Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Generation of Algorithms for Black-Box Robust Optimisation Problems

We develop algorithms capable of tackling robust black-box optimisation problems, where the number of model runs is limited. When a desired solution cannot be implemented exactly the aim is to find a robust one, where the worst case in an uncertainty neighbourhood around a solution still performs well. This requires a local maximisation within a global minimisation. To investigate improved optimisation methods for robust problems, and remove the need to manually determine an effective heuristic and parameter settings, we employ an automatic generation of algorithms approach: Grammar-Guided Genetic Programming. We develop algorithmic building blocks to be implemented in a Particle Swarm Optimisation framework, define the rules for constructing heuristics from these components, and evolve populations of search algorithms. Our algorithmic building blocks combine elements of existing techniques and new features, resulting in the investigation of a novel heuristic solution space. As a result of this evolutionary process we obtain algorithms which improve upon the current state of the art. We also analyse the component level breakdowns of the populations of algorithms developed against their performance, to identify high-performing heuristic components for robust problems.
Online Multiserver Convex Chasing and Optimization

We introduce the problem of $k$-chasing of convex functions, a simultaneous generalization of both the famous k-server problem in $R^d$, and of the problem of chasing convex bodies and functions. Aside from fundamental interest in this general form, it has natural applications to online $k$-clustering problems with objectives such as $k$-median or $k$-means. We show that this problem exhibits a rich landscape of behavior. In general, if both $k > 1$ and $d > 1$ there does not exist any online algorithm with bounded competitiveness. By contrast, we exhibit a class of nicely behaved functions (which include in particular the above-mentioned clustering problems), for which we show that competitive online algorithms exist, and moreover with dimension-free competitive ratio. We also introduce a parallel question of top-$k$ action regret minimization in the realm of online convex optimization. There, too, a much rougher landscape emerges for $k > 1$. While it is possible to achieve vanishing regret, unlike the top-one action case the rate of vanishing does not speed up for strongly convex functions. Moreover, vanishing regret necessitates both intractable computations and randomness. Finally we leave open whether almost dimension-free regret is achievable for $k > 1$ and general convex losses. As evidence that it might be possible, we prove dimension-free regret for linear losses via an information-theoretic argument.
There is Strength in Numbers: Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training

Natural Language Inference (NLI) datasets contain annotation artefacts resulting in spurious correlations between the natural language utterances and their respective entailment classes. These artefacts are exploited by neural networks even when only considering the hypothesis and ignoring the premise, leading to unwanted biases. Previous work proposed tackling this problem via adversarial training, but this leads to learned sentence representations that still suffer from the same biases. As a solution, we propose using an ensemble of adversaries during the training, encouraging the model to jointly decrease the accuracy of these different adversaries while fitting the data. We show that using an ensemble of adversaries can prevent the bias from being relearned after the model training is completed, further improving how well the model generalises to different NLI datasets. In particular, these models outperformed previous approaches when tested on 12 different NLI datasets not used in the model training. Finally, the optimal number of adversarial classifiers depends on the dimensionality of the sentence representations, with larger dimensional representations benefiting when trained with a greater number of adversaries.
National mapping and estimation of forest area by dominant tree species using Sentinel-2 data

Nation-wide Sentinel-2 mosaics were used with National Forest Inventory (NFI) data for modelling and subsequent mapping of spruce, pine and deciduous forest in Norway in 16 m $\times$ 16 m resolution. The accuracies of the best model ranged between 74% for spruce and 87% for deciduous forest. An overall accuracy of 90% was found on stand level using independent data from more than 42.000 stands. Errors mostly resulting from a forest mask reduced the model accuracies by approximately 10%. Nonetheless, efficiencies of national forest area estimates increased by 20% to 50% for model-assisted (MA) estimates and up to 90% for poststratification (PS). Greater minimum number of observations, however, constrained the use of PS. For estimates of sub-populations (municipalities), efficiencies improved by up to a factor of 8. However, even for municipalities with a decent number of NFI plots, direct NFI estimates were sometimes more precise than MA estimates. PS estimates were always more precise than direct and MA estimates but were applicable in fewer municipalities. The tree species prediction map is freely available as part of the Norwegian forest resource map and is used, among others, to improve maps of other variables of interest such as timber volume and biomass.
Network-principled deep generative models for designing drug combinations as graph sets

