Miracle-dz/UniFAR-FTUs huggingface dataset:
Reconfigurable MIMO towards Electro-magnetic Information Theory: Capacity Maximization Pattern Design

In this paper, we focus on the pattern reconfigurable multiple-input multiple-output (PR-MIMO), a technique that has the potential to bridge the gap between electro-magnetics and communications towards the emerging Electro-magnetic Information Theory (EIT). Specifically, we focus on the pattern design problem aimed at maximizing the channel capacity for reconfigurable MIMO communication systems, where we firstly introduce the matrix representation of PR-MIMO and further formulate a pattern design problem. We decompose the pattern design into two steps, i.e., the correlation modification process to optimize the correlation structure of the channel, followed by the power allocation process to improve the channel quality based on the optimized channel structure. For the correlation modification process, we propose a sequential optimization framework with eigenvalue decomposition to obtain near-optimal solutions. For the power allocation process, we provide a closed-form power allocation scheme to redistribute the transmission power among the modified subchannels. Numerical results show that the proposed pattern design scheme offers significant improvements over legacy MIMO systems, which motivates the application of PR-MIMO in wireless communication systems.
Mapping Topology of Skyrmions and Fractional Quantum Hall Droplets to Nuclear EFT for Ultra-Dense Baryonic Matter

We describe the mapping at high density of topological structure of baryonic matter to a nuclear effective field theory that implements hidden symmetries emergent from strong nuclear correlations. The theory constructed is found to be consistent with no conflicts with the presently available observations in both normal nuclear matter and compact-star matter. The hidden symmetries involved are “local flavor symmetry” of the vector mesons identified to be (Seiberg-)dual to the gluons of QCD and hidden “quantum scale symmetry” with an IR fixed point with a “genuine dilaton (GD)” characterized by non-vanishing pion and dilaton decay constants. Both the skyrmion topology for Nf≥2 baryons and the fractional quantum Hall (FQH) droplet topology for Nf=1 baryons are unified in the “homogeneous/hidden” Wess–Zumino term in the hidden local symmetry (HLS) Lagrangian. The possible indispensable role of the FQH droplets in going beyond the density regime of compact stars approaching scale-chiral restoration is explored by moving toward the limit where both the dilaton and the pion go massless.
Factored Attention and Embedding for Unstructured-view Topic-related Ultrasound Report Generation

Echocardiography is widely used to clinical practice for diagnosis and treatment, e.g., on the common congenital heart defects. The traditional manual manipulation is error-prone due to the staff shortage, excess workload, and less experience, leading to the urgent requirement of an automated computer-aided reporting system to lighten the workload of ultrasonologists considerably and assist them in decision making. Despite some recent successful attempts in automatical medical report generation, they are trapped in the ultrasound report generation, which involves unstructured-view images and topic-related descriptions. To this end, we investigate the task of the unstructured-view topic-related ultrasound report generation, and propose a novel factored attention and embedding model (termed FAE-Gen). The proposed FAE-Gen mainly consists of two modules, i.e., view-guided factored attention and topicoriented factored embedding, which 1) capture the homogeneous and heterogeneous morphological characteristic across different views, and 2) generate the descriptions with different syntactic patterns and different emphatic contents for different topics. Experimental evaluations are conducted on a to-be-released large-scale clinical cardiovascular ultrasound dataset (CardUltData). Both quantitative comparisons and qualitative analysis demonstrate the effectiveness and the superiority of FAE-Gen over seven commonly-used metrics.
Secret-to-Image Reversible Transformation for Generative Steganography

