Miracle-dz/UniFAR-FTUs huggingface dataset:
Cooperative Pilot Spoofing in MU-MIMO Systems

In this letter, we consider downlink transmission of a multiuser multiple-input multiple-output (MU-MIMO) system with zero-forcing (ZF) precoders in the presence of multiple attackers. We propose a cooperative pilot spoofing attack (CPSA), where the attackers collaboratively impair the channel estimations in the uplink channel training phase, aiming at deteriorating the downlink throughput of the whole cell. We first evaluate the impacts of CPSA on the channel estimation and the downlink ZF precoding design, and then we derive an analytical expression for the achievable downlink sum-rate. Furthermore, we investigate the optimal attack strategy to minimize the achievable downlink sum-rate. We show that the optimization problem under consideration is a convex one so the global optimum could be obtained conveniently. Numerical results show that the CPSA attack results in a severe performance deterioration with the increase in the attacking power and the number of attackers.
The vanishing cohomology of non‐isolated hypersurface singularities

We employ the perverse vanishing cycles to show that each reduced cohomology group of the Milnor fiber, except the top two, can be computed from the restriction of the vanishing cycle complex to only singular strata with a certain lower bound in dimension. Guided by geometric results, we alternately use the nearby and vanishing cycle functors to derive information about the Milnor fiber cohomology via iterated slicing by generic hyperplanes. These lead to the description of the reduced cohomology groups, except the top two, in terms of the vanishing cohomology of the nearby section. We use it to compute explicitly the lowest (possibly nontrivial) vanishing cohomology group of the Milnor fiber.
A Weakly Supervised Region-Based Active Learning Method for COVID-19 Segmentation in CT Images

One of the key challenges in the battle against the Coronavirus (COVID-19) pandemic is to detect and quantify the severity of the disease in a timely manner. Computed tomographies (CT) of the lungs are effective for assessing the state of the infection. Unfortunately, labeling CT scans can take a lot of time and effort, with up to 150 minutes per scan. We address this challenge introducing a scalable, fast, and accurate active learning system that accelerates the labeling of CT scan images. Conventionally, active learning methods require the labelers to annotate whole images with full supervision, but that can lead to wasted efforts as many of the annotations could be redundant. Thus, our system presents the annotator with unlabeled regions that promise high information content and low annotation cost. Further, the system allows annotators to label regions using point-level supervision, which is much cheaper to acquire than per-pixel annotations. Our experiments on open-source COVID-19 datasets show that using an entropy-based method to rank unlabeled regions yields to significantly better results than random labeling of these regions. Also, we show that labeling small regions of images is more efficient than labeling whole images. Finally, we show that with only 7\% of the labeling effort required to label the whole training set gives us around 90\% of the performance obtained by training the model on the fully annotated training set. Code is available at: \url{this https URL}.
OVC-Net: Object-Oriented Video Captioning with Temporal Graph and Detail Enhancement.

Traditional video captioning requests a holistic description of the video, yet the detailed descriptions of the specific objects may not be available. Without associating the moving trajectories, these image-based data-driven methods cannot understand the activities from the spatio-temporal transitions in the inter-object visual features. Besides, adopting ambiguous clip-sentence pairs in training, it goes against learning the multi-modal functional mappings owing to the one-to-many nature. In this paper, we propose a novel task to understand the videos in object-level, named object-oriented video captioning. We introduce the video-based object-oriented video captioning network (OVC)-Net via temporal graph and detail enhancement to effectively analyze the activities along time and stably capture the vision-language connections under small-sample condition. The temporal graph provides useful supplement over previous image-based approaches, allowing to reason the activities from the temporal evolution of visual features and the dynamic movement of spatial locations. The detail enhancement helps to capture the discriminative features among different objects, with which the subsequent captioning module can yield more informative and precise descriptions. Thereafter, we construct a new dataset, providing consistent object-sentence pairs, to facilitate effective cross-modal learning. To demonstrate the effectiveness, we conduct experiments on the new dataset and compare it with the state-of-the-art video captioning methods. From the experimental results, the OVC-Net exhibits the ability of precisely describing the concurrent objects, and achieves the state-of-the-art performance.
Robust Identifiability in Linear Structural Equation Models of Causal Inference

