Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Industrial Espionage

The theory of deep learning is now considered largely solved, and is well understood by researchers and influencers alike. To maintain our relevance, we therefore seek to apply our skills to under-explored, lucrative applications of this technology. To this end, we propose and Deep Industrial Espionage, an efficient end-to-end framework for industrial information propagation and productisation. Specifically, given a single image of a product or service, we aim to reverse-engineer, rebrand and distribute a copycat of the product at a profitable price-point to consumers in an emerging market---all within in a single forward pass of a Neural Network. Differently from prior work in machine perception which has been restricted to classifying, detecting and reasoning about object instances, our method offers tangible business value in a wide range of corporate settings. Our approach draws heavily on a promising recent arxiv paper until its original authors' names can no longer be read (we use felt tip pen). We then rephrase the anonymised paper, add the word "novel" to the title, and submit it a prestigious, closed-access espionage journal who assure us that someday, we will be entitled to some fraction of their extortionate readership fees.
Active Field Theories

Lecture Notes for Les Houches 2018 Summer School on Active Matter and Nonequilibrium Statistical Physics. These notes give an introduction to statistical field theories of active matter. After showing an example of how such theories can be created bottom-up by explicit coarse graining of active particle models, the main approach taken here is top-down: minimal terms are added to well-studied stochastic field theories of passive systems that break time-reversibility and thereby cause nonzero steady-state entropy production. These theories are, so far, rather poorly understood compared to their passive counterparts.
Effects of the QCD equation of state and lepton asymmetry on primordial gravitational waves

Using the quantum chromodynamics (QCD) equation of state (EoS) from lattice calculations we investigate effects from QCD on primordial gravitational waves (PGWs) produced during the inflationary era. We also consider different cases for vanishing and nonvanishing lepton asymmetry where the latter one is constrained by cosmic microwave background experiments. Our results show that there is up to a few percent deviation in the predicted gravitational wave background in the frequency range around the QCD transition ($10^{-10}- 10^{-7}$~Hz) for different lattice QCD EoSs, or at larger frequencies for nonvanishing lepton asymmetry using perturbative QCD. Future gravitational wave experiments with high enough sensitivity in the measurement of the amplitude of PGWs like SKA, EPTA, DECIGO and LISA can probe these differences and can shed light on the real nature of the cosmic QCD transition and the existence of a nonvanishing lepton asymmetry in the early universe.
Compact high bandwidth dimension ratio steering‐shaped super wideband antenna for future wireless communication applications

In this article, a compact super wideband (SWB) high bandwidth dimension ratio (BDR) steering‐shaped antenna is proposed for future wireless communication applications. The proposed antenna consists of a linearly tapered microstrip‐fed circular ring with four elliptical spokes to connect with a central circular disk to form a steering‐shaped radiating patch and a rounded cornered notch‐loaded rectangular partial ground. The experimental results demonstrate a SWB from 2.3 to 34.8 GHz with a ratio bandwidth (RBW) of 15.13:1 and percentage bandwidth of 175.2% for |S11| ≤ −10 dB that are in good agreement with simulation results. The overall size of the proposed antenna is 35 × 35 × 1.6 mm3 with a BDR of 2403.3. The simulated average peak gain of the antenna is 4.76 dBi. Due to the compact size and good radiation properties over a wide bandwidth, the designed antenna is suitable for civilian and military wireless applications.
Diagnostic Biopsy via In-Office Frozen Sections for Clinical Nonmelanoma Skin Cancer.

BACKGROUND Treatment of nonmelanoma skin cancer (NMSC) by Mohs surgery has traditionally relied on previous pathologic evaluation of paraffin-embedded tissue. Tissue processing by frozen sections allows for expedited diagnosis and treatment; however, data on its accuracy are limited. OBJECTIVE To measure the accuracy and outcomes of biopsy via frozen sections for clinical NMSC. METHODS Biopsies of clinical NMSCs processed via frozen sections with in-office diagnosis rendered by one Mohs surgeon were retrospectively reviewed by one board-certified dermatopathologist. Discordant diagnoses were re-read in blinded fashion by both physicians. If still discordant, final diagnosis was determined by consensus discussion. Inter-rater reliability was calculated using Cohen's kappa statistic. RESULTS Two hundred ninety-seven lesions from 208 patients were included. Correlation between in-office and final diagnosis was 0.876 indicating "almost perfect" concordance. Sensitivity and specificity of in-office diagnosis for detecting malignancy were 98.1% and 94.4%. Seven cases (2.0%) had a clinically relevant change in final diagnosis, but appropriate treatment had been rendered. Two benign lesions (0.7%) initially diagnosed as malignant underwent excision. CONCLUSION In-office biopsy via frozen sections is highly accurate in confirming NMSC. This practice may speed diagnosis and treatment thus improving outcomes and patient satisfaction.
Progressive LiDAR adaptation for road detection