Motivation Combination therapy has shown to improve therapeutic efficacy while reducing side effects. Importantly, it has become an indispensable strategy to overcome resistance in antibiotics, anti-microbials, and anti-cancer drugs. Facing enormous chemical space and unclear design principles for small-molecule combinations, computational drug-combination design has not seen generative models to meet its potential to accelerate resistance-overcoming drug combination discovery. Results We have developed the first deep generative model for drug combination design, by jointly embedding graph-structured domain knowledge and iteratively training a reinforcement learning-based chemical graph-set designer. First, we have developed Hierarchical Variational Graph Auto-Encoders (HVGAE) trained end-to-end to jointly embed gene-gene, gene-disease, and disease-disease networks. Novel attentional pooling is introduced here for learning disease-representations from associated genes’ representations. Second, targeting diseases in learned representations, we have recast the drug-combination design problem as graph-set generation and developed a deep learning-based model with novel rewards. Specifically, besides chemical validity rewards, we have introduced novel generative adversarial award, being generalized sliced Wasserstein, for chemically diverse molecules with distributions similar to known drugs. We have also designed a network principle-based reward for drug combinations. Numerical results indicate that, compared to state-of-the-art graph embedding methods, HVGAE learns more informative and generalizable disease representations. Results also show that the deep generative models generate drug combinations following the principle across diseases. Case studies on four diseases show that network-principled drug combinations tend to have low toxicity. The generated drug combinations collectively cover the disease module similar to FDA-approved drug combinations and could potentially suggest novel systems-pharmacology strategies. Our method allows for examining and following network-based principle or hypothesis to efficiently generate disease-specific drug combinations in a vast chemical combinatorial space. Availability https://github.com/Shen-Lab/Drug-Combo-Generator
Alloying V in MnBi$_2$Te$_4$ for Robust Ferromagnetic Coupling and Quantum Anomalous Hall Effect

The intrinsic antiferromagnetic (AFM) interlayer coupling in two-dimensional magnetic topological insulator MnBi2Te4 places a restriction on realizing stable quantum anomalous Hall effect (QAHE) [Y. Deng et al., Science 367, 895 (2020)]. Through density functional theory calculations, we demonstrate the possibility of tuning the AFM coupling to the ferromagnetic coupling in MnBi2Te4 films by alloying about 50% V with Mn. As a result, QAHE can be achieved without alternation with the even or odd septuple layers. This provides a practical strategy to get robust QAHE in ultrathin MnBi2Te4 films, rendering them attractive for technological innovations.
Destination Prediction Based on Partial Trajectory Data

Two-thirds of the people who buy a new car prefer to use a substitute instead of the built-in navigation system. However, for many applications, knowledge about a user's intended destination and route is crucial. For example, suggestions for available parking spots close to the destination can be made or ride-sharing opportunities along the route are facilitated. Our approach predicts probable destinations and routes of a vehicle, based on the most recent partial trajectory and additional contextual data. The approach follows a three-step procedure: First, a $k$-d tree-based space discretization is performed, mapping GPS locations to discrete regions. Secondly, a recurrent neural network is trained to predict the destination based on partial sequences of trajectories. The neural network produces destination scores, signifying the probability of each region being the destination. Finally, the routes to the most probable destinations are calculated. To evaluate the method, we compare multiple neural architectures and present the experimental results of the destination prediction. The experiments are based on two public datasets of non-personalized, timestamped GPS locations of taxi trips. The best performing models were able to predict the destination of a vehicle with a mean error of 1.3 km and 1.43 km respectively.
Alzheimer's disease therapeutics - Are we making progress towards a disease-modifying treatment for patients? Highlights from the Society for Medicines Research Symposium, held June 20, 2016

Alzheimer's disease is arguably the largest healthcare issue of our time, with over 45 million people currently diagnosed with dementia worldwide. With the single biggest risk factor being age, this number is only going to increase as our populations' age. Alzheimer's disease pathology is highly complex, but is believed to be principally the result of an inter-play between the toxic proteins beta-amyloid and tau and driven by several genetic and environmental risk factors. Current treatments focus on treating cognitive and behavioral symptoms but have only modest effects and duration of efficacy. To discuss these topics, a 1-day symposium was held in Brussels to look at the progress that the field has made over recent years in attempting to target different aspects and stages of Alzheimer's disease pathology and what lessons have been learned. It also looked at the current biological mechanisms under clinical and preclinical evaluation, together with exploring how new and developing genetic insights into the disease are directing us to novel mechanisms for potential new treatment options and to address the critical question: "Are we making progress towards a disease-modifying treatment for patients? "
Nonlocal adhesion models for two type cancer on multidimensional bounded domain

Cell-cell adhesion is an inherently nonlocal phenomenon. Numerous partial differential equation models with nonlocal terms have been recently presented to describe this phenomenon, yet the mathematical properties of nonlocal adhesion model are not well understood. Here we consider two kinds of nonlocal cell-cell adhesion models satisfying no-flux conditions in a multidimensional bounded domain. We show global-in-time well-posedness of the solution to this model and obtain the uniform boundedness of solution.
Four Pages Are Indeed Necessary for Planar Graphs