Recently, generative steganography that transforms secret information to a generated image has been a promising technique to resist steganalysis detection. However, due to the inefficiency and irreversibility of the secret-to-image transformation, it is hard to find a good trade-off between the information hiding capacity and extraction accuracy. To address this issue, we propose a secret-to-image reversible transformation (S2IRT) scheme for generative steganography. The proposed S2IRT scheme is based on a generative model, i.e., Glow model, which enables a bijective-mapping between latent space with multivariate Gaussian distribution and image space with a complex distribution. In the process of S2I transformation, guided by a given secret message, we construct a latent vector and then map it to a generated image by the Glow model, so that the secret message is finally transformed to the generated image. Owing to good efficiency and reversibility of S2IRT scheme, the proposed steganographic approach achieves both high hiding capacity and accurate extraction of secret message from generated image. Furthermore, a separate encoding-based S2IRT (SE-S2IRT) scheme is also proposed to improve the robustness to common image attacks. The experiments demonstrate the proposed steganographic approaches can achieve high hiding capacity (up to 4 bpp) and accurate information extraction (almost 100% accuracy rate) simultaneously, while maintaining desirable anti-detectability and imperceptibility.
Bi-accessible and bipresentable 2-categories

We develop a 2-dimensional version of accessibility and presentability compatible with the formalism of flat pseudofunctors. First we give prerequisites on the different notions of 2-dimensional colimits, filteredness and cofinality; in particular we show that sigma-filteredness and bifilteredness are actually equivalent in practice for our purposes. Then, we define bi-accessible and bipresentable 2-categories in terms of bicompact objects and bifiltered bicolimits. We then characterize them as categories of flat pseudofunctors. We also prove a bi-accessible right bi-adjoint functor theorem and deduce a 2-dimensional Gabriel-Ulmer duality relating small bilex 2-categories and finitely bipresentable 2-categories. Finally, we show that 2-categories of pseudo-algebras of bifinitary pseudomonads on Cat are finitely bipresentable, which in particular captures the case of Lex, the 2-category of small lex categories. Invoking the technology of lex-colimits, we prove further that several 2-categories arising in categorical logic (Reg, Ex, Coh, Ext, Adh, Pretop) are also finitely bipresentable.
Can I see an Example? Active Learning the Long Tail of Attributes and Relations

There has been significant progress in creating machine learning models that identify objects in scenes along with their associated attributes and relationships; however, there is a large gap between the best models and human capabilities. One of the major reasons for this gap is the difficulty in collecting sufficient amounts of annotated relations and attributes for training these systems. While some attributes and relations are abundant, the distribution in the natural world and existing datasets is long tailed. In this paper, we address this problem by introducing a novel incremental active learning framework that asks for attributes and relations in visual scenes. While conventional active learning methods ask for labels of specific examples, we flip this framing to allow agents to ask for examples from specific categories. Using this framing, we introduce an active sampling method that asks for examples from the tail of the data distribution and show that it outperforms classical active learning methods on Visual Genome.
Strategy Complexity of Point Payoff, Mean Payoff and Total Payoff Objectives in Countable MDPs

We study countably infinite Markov decision processes (MDPs) with real-valued transition rewards. Every infinite run induces the following sequences of payoffs: 1. Point payoff (the sequence of directly seen transition rewards), 2. Mean payoff (the sequence of the sums of all rewards so far, divided by the number of steps), and 3. Total payoff (the sequence of the sums of all rewards so far). For each payoff type, the objective is to maximize the probability that the $\liminf$ is non-negative. We establish the complete picture of the strategy complexity of these objectives, i.e., how much memory is necessary and sufficient for $\varepsilon$-optimal (resp. optimal) strategies. Some cases can be won with memoryless deterministic strategies, while others require a step counter, a reward counter, or both.
LOGÍSTICA REVERSA DE PNEUS INSERVÍVEIS: DISCUSSÕES SOBRE TRÊS ALTERNATIVAS DE RECICLAGEM PARA ESTE PASSIVO AMBIENTAL REVERSE LOGISTICS OF SCRAP TIRES: DISCUSSIONS ON THREE RECYCLING ALTERNATIVES FOR THIS ENVIRONMENTAL LIABILITY