Despite rapid developments in visual image-based road detection, robustly identifying road areas in visual images remains challenging due to issues like illumination changes and blurry images. To this end, LiDAR sensor data can be incorporated to improve the visual image-based road detection, because LiDAR data is less susceptible to visual noises. However, the main difficulty in introducing LiDAR information into visual image-based road detection is that LiDAR data and its extracted features do not share the same space with the visual data and visual features. Such gaps in spaces may limit the benefits of LiDAR information for road detection. To overcome this issue, we introduce a novel Progressive LiDAR adaptation-aided road detection ( PLARD ) approach to adapt LiDAR information into visual image-based road detection and improve detection performance. In PLARD, progressive LiDAR adaptation consists of two subsequent modules: 1 ) data space adaptation, which transforms the LiDAR data to the visual data space to align with the perspective view by applying altitude difference-based transformation; and 2 ) feature space adaptation, which adapts LiDAR features to visual features through a cascaded fusion structure. Comprehensive empirical studies on the well-known KITTI road detection benchmark demonstrate that PLARD takes advantage of both the visual and LiDAR information, achieving much more robust road detection even in challenging urban scenes. In particular, PLARD outperforms other state-of-the-art road detection models and is currently top of the publicly accessible benchmark leader-board.
Unsupervised Training of a Deep Clustering Model for Multichannel Blind Source Separation

We propose a training scheme to train neural network-based source separation algorithms from scratch when parallel clean data is unavailable. In particular, we demonstrate that an unsupervised spatial clustering algorithm is sufficient to guide the training of a deep clustering system. We argue that previous work on deep clustering requires strong supervision and elaborate on why this is a limitation. We demonstrate that (a) the single-channel deep clustering system trained according to the proposed scheme alone is able to achieve a similar performance as the multi-channel teacher in terms of word error rates and (b) initializing the spatial clustering approach with the deep clustering result yields a relative word error rate reduction of 26 % over the unsupervised teacher.
The First Measurement of the $2^{3}S_{1} \rightarrow 3^{3}P - 2^{3}P$ Tune-Out Wavelength in He*

The workhorse of atomic physics: quantum electrodynamics is one of the best tested theories in physics. However recent discrepancies have shed doubt on its accuracy for complex atomic systems. To facilitate the development of the theory further we aim to measure transition dipole matrix elements of metastable helium (He*) (the ideal 3 body test-bed) to the highest accuracy thus far. We have undertaken a measurement of the `tune-out wavelength' which occurs when the contributions to the dynamic polarizability from all atomic transitions sum to zero; thus illuminating an atom with this wavelength of light then produces no net energy shift. This provides a strict constraint on the transition dipole matrix elements without the complication and inaccuracy of other methods. Using a novel atom-laser based technique we have made the first measurement of the the tune-out wavelength in metastable helium between the $3^{3}P_{1,2,3}$ and $2^{3}P_{1,2,3}$ states at 413.07(2)nm which compares well with the predicted value\cite{Mitroy2013} of 413.02(9). We have additionally developed many of the methods necessary to improve this measurement to the 100fm level of accuracy where it will form the most accurate determination of transition rate information ever made in He* and provide a stringent test for atomic QED simulations. We believe this measurement to be one of the most sensitive ever made of an optical dipole potential, able to detect changes in potentials of $\sim200pK$ and is widely applicable to other species and areas of atom optics.
The tropical geometry of shortest paths

We study parameterized versions of classical algorithms for computing shortest-path trees. This is most easily expressed in terms of tropical geometry. Applications include the enumeration of polytropes, i.e., ordinary convex polytopes which are also tropically convex, as well as shortest paths in traffic networks with variable link travel times.
Learning Algorithms via Neural Logic Networks

We propose a novel learning paradigm for Deep Neural Networks (DNN) by using Boolean logic algebra. We first present the basic differentiable operators of a Boolean system such as conjunction, disjunction and exclusive-OR and show how these elementary operators can be combined in a simple and meaningful way to form Neural Logic Networks (NLNs). We examine the effectiveness of the proposed NLN framework in learning Boolean functions and discrete-algorithmic tasks. We demonstrate that, in contrast to the implicit learning in MLP approach, the proposed neural logic networks can learn the logical functions explicitly that can be verified and interpreted by human. In particular, we propose a new framework for learning the inductive logic programming (ILP) problems by exploiting the explicit representational power of NLN. We show the proposed neural ILP solver is capable of feats such as predicate invention and recursion and can outperform the current state of the art neural ILP solvers using a variety of benchmark tasks such as decimal addition and multiplication, and sorting on ordered list.
Incorrect implementations of the Floyd-Warshall algorithm give correct solutions after three repeats