In this work, we consider the problem of robust parameter estimation from observational data in the context of linear structural equation models (LSEMs). LSEMs are a popular and well-studied class of models for inferring causality in the natural and social sciences. One of the main problems related to LSEMs is to recover the model parameters from the observational data. Under various conditions on LSEMs and the model parameters the prior work provides efficient algorithms to recover the parameters. However, these results are often about generic identifiability. In practice, generic identifiability is not sufficient and we need robust identifiability: small changes in the observational data should not affect the parameters by a huge amount. Robust identifiability has received far less attention and remains poorly understood. Sankararaman et al. (2019) recently provided a set of sufficient conditions on parameters under which robust identifiability is feasible. However, a limitation of their work is that their results only apply to a small sub-class of LSEMs, called ``bow-free paths. '' In this work, we significantly extend their work along multiple dimensions. First, for a large and well-studied class of LSEMs, namely ``bow free'' models, we provide a sufficient condition on model parameters under which robust identifiability holds, thereby removing the restriction of paths required by prior work. We then show that this sufficient condition holds with high probability which implies that for a large set of parameters robust identifiability holds and that for such parameters, existing algorithms already achieve robust identifiability. Finally, we validate our results on both simulated and real-world datasets.
Transposer: Universal Texture Synthesis Using Feature Maps as Transposed Convolution Filter

Conventional CNNs for texture synthesis consist of a sequence of (de)-convolution and up/down-sampling layers, where each layer operates locally and lacks the ability to capture the long-term structural dependency required by texture synthesis. Thus, they often simply enlarge the input texture, rather than perform reasonable synthesis. As a compromise, many recent methods sacrifice generalizability by training and testing on the same single (or fixed set of) texture image(s), resulting in huge re-training time costs for unseen images. In this work, based on the discovery that the assembling/stitching operation in traditional texture synthesis is analogous to a transposed convolution operation, we propose a novel way of using transposed convolution operation. Specifically, we directly treat the whole encoded feature map of the input texture as transposed convolution filters and the features' self-similarity map, which captures the auto-correlation information, as input to the transposed convolution. Such a design allows our framework, once trained, to be generalizable to perform synthesis of unseen textures with a single forward pass in nearly real-time. Our method achieves state-of-the-art texture synthesis quality based on various metrics. While self-similarity helps preserve the input textures' regular structural patterns, our framework can also take random noise maps for irregular input textures instead of self-similarity maps as transposed convolution inputs. It allows to get more diverse results as well as generate arbitrarily large texture outputs by directly sampling large noise maps in a single pass as well.
Quantum Software Engineering: Landscapes and Horizons

Quantum software plays a critical role in exploiting the full potential of quantum computing systems. As a result, it is drawing increasing attention recently. This paper defines the term "quantum software engineering" and introduces a quantum software life cycle. Based on these, the paper provides a comprehensive survey of the current state of the art in the field and presents the challenges and opportunities that we face. The survey summarizes the technology available in the various phases of the quantum software life cycle, including quantum software requirements analysis, design, implementation, test, and maintenance. It also covers the crucial issue of quantum software reuse.
Anomalous dimensions for 
ϕn
 in scale invariant 
d=3
 theory

Recently it was shown that the scaling dimension of the operator $\phi^n$ in scale-invariant $d=3$ theory may be computed semiclassically, and this was verified to leading order (two loops) in perturbation theory at leading and subleading $n$. Here we extend this verification to six loops, once again at leading and subleading $n$. We then perform a similar exercise for a theory with a multiplet of real scalars and an $O(N)$ invariant hexic interaction. We also investigate the strong-coupling regime for this example.
Investigation of Sentiment Controllable Chatbot

Conventional seq2seq chatbot models attempt only to find sentences with the highest probabilities conditioned on the input sequences, without considering the sentiment of the output sentences. In this paper, we investigate four models to scale or adjust the sentiment of the chatbot response: a persona-based model, reinforcement learning, a plug and play model, and CycleGAN, all based on the seq2seq model. We also develop machine-evaluated metrics to estimate whether the responses are reasonable given the input. These metrics, together with human evaluation, are used to analyze the performance of the four models in terms of different aspects; reinforcement learning and CycleGAN are shown to be very attractive.
Unifying holographic inflation with holographic dark energy: A covariant approach