An embedding of a graph in a book consists of a linear order of its vertices along the spine of the book and of an assignment of its edges to the pages of the book, so that no two edges on the same page cross. The book thickness of a graph is the minimum number of pages over all its book embeddings. Accordingly, the book thickness of a class of graphs is the maximum book thickness over all its members. In this paper, we address a long-standing open problem regarding the exact book thickness of the class of planar graphs, which previously was known to be either three or four. We settle this problem by demonstrating planar graphs that require four pages in any of their book embeddings, thus establishing that the book thickness of the class of planar graphs is four.
Quantifying resolution in cosmological N-body simulations using self-similarity

We demonstrate that testing for self-similarity in scale-free simulations provides an excellent tool to quantify the resolution at small scales of cosmological N-body simulations. Analysing two-point correlation functions measured in simulations using ABACUS, we show how observed deviations from self-similarity reveal the range of time and distance scales in which convergence is obtained. While the well-converged scales show accuracy below 1 percent, our results show that, with a small force softening length, the spatial resolution is essentially determined by the mass resolution. At later times the lower cut-off scale on convergence evolves in comoving units as $a^{-1/2}$ ($a$ being the scale factor), consistent with a hypothesis that it is set by two-body collisionality. A corollary of our results is that N-body simulations, particularly at high red-shift, contain a significant spatial range in which clustering appears converged with respect to the time-stepping and force softening but has not actually converged to the physical continuum result. The method developed can be applied to determine the resolution of any clustering statistic and extended to infer resolution limits for non-scale-free simulations.
ESResNet: Environmental Sound Classification Based on Visual Domain Models

Environmental Sound Classification (ESC) is an active research area in the audio domain and has seen a lot of progress in the past years. However, many of the existing approaches achieve high accuracy by relying on domain-specific features and architectures, making it harder to benefit from advances in other fields (e.g., the image domain). Additionally, some of the past successes have been attributed to a discrepancy of how results are evaluated (i.e., on unofficial splits of the UrbanSound8K (US8K) dataset), distorting the overall progression of the field. The contribution of this paper is twofold. First, we present a model that is inherently compatible with mono and stereo sound inputs. Our model is based on simple log-power Short-Time Fourier Transform (STFT) spectrograms and combines them with several well-known approaches from the image domain (i.e., ResNet, Siamese-like networks and attention). We investigate the influence of cross-domain pre-training, architectural changes, and evaluate our model on standard datasets. We find that our model out-performs all previously known approaches in a fair comparison by achieving accuracies of 97.0 % (ESC-10), 91.5 % (ESC-50) and 84.2 % / 85.4 % (US8K mono / stereo). Second, we provide a comprehensive overview of the actual state of the field, by differentiating several previously reported results on the US8K dataset between official or unofficial splits. For better reproducibility, our code (including any re- implementations) is made available.
On a new geometric homology theory

In this note we present a new homology theory, we call it geometric homology theory (or GHT for brevity). We prove that the homology groups of GHT are isomorphic to the singular homology groups, which solves a Conjecture of Voronov. GHT has several nice properties compared with singular homology, which makes itself more suitable than singular homology in some situations, especially in chain-level theories. We will develop further of this theory in our sequel paper.
Connecting the Dots of COVID-19 Transmissions in India

Witnessing its first case in late January 2020 India has seen a sharp rise in the number of positive cases of COVID-19. 34 States/UT (s) of the country have been found to be affected by the pandemic to date. We in this work, study the progress of COVID-19 pandemic in India. We aim to create transmission network visualization (s) of COVID-19 in India and perform analysis upon them. Using the transmission networks obtained we attempt to find the possible Super Spreader Individual (s) and Super Spreader Events (SSE) responsible for the outbreak in their respective regions. We discuss the potentials of network analysis in mitigating the further spread of the disease. This is one of the initial studies of the outbreak in India and the first attempt to study the pandemic in the country from a transmission network perspective.
Exchange interaction of hole-spin qubits in double quantum dots in highly anisotropic semiconductors

We study the exchange interaction between two hole-spin qubits in a double quantum dot setup in a silicon nanowire in the presence of magnetic and electric fields. Based on symmetry arguments we show that there exists an effective spin that is conserved even in highly anisotropic semiconductors, provided that the system has a twofold symmetry with respect to the direction of the applied magnetic field. This finding facilitates the definition of qubit basis states and simplifies the form of exchange interaction for two-qubit gates in coupled quantum dots. If the magnetic field is applied along a generic direction, cubic anisotropy terms act as an effective spin-orbit interaction introducing novel exchange couplings even for an inversion symmetric setup. Considering the example of a silicon nanowire double dot, we present the relative strength of these anisotropic exchange interaction terms and calculate the fidelity of the $\sqrt{\text{SWAP}}$ gate. Furthermore, we show that the anisotropy-induced spin-orbit effects can be comparable to that of the direct Rashba spin-orbit interaction for experimentally feasible electric field strengths.
Autonomous Cooperative Flight Control for Airship Swarms

