consistent model interpretation. Further, we propose \emph{Shapley regulariser}, applicable to a general empirical risk minimisation framework, allowing learning while controlling the level of specific feature's contributions to the model. We demonstrate that the Shapley regulariser enables learning which is robust to covariate shift of a given feature and fair learning which controls the SVs of sensitive features.
Surveying Wonderland for many more literature visualization techniques

There are still many potential literature visualizations to be discovered. By focusing on a single text, the author surveys many existing visualizations across research domains, in the wild, and creates new visualizations. 58 techniques are indicated, suggesting a wider variety of visualizations beyond research disciplines.
Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm

Conventional wisdom in pruning Transformer-based language models is that pruning reduces the model expressiveness and thus is more likely to underfit rather than overfit. However, under the trending pretrain-and-finetune paradigm, we postulate a counter-traditional hypothesis, that is: pruning increases the risk of overfitting when performed at the fine-tuning phase. In this paper, we aim to address the overfitting problem and improve pruning performance via progressive knowledge distillation with error-bound properties. We show for the first time that reducing the risk of overfitting can help the effectiveness of pruning under the pretrain-and-finetune paradigm. Ablation studies and experiments on the GLUE benchmark show that our method outperforms the leading competitors across different tasks.
Creating Training Sets via Weak Indirect Supervision

Creating labeled training sets has become one of the major roadblocks in machine learning. To address this, recent \emph{Weak Supervision (WS)} frameworks synthesize training labels from multiple potentially noisy supervision sources. However, existing frameworks are restricted to supervision sources that share the same output space as the target task. To extend the scope of usable sources, we formulate Weak Indirect Supervision (WIS), a new research problem for automatically synthesizing training labels based on indirect supervision sources that have different output label spaces. To overcome the challenge of mismatched output spaces, we develop a probabilistic modeling approach, PLRM, which uses user-provided label relations to model and leverage indirect supervision sources. Moreover, we provide a theoretically-principled test of the distinguishability of PLRM for unseen labels, along with a generalization bound. On both image and text classification tasks as well as an industrial advertising application, we demonstrate the advantages of PLRM by outperforming baselines by a margin of 2%-9%.
Ordered Weighted Average Based grouping of nanomaterials with Arsinh and Dose Response similarity models

In the context of the EU GRACIOUS project, we propose a novel procedure for similarity assessment and grouping of nanomaterials. This methodology is based on the (1) Arsinh transformation function for scalar properties, (2) full curve shape comparison by application of a modified Kolmogorov-Smirnov metric for bivariate properties, (3) Ordered Weighted Average (OWA) aggregation-based grouping distance, and (4) hierarchical clustering. The approach allows for grouping of nanomaterials that is not affected by the dataset, so that group membership will not change when new candidates are included in the set of assessed materials. To facilitate the application of the proposed methodology, a software script was developed by using the R programming language which is currently under migration to a web tool. The presented approach was tested against a dataset, derived from literature review, related to immobilisation of Daphnia magna and reporting information on several nanomaterials.
Posterior predictive model checking using formal methods in a  spatio-temporal model

We propose an interdisciplinary framework, Bayesian formal predictive model checking (Bayes FPMC), which combines Bayesian predictive inference, a well established tool in statistics, with formal verification methods rooting in the computer science community. Bayesian predictive inference allows for coherently incorporating uncertainty about unknown quantities by making use of methods or models that produce predictive distributions which in turn inform decision problems. By formalizing these problems and the corresponding properties, we can use spatio-temporal reach and escape logic to probabilistically assess their satisfaction. This way, competing models can directly be ranked according to how well they solve the actual problem at hand. The approach is illustrated on an urban mobility application, where the crowdedness in the center of Milan is proxied by aggregated mobile phone traffic data. We specify several desirable spatio-temporal properties related to city crowdedness such as a fault tolerant network or the reachability of hospitals. After verifying these properties on draws from the posterior predictive distributions, we compare several spatio-temporal Bayesian models based on their overall and property-based predictive performance.
Language-Aligned Waypoint (LAW) Supervision for Vision-and-Language Navigation in Continuous Environments