The Floyd--Warshall algorithm is a well-known algorithm for the all-pairs shortest path problem that is simply implemented by triply nested loops. In this study, we show that the incorrect implementations of the Floyd--Warshall algorithm that misorder the triply nested loops give correct solutions if these are repeated three times.
Anharmonic lattice relaxation during nonradiative carrier capture

Lattice vibrations of point defects are essential for understanding non-radiative electron and hole capture in semiconductors as they govern properties including persistent photoconductivity and Shockley-Read-Hall recombination rate. Although the harmonic approximation is sufficient to describe a defect with small lattice relaxation, for cases of large lattice relaxation it is likely to break down. We describe a first-principles procedure to account for anharmonic carrier capture and apply it to the important case of the \textit{DX} center in GaAs. This is a system where the harmonic approximation grossly fails. Our treatment of the anharmonic Morse-like potentials accurately describes the observed electron capture barrier, predicting the absence of quantum tunnelling at low temperature, and a high hole capture rate that is independent of temperature. The model also explains the origin of the composition-invariant electron emission barrier. These results highlight an important shortcoming of the standard approach for describing point defect ionization that is accompanied by large lattice relaxation, where charge transfer occurs far from the equilibrium configuration.
Enhancement of Energy-Based Swing-Up Controller via Entropy Search

An energy based approach for stabilizing a mechanical system has offered a simple yet powerful control scheme. However, since it does not impose such strong constraints on parameter space of the controller, finding appropriate parameter values for an optimal controller is known to be hard. This paper intends to generate an optimal energy-based controller for swinging up a rotary inverted pendulum, also known as the Furuta pendulum, by applying the Bayesian optimization called Entropy Search. Simulations and experiments show that the optimal controller has an improved performance compared to a nominal controller for various initial conditions.
Algorithms Clearly Beat Gamers at Quantum Moves. A Verification

The paper [Sorensen et al., Nature 532] considers how human players compare to algorithms for solving the Quantum Moves game BringHomeWater and design new algorithms based on the intuition extracted from players. The claim by [Sorensen et al., Nature 532] is that players outperform widely used algorithms, in particular the KASS algorithm, based on the Krotov algorithm, and that player intuition is crucial to develop improved methods. However, as initially discussed by D. Sels [D. Sels, Phys. Rev. A 97], a standard Coordinate Ascent algorithm outperforms all players by a large margin. Albeit D. Sels only compare to player solutions, the simple algorithm outperforms all algorithms based on player solutions and Krotov, and it does so using much less time and iterations. In this paper we elaborate on the methods discussed by D. Sels and verify that the presented algorithm, solves the problem better than all players and algorithms derived from player solutions in [Sorensen et al., Nature 532]. We also verify the theoretical analysis presented by D. Sels, that gives a theoretically derived protocol that outperforms all players. We add a comparison with gradient ascent or GRAPE. Starting from uniform random values, GRAPE outperforms all players by a large margin. GRAPE works at least as well as the methods from [Sorensen et al., Nature 532] initialized with player solutions. A standard analysis of the results from GRAPE provides a starting point for GRAPE, that outperform all algorithms from [Sorensen et al., Nature 532]. We compare with a basic Krotov algorithm, and get results similar to GRAPE, clearly outperforming players and the KASS algorithm. These experiments verify and underline the result in [D. Sels, Phys. Rev. A 97] that the conclusions from [Sorensen et al., Nature 532] regarding algorithms are untenable. In fact the opposite conclusions are true.
Semantics Disentangling for Text-To-Image Generation

Synthesizing photo-realistic images from text descriptions is a challenging problem. Previous studies have shown remarkable progresses on visual quality of the generated images. In this paper, we consider semantics from the input text descriptions in helping render photo-realistic images. However, diverse linguistic expressions pose challenges in extracting consistent semantics even they depict the same thing. To this end, we propose a novel photo-realistic text-to-image generation model that implicitly disentangles semantics to both fulfill the high-level semantic consistency and low-level semantic diversity. To be specific, we design (1) a Siamese mechanism in the discriminator to learn consistent high-level semantics, and (2) a visual-semantic embedding strategy by semantic-conditioned batch normalization to find diverse low-level semantics. Extensive experiments and ablation studies on CUB and MS-COCO datasets demonstrate the superiority of the proposed method in comparison to state-of-the-art methods.
Learning a Partitioning Advisor with Deep Reinforcement Learning