This paper examines the post-consumer reverse logistics of scrap tires with a focus on three recycling alternatives: asphalt rubber, cement industry and pyrolysis with Shale. Based on primary and secondary data, this research attempts to answer the following question: What are the main advantages and limitations offered by the three scrap tires recycling alternatives? At first, secondary data were collected through bibliographic research which was based on academic articles, dissertations and theses approved after 2000, along with websites and periodicals of the area. Primary data were collected in two stages. The first one used a structured questionnaire sent to practitioners registered in CNPq's Lattes Platform, which had some
Unicorns and Giraffes in the binary zoo: stripped giants with subgiant companions

We analyze two binary systems containing giant stars, V723 Mon (“the Unicorn”) and 2M04123153+6738486 (“the Giraffe”). Both giants orbit more massive but less luminous companions, previously proposed to be mass-gap black holes. Spectral disentangling reveals luminous companions with star-like spectra in both systems. Joint modeling of the spectra, light curves, and spectral energy distributions robustly constrains the masses, temperatures, and radii of both components: the primaries are luminous, cool giants (Teff, giant = 3, 800K and 4, 000K, Rgiant = 22.5 R and 25 R ) with exceptionally low masses (Mgiant ≈ 0.4M ) that likely fill their Roche lobes. The secondaries are only slightly warmer subgiants (Teff, 2 = 5, 800K and 5, 150K, R2 = 8.3 R and 9 R ) and thus are consistent with observed UV limits that would rule out main-sequence stars with similar masses (M2 ≈ 2.8M and ≈ 1.8M ). In the Unicorn, rapid rotation blurs the spectral lines of the subgiant, making it challenging to detect even at wavelengths where it dominates the total light. Both giants have surface abundances indicative of CNO processing and subsequent envelope stripping. The properties of both systems can be reproduced by binary evolution models in which a 1 − 2M primary is stripped by a companion as it ascends the giant branch. The fact that the companions are also evolved implies either that the initial mass ratio was very near unity, or that the companions are temporarily inflated due to rapid accretion. The Unicorn and Giraffe offer a window into into a rarely-observed phase of binary evolution preceding the formation of wide-orbit helium white dwarfs, and eventually, compact binaries containing two helium white dwarfs.
The Future Circular Collider: a Summary for the US 2021 Snowmass Process

In this white paper for the 2021 Snowmass process, we give a description of the proposed Future Circular Collider (FCC) project and its physics program. The paper summarizes and updates the discussion submitted to the European Strategy on Particle Physics. After construction of an ≈ 90 km tunnel, an electron-positron collider based on established technologies allows world-record instantaneous luminosities at center-of-mass energies from the Z resonance through the ZH and WW and up to tt thresholds, enabling a very rich set of fundamental measurements including Higgs couplings determinations at the subpercent level, precision tests of the weak and strong forces, and searches for new particles, including dark matter, both directly and via virtual corrections or mixing. Among other possibilities, the FCC-ee will be able to (i) indirectly discover new particles coupling to the Higgs and/or electroweak bosons up to scales Λ ≈ 7 and 50 TeV, respectively; (ii) perform competitive SUSY tests at the loop level in regions not accessible at the LHC; (iii) study heavy-ﬂavor and tau physics in ultra-rare decays beyond the LHC reach, and (iv) achieve the best potential in direct collider searches for dark matter, sterile neutrinos, and axion-like particles with masses up to ≈ 90 GeV. The tunnel can then be reused for a proton-proton collider, es-tablishing record center-of-mass collision energy, allowing unprecedented reach for direct searches for new particles up to the ≈ 50 TeV scale, and a diverse program of measurements of the Standard Model and Higgs boson, including a precision measurement of the Higgs self-coupling, and conclusively testing weakly-interacting massive particle scenarios of thermal relic dark matter. The FCC-ee and FCC-hh physics and accelerator programs are remarkably synergis-tic and complementary. The program builds on the stable funding provided by the CERN member states and the existing, long-standing worldwide partner-ships built via the LHC, but requires substantial contributions both intellectual and ﬁnancial from the US and other non-CERN-members to become a reality.
Fast cycling HTS based superconducting accelerator magnets: Feasibility study and readiness demonstration program driven by neutrino physics and muon collider needs