This work investigates two approaches for the design of autonomous cooperative flight controllers for airship swarms. The first controller is based on formation flight and the second one is based on swarm intelligence strategies. In both cases, the team of airships needs to perform two different tasks: waypoint path following and ground moving target tracking. The UAV platform considered in this work is the NOAMAY airship developed in Brazil. We use a simulated environment to test the proposed approaches. Results show the inherent flexibility of the swarm intelligence approach on both tasks.
Secukinumab for rheumatology: development and its potential place in therapy

Rheumatic disease is not a single disorder, but a group of more than 100 diseases that affect joints, connective tissues, and/or internal organs. Although rheumatic diseases like rheumatoid arthritis (RA), psoriatic arthritis, and ankylosing spondylitis (AS) differ in their pathogenesis and clinical presentation, the treatment of these inflammatory disorders overlaps. Non-steroid anti-inflammatory drugs are used to reduce pain and inflammation. Additional disease-modifying anti-rheumatic drugs are prescribed to slowdown disease progression, and is in RA more frequently and effectively applied than in AS. Biologicals are a relatively new class of treatments that specifically target cytokines or cells of the immune system, like tumor necrosis factor alpha inhibitors or B-cell blockers. A new kid on the block is the interleukin-17 (IL-17) inhibitor secukinumab, which has been recently approved by the US Food and Drug Administration for moderate-to-severe plaque psoriasis, psoriatic arthritis, and AS. IL-17 is a proinflammatory cytokine that has an important role in host defense, but its proinflammatory and destructive effects have also been linked to pathogenic processes in autoimmune diseases like RA and psoriasis. Animal models have greatly contributed to further insights in the potential of IL-17 blockade in autoimmune and autoinflammatory diseases, and have resulted in the development of various potential drugs targeting the IL-17 pathway. Secukinumab (AIN457) is a fully human monoclonal antibody that selectively binds to IL-17A and recently entered the market under the brand name Cosentyx®. By binding to IL-17A, secukinumab prevents it from binding to its receptor and inhibits its ability to trigger inflammatory responses that play a role in the development of various autoimmune diseases. With secukinumab being the first in class to receive Food and Drug Administration approval, this article will further focus on this new biologic agent and review the milestones in its development and marketing.
The Qualification of Outcome after Cervical Spine Surgery by Patients Compared to the Neck Disability Index

Objective The Neck Disability Index (NDI) is a patient self-assessed outcome measurement tool to assess disability, and that is frequently used to evaluate the effects of the treatment of neck-related problems. In individualized medicine it is mandatory that patients can interpret data in order to choose a treatment. A change of NDI or an absolute NDI is generally meaningless to a patient. Therefore, a correlation between the qualification of the clinical situation rated by the patient and the NDI score was evaluated. Methods Patients who completed an NDI after anterior surgery because of symptomatic single level degenerative cervical disc disease were asked one month after completion of the NDI to qualify their clinical situation of a 5-item Likert scale varying from excellent to bad. Since a clear distinction between the categories was not possible based on the total NDI score, a ROC-curve was built, and the AUC computed in order to estimate best dichotomization in qualification of the clinical situation. The best corresponding cut-off point for the NDI total score was found by studying sensitivity and specificity for all possible cut-off points. Results 102 patients were included. The highest AUC was obtained by dichotomizing the qualification into a group with good outcome and less-good outcome. The highest sensitivity and specificity for the dichotomized qualification as good outcome corresponded to a NDI ≤ 7. Sensitivity was 81.08% and specificity was 78.57%. Conclusion This is the first study that correlated the qualification of the situation by the patients themselves and NDI. An NDI ≤ 7 corresponded to a good outcome according to the patients. This is valuable information to inform patients in their decision for any treatment.
Current evidence and applications of photodynamic therapy in dermatology

In photodynamic therapy (PDT) a photosensitizer – a molecule that is activated by light – is administered and exposed to a light source. This leads both to destruction of cells targeted by the particular type of photosensitizer, and immunomodulation. Given the ease with which photosensitizers and light can be delivered to the skin, it should come as no surprise that PDT is an increasingly utilized therapeutic in dermatology. PDT is used commonly to treat precancerous cells, sun-damaged skin, and acne. It has reportedly also been used to treat other conditions including inflammatory disorders and cutaneous infections. This review discusses the principles behind how PDT is used in dermatology, as well as evidence for current applications of PDT.