Commercial data analytics products such as Microsoft Azure SQL Data Warehouse or Amazon Redshift provide ready-to-use scale-out database solutions for OLAP-style workloads in the cloud. While the provisioning of a database cluster is usually fully automated by cloud providers, customers typically still have to make important design decisions which were traditionally made by the database administrator such as selecting the partitioning schemes. In this paper we introduce a learned partitioning advisor for analytical OLAP-style workloads based on Deep Reinforcement Learning (DRL). The main idea is that a DRL agent learns its decisions based on experience by monitoring the rewards for different workloads and partitioning schemes. We evaluate our learned partitioning advisor in an experimental evaluation with different databases schemata and workloads of varying complexity. In the evaluation, we show that our advisor is not only able to find partitionings that outperform existing approaches for automated partitioning design but that it also can easily adjust to different deployments. This is especially important in cloud setups where customers can easily migrate their cluster to a new set of (virtual) machines.
Context and Attribute Grounded Dense Captioning

Dense captioning aims at simultaneously localizing semantic regions and describing these regions-of-interest (ROIs) with short phrases or sentences in natural language. Previous studies have shown remarkable progresses, but they are often vulnerable to the aperture problem that a caption generated by the features inside one ROI lacks contextual coherence with its surrounding context in the input image. In this work, we investigate contextual reasoning based on multi-scale message propagations from the neighboring contents to the target ROIs. To this end, we design a novel end-to-end context and attribute grounded dense captioning framework consisting of 1) a contextual visual mining module and 2) a multi-level attribute grounded description generation module. Knowing that captions often co-occur with the linguistic attributes (such as who, what and where), we also incorporate an auxiliary supervision from hierarchical linguistic attributes to augment the distinctiveness of the learned captions. Extensive experiments and ablation studies on Visual Genome dataset demonstrate the superiority of the proposed model in comparison to state-of-the-art methods.
How Much Can D2D Communication Reduce Content Delivery Latency in Fog Networks With Edge Caching?

A Fog-Radio Access Network (F-RAN) is studied in which cache-enabled Edge Nodes (ENs) with dedicated fronthaul connections to the cloud aim at delivering contents to mobile users. Using an information-theoretic approach, this work tackles the problem of quantifying the potential latency reduction that can be obtained by enabling Device-to-Device (D2D) communication over out-of-band broadcast links. Following prior work, the Normalized Delivery Time (NDT) — a metric that captures the high signal-to-noise ratio worst-case latency — is adopted as the performance criterion of interest. Joint edge caching, downlink transmission, and D2D communication policies based on compress-and-forward are proposed that are shown to be information-theoretically optimal to within a constant multiplicative factor of two for all values of the problem parameters, and to achieve the minimum NDT for a number of special cases. The analysis provides insights on the role of D2D cooperation in improving the delivery latency.
Quasi-Polynomial Algorithms for Submodular Tree Orienteering and Other Directed Network Design Problems

We consider the following general network design problem on directed graphs. The input is an asymmetric metric $(V,c)$, root $r^{*}\in V$, monotone submodular function $f:2^V\rightarrow \mathbb{R}_+$ and budget $B$. The goal is to find an $r^{*}$-rooted arborescence $T$ of cost at most $B$ that maximizes $f(T)$. Our main result is a simple quasi-polynomial time $O(\frac{\log k}{\log\log k})$-approximation algorithm for this problem, where $k\le |V|$ is the number of vertices in an optimal solution. To the best of our knowledge, this is the first non-trivial approximation ratio for this problem. As a consequence we obtain an $O(\frac{\log^2 k}{\log\log k})$-approximation algorithm for directed (polymatroid) Steiner tree in quasi-polynomial time. We also extend our main result to a setting with additional length bounds at vertices, which leads to improved $O(\frac{\log^2 k}{\log\log k})$-approximation algorithms for the single-source buy-at-bulk and priority Steiner tree problems. For the usual directed Steiner tree problem, our result matches the best previous approximation ratio [GLL19]. Our algorithm has the advantage of being deterministic and faster: the runtime is $\exp(O(\log n\, \log^{1+\epsilon} k))$. For polymatroid Steiner tree and single-source buy-at-bulk, our result improves prior approximation ratios by a logarithmic factor. For directed priority Steiner tree, our result seems to be the first non-trivial approximation ratio. All our approximation ratios are tight (up to constant factors) for quasi-polynomial algorithms.
Learning to Stop in Structured Prediction for Neural Machine Translation

Beam search optimization (Wiseman and Rush, 2016) resolves many issues in neural machine translation. However, this method lacks principled stopping criteria and does not learn how to stop during training, and the model naturally prefers longer hypotheses during the testing time in practice since they use the raw score instead of the probability-based score. We propose a novel ranking method which enables an optimal beam search stop- ping criteria. We further introduce a structured prediction loss function which penalizes suboptimal finished candidates produced by beam search during training. Experiments of neural machine translation on both synthetic data and real languages (German→English and Chinese→English) demonstrate our pro- posed methods lead to better length and BLEU score.