Development of energy-efficient fast cycling accelerator magnets is critical for the next generation of proton rapid cycling synchrotrons (RCS) for neutrino research and booster accelerators of future muon colliders. We see a unique opportunity for having such magnets to be built on base of High Temperature Superconductors (HTS). Besides being superconducting at relatively high temperatures, rare-earth HTS tapes have shown very small AC losses compared to low-temperature NbTi superconductor cables. Recent tests of the HTS-based 0.5 m long two-bore superconducting accelerator magnet have shown record high dB/dt ramping rates of about 300 T/s at 10 Hz repetition rate and 0.5 T B-field span. No temperature rise in 6 K cooling He was observed within the 0.003 K error setting the upper limit on the cryogenic power loss in the magnet conductor coil to be less than 0.2 W/m. Based on this result we outline a possible upgrade of this test magnet design to 2 T B-field in the 10 mm beam gap with the dB/dt ramping rates up to 1000 T/s. The power test results of this short sample magnet will be used to project both cryogenic and electrical power losses as a function of the magnet B-field and the dB/dt ramping rates. Then these projections will be scaled to the range of expected accelerator magnet beam gaps and B-fields for the proton and muon RCS accelerators. We invite collaborators to join these studies and call for support of the R&D program aimed at comprehensive demonstration of this approach that includes design, construction, and power tests of a long prototype of the HTS-based fast-cycling accelerator magnet by 2028.
Tactile-ViewGCN: Learning Shape Descriptor from Tactile Data using Graph Convolutional Network

For humans, our"senses of touch"have always been necessary for our ability to precisely and efficiently manipulate objects of all shapes in any environment, but until recently, not many works have been done to fully understand haptic feedback. This work proposed a novel method for getting a better shape descriptor than existing methods for classifying an object from multiple tactile data collected from a tactile glove. It focuses on improving previous works on object classification using tactile data. The major problem for object classification from multiple tactile data is to find a good way to aggregate features extracted from multiple tactile images. We propose a novel method, dubbed as Tactile-ViewGCN, that hierarchically aggregate tactile features considering relations among different features by using Graph Convolutional Network. Our model outperforms previous methods on the STAG dataset with an accuracy of 81.82%.
Snowmass2021 Cosmic Frontier White Paper: High Density Galaxy Clustering in the Regime of Cosmic Acceleration

Joint studies of imaging and spectroscopic samples, informed by theory and simulations, offer the potential for comprehensive tests of the cosmological model over redshifts z<1.5. Spectroscopic galaxy samples at these redshifts can be increased beyond the planned Dark Energy Spectroscopic Instrument (DESI) program by at least an order of magnitude, thus offering significantly more constraining power for these joint studies. Spectroscopic observations of these galaxies in the latter half of the 2020's and beyond would leverage the theory and simulation effort in this regime. In turn, these high density observations will allow enhanced tests of dark energy, physics beyond the standard model, and neutrino masses that will greatly exceed what is currently possible. Here, we present a coordinated program of simulations, theoretical modeling, and future spectroscopy that would enable precise cosmological studies in the accelerating epoch where the effects of dark energy are most apparent.
Investigating Coverage Guided Fuzzing with Mutation Testing