In the present paper, we use the holographic approach to describe the early-time acceleration and the late-time acceleration eras of our Universe in a unified manner. Such ``holographic unification'' is found to have a correspondence with various higher curvature cosmological models with or without matter fields. The corresponding holographic cutoffs are determined in terms of the particle horizon and its derivatives, or the future horizon and its derivatives. As a result, the holographic energy density we propose is able to merge various cosmological epochs of the Universe from a holographic point of view. We find the holographic correspondence of several $F(R)$ gravity models, including axion-$F(R)$ gravity models, of several Gauss-Bonnet $F(G)$ models and finally of $F(T)$ models, and in each case we demonstrate that it is possible to describe in a unified way inflation and late-time acceleration in the context of the same holographic model.
Brackett-γ as a Gold-standard Test of Star Formation Rates Derived from SED Fitting

Using a local reference sample of 21 galaxies, we compare observations of the 2.16 μm Brackett-γ (Brγ) hydrogen recombination line with predictions from the Prospector Bayesian inference framework, which was used to fit the broadband photometry of these systems. This is a clean test of the spectral-energy-distribution-derived star formation rates (SFRs), as dust is expected to be optically thin at this wavelength in nearly all galaxies; thus, the internal conversion of SFR to predicted line luminosity does not depend strongly on the adopted dust model and posterior dust parameters, as is the case for shorter-wavelength lines such as Hα. We find that Prospector predicts Brγ luminosities and equivalent widths with small offsets (∼0.05 dex) and scatter (∼0.2 dex), consistent with measurement uncertainties, though we caution that the derived offset is dependent on the choice of stellar isochrones. We demonstrate that even when the Prospector-derived dust attenuation does not well describe, e.g., Hα line properties or observed reddening between Hα and Brγ, the underlying SFRs are accurate, as verified by the dust-free Brγ comparison. Finally, we discuss in what ways Brγ might be able to help constrain model parameters when treated as an input to the model, and we comment on its potential as an accurate monochromatic SFR indicator in the era of JWST multiobject near-IR spectroscopy.
A preceding low-virulence strain pandemic inducing immunity against COVID-19

The COVID-19 pandemic is thought to began in Wuhan, China in December 2019. Mobility analysis identified East-Asia and Oceania countries to be highly-exposed to COVID-19 spread, consistent with the earliest spread occurring in these regions. However, here we show that while a strong positive correlation between case-numbers and exposure level could be seen early-on as expected, at later times the infection-level is found to be negatively correlated with exposure-level. Moreover, the infection level is positively correlated with the population size, which is puzzling since it has not reached the level necessary for population-size to affect infection-level through herd immunity. These issues are resolved if a low-virulence Corona-strain (LVS) began spreading earlier in China outside of Wuhan, and later globally, providing immunity from the later appearing high-virulence strain (HVS). Following its spread into Wuhan, cumulative mutations gave rise to the emergence of an HVS, known as SARS-CoV-2, starting the COVID-19 pandemic. We model the co-infection by an LVS and an HVS and show that it can explain the evolution of the COVID-19 pandemic and the non-trivial dependence on the exposure level to China and the population-size in each country. We find that the LVS began its spread a few months before the onset of the HVS and that its spread doubling-time is \sim1.59\pm0.17 times slower than the HVS. Although more slowly spreading, its earlier onset allowed the LVS to spread globally before the emergence of the HVS. In particular, in countries exposed earlier to the LVS and/or having smaller population-size, the LVS could achieve herd-immunity earlier, and quench the later-spread HVS at earlier stages. We find our two-parameter (the spread-rate and the initial onset time of the LVS) can accurately explain the current infection levels (R^2=0.74); p-value (p) of 5.2x10^-13). Furthermore, countries exposed early should have already achieved herd-immunity. We predict that in those countries cumulative infection levels could rise by no more than 2-3 times the current level through local-outbreaks, even in the absence of any containment measures. We suggest several tests and predictions to further verify the double-strain co-infection model and discuss the implications of identifying the LVS.
CHANGES IN ARABINOGALACTAN PROTEINS DURING SOMATIC EMBRYOGENESIS IN SUSPENSION IN VITRO CULTURES OF DACTYLIS GLOMERATA L.