In the Vision-and-Language Navigation (VLN) task an embodied agent navigates a 3D environment, following natural language instructions. A challenge in this task is how to handle 'off the path' scenarios where an agent veers from a reference path. Prior work supervises the agent with actions based on the shortest path from the agent's location to the goal, but such goal-oriented supervision is often not in alignment with the instruction. Furthermore, the evaluation metrics employed by prior work do not measure how much of a language instruction the agent is able to follow. In this work, we propose a simple and effective language-aligned supervision scheme, and a new metric that measures the number of sub-instructions the agent has completed during navigation.
Explicit commutative sequence space representations of function and  distribution spaces on the real half-line

We provide explicit commutative sequence space representations for classical function and distribution spaces on the real half-line. This is done by evaluating at the Fourier transforms of the elements of an orthonormal wavelet basis.
Non-Asymptotic Convergence Analysis of the Multiplicative Gradient  Algorithm for PET-Type Problems

We analyze the non-asymptotic convergence rate of the multiplicative gradient (MG) algorithm for the PET-type problems, and show that it exhibits $O(\ln(n)/t)$ convergence rates, where $n$ is the dimension of the optimization variable, in both ergodic and non-ergodic senses. Furthermore, we show that the distances from the iterates to the set of optimal solutions converge (to zero) at rate $O(1/\sqrt{t})$.
Enhancing Self-Disclosure In Neural Dialog Models By Candidate Re-ranking

Neural language modelling has progressed the state-of-the-art in different downstream Natural Language Processing (NLP) tasks. One such area is of open-domain dialog modelling, neural dialog models based on GPT-2 such as DialoGPT have shown promising performance in single-turn conversation. However, such (neural) dialog models have been criticized for generating responses which although may have relevance to the previous human response, tend to quickly dissipate human interest and descend into trivial conversation. One reason for such performance is the lack of explicit conversation strategy being employed in human-machine conversation. Humans employ a range of conversation strategies while engaging in a conversation, one such key social strategies is Self-disclosure(SD). A phenomenon of revealing information about one-self to others. Social penetration theory (SPT) proposes that communication between two people moves from shallow to deeper levels as the relationship progresses primarily through self-disclosure. Disclosure helps in creating rapport among the participants engaged in a conversation. In this paper, Self-disclosure enhancement architecture (SDEA) is introduced utilizing Self-disclosure Topic Model (SDTM) during inference stage of a neural dialog model to re-rank response candidates to enhance self-disclosure in single-turn responses from from the model.
LG4AV: Combining Language Models and Graph Neural Networks for Author  Verification

The automatic verification of document authorships is important in various settings. Researchers are for example judged and compared by the amount and impact of their publications and public figures are confronted by their posts on social media platforms. Therefore, it is important that authorship information in frequently used web services and platforms is correct. The question whether a given document is written by a given author is commonly referred to as authorship verification (AV). While AV is a widely investigated problem in general, only few works consider settings where the documents are short and written in a rather uniform style. This makes most approaches unpractical for online databases and knowledge graphs in the scholarly domain. Here, authorships of scientific publications have to be verified, often with just abstracts and titles available. To this point, we present our novel approach LG4AV which combines language models and graph neural networks for authorship verification. By directly feeding the available texts in a pre-trained transformer architecture, our model does not need any hand-crafted stylometric features that are not meaningful in scenarios where the writing style is, at least to some extent, standardized. By the incorporation of a graph neural network structure, our model can benefit from relations between authors that are meaningful with respect to the verification process. For example, scientific authors are more likely to write about topics that are addressed by their co-authors and twitter users tend to post about the same subjects as people they follow. We experimentally evaluate our model and study to which extent the inclusion of co-authorships enhances verification decisions in bibliometric environments.