Coverage guided fuzzing (CGF) is an effective testing technique which has detected hundreds of thousands of bugs from various software applications. It focuses on maximizing code coverage to reveal more bugs during fuzzing. However, a higher coverage does not necessarily imply a better fault detection capability. Triggering a bug involves not only exercising the specific program path but also reaching interesting program states in that path. In this paper, we use mutation testing to improve CGF in detecting bugs. We use mutation scores as additional feedback to guide fuzzing towards detecting bugs rather than just covering code. To evaluate our approach, we conduct a well-designed experiment on 5 benchmarks. We choose the state-of-the-art fuzzing technique Zest as baseline and construct two modified techniques on it using our approach. The experimental results show that our approach can improve CGF in both code coverage and bug detection.
Extracting associations and meanings of objects depicted in artworks through bi-modal deep networks

We present a novel bi-modal system based on deep networks to address the problem of learning associations and simple meanings of objects depicted in"authored"images, such as fine art paintings and drawings. Our overall system processes both the images and associated texts in order to learn associations between images of individual objects, their identities and the abstract meanings they signify. Unlike past deep nets that describe depicted objects and infer predicates, our system identifies meaning-bearing objects ("signifiers") and their associations ("signifieds") as well as basic overall meanings for target artworks. Our system had precision of 48% and recall of 78% with an F1 metric of 0.6 on a curated set of Dutch vanitas paintings, a genre celebrated for its concentration on conveying a meaning of great import at the time of their execution. We developed and tested our system on fine art paintings but our general methods can be applied to other authored images.
Complex structure and efficient characterization of multiphoton split states in integrated circuits

Multi-photon split states, where each photon is in a different spatial mode, represent an essential resource for various quantum applications, yet their efficient characterization remains an open problem. Here, we formulate the general structure of their reduced spatial density matrices and identify the number of real and complex-valued independent coefficients, which in particular completely determine the distinguishability of all photons. Then, we show that this density matrix can be fully characterized by measuring correlations after photon interference in a static integrated circuit, where the required outputs scale sub-quadratically versus the number of photons. We present optimized circuit designs composed of segmented coupled waveguides, representing a linear optical neural network, which minimize the reconstruction error and facilitate robustness to fabrication deviations.
Strategies for Beam-Induced Background Reduction at Muon Colliders

Future collider detectors at muon colliders will be bombarded by Beam-Induced Backgrounds (BIB) due to the in-flight muon decays from the beam line. These backgrounds can inhibit the ability of the detector and subsequent data analysis to successfully reconstruct collision products. We explore methods for geometrically reducing these effects for use in the readout, triggering, and data analysis of future experiments. Studies are performed for a collision energy of 1.5~TeV, and a detector with a tungsten nozzle designed to block the majority of the BIB. In this context, detector strategies are explored to further reduce the BIB, with a focus on the innermost layers of the tracker where its density is highest. In addition, a conceptual design of a calorimeter built to reject BIB is presented.
CAR: Class-aware Regularizations for Semantic Segmentation

Recent segmentation methods, such as OCR and CPNet, utilizing"class level"information in addition to pixel features, have achieved notable success for boosting the accuracy of existing network modules. However, the extracted class-level information was simply concatenated to pixel features, without explicitly being exploited for better pixel representation learning. Moreover, these approaches learn soft class centers based on coarse mask prediction, which is prone to error accumulation. In this paper, aiming to use class level information more effectively, we propose a universal Class-Aware Regularization (CAR) approach to optimize the intra-class variance and inter-class distance during feature learning, motivated by the fact that humans can recognize an object by itself no matter which other objects it appears with. Three novel loss functions are proposed. The first loss function encourages more compact class representations within each class, the second directly maximizes the distance between different class centers, and the third further pushes the distance between inter-class centers and pixels. Furthermore, the class center in our approach is directly generated from ground truth instead of from the error-prone coarse prediction. Our method can be easily applied to most existing segmentation models during training, including OCR and CPNet, and can largely improve their accuracy at no additional inference overhead. Extensive experiments and ablation studies conducted on multiple benchmark datasets demonstrate that the proposed CAR can boost the accuracy of all baseline models by up to 2.23% mIOU with superior generalization ability. The complete code is available at https://github.com/edwardyehuang/CAR.