ZAGORCHEV, L. , R. STOINEVA and M. ODJAKOVA, 2013. Changes in arabinogalactan proteins during somatic embryogenesis in suspension in vitro cultures of Dactylis glomerata L. Bulg. J. Agric. Sci., Supplement 2, 19: 35–38 Somatic embryogenesis is a remarkable process in plant cells in which somatic embryos are formed from a single somatic cell that could further regenerate into a whole plant. Arabinogalactan proteins (AGPs), a family of heavily glycosylated cell surface glycoproteins, are among the key regulators of somatic embryogenesis. The aim of the present study is to investigate the expression and localization of AGPs in embryogenic suspension cultures of Dactylis glomerata L. , treated with NaCl. We have used two concentrations of NaCl – 0.085 M, which stimulates somatic embryogenesis and 0.17 M, which inhibits the process. The addition of (β-D-glucosyl) 3 Yariv reagent to the growth medium, a synthetic phenylglycoside that specifi cally reacts with AGPs, inhibited both fresh mass accumulation and somatic embryogenesis. Many cells with abnormal shape were observed in cultures with Yariv reagent compared to controls showing the importance of AGPs for the development of plant cells. Salt stress showed differential effect on distribution of AGPs, recognized by LM2 and MAC207 antibodies as shown by immunoblot and immunofl uorescence analyses. While LM2 antibody recognizes a glucuronic acid – containing epitop, MAC207 is directed against L-arabinose containing epitop. MAC207-recognized AGPs are found predominantly in the intracellular fraction in controls and migrates to the cell wall in salt-treated cultures. LM2-recognized AGPs almost disappeared from the cell wall at 0.17 M NaCl. The pattern of AGPs is differing according to the applied salt stress and at least some of them could be associated with the process of somatic embryogenesis.
Evaluating Incidence and Impact Estimates of the COVID-19 Outbreak from Wuhan before Lockdown

Background: Wuhan, China was the epicenter of COVID-19 pandemic. The goal of current study is to understand the infection transmission dynamics before intervention measures were taken. Methods: Data and key events were searched through pubmed and internet. Epidemiological data were calculated using data extracted from a variety of data sources. Results: We established a timeline showing by January 1, 2020, Chinese authorities had been presented convincing evidence of human-to-human transmission; however, it was not until January 20, 2020 that this information was shared with the public. Our study estimated that there would have been 10989 total infected cases if interventions were taken on January 2, 2020, versus 239875 cases when lockdown was put in place on January 23, 2020. Conclusions: China's withholding of key information about the 2020 COVID-19 outbreak and its delayed response ultimately led to the largest public health crisis of this century and could have been avoided with earlier countermeasures.
Tunneling Responses of Mole Crickets (Orthoptera: Gryllotalpidae) to the Entomopathogenic Fungus, Beauveria bassiana

Abstract Greenhouse studies of mole cricket tunneling architecture were conducted with adult southern, Scapteriscus borellii Giglio-Tos and tawny, Scapteriscus vicinus Scudder, mole crickets exposed to Beauveria bassiana (Balsamo) Vuillemin. Three different strains of B. bassiana as well as the commercially available insecticide Talstar (bifenthrin) were evaluated for avoidance behaviors by examining tunneling characteristics. Each treated container was inspected 24 h after treatment for specific tunneling behaviors in association with the presence of a control agent and the cricket’s response to the conidia or chemical. One of the B. bassiana strains tested, DB-2, caused changes in mole cricket behavior, including significantly less new surface tunneling, fewer vertical tunnels descending into the soil, less tunneling along the perimeter of the containers, and significantly more occurrences of the crickets remaining in an area that reduced exposure to the conidia. Two of the other treatments, strain 10-22 and Talstar, produced some of these same altered behaviors in mole crickets. Mole crickets exposed to a third strain of B. bassiana, BotaniGard, as well as two carrier formulations did not exhibit these same levels of avoidance. These observations indicate that the presence of environmentally “friendly” control agents, such as entomogenous fungi, may affect pest behavior, and strain selection may be critical to eliminate detection and avoidance by the target insect.
Additively Homomorphical Encryption based Deep Neural Network for Asymmetrically Collaborative Machine Learning

The financial sector presents many opportunities to apply various machine learning techniques. Centralized machine learning creates a constraint which limits further applications in finance sectors. Data privacy is a fundamental challenge for a variety of finance and insurance applications that account on learning a model across different sections. In this paper, we define a new practical scheme of collaborative machine learning that one party owns data, but another party owns labels only, and term this \textbf{Asymmetrically Collaborative Machine Learning}. For this scheme, we propose a novel privacy-preserving architecture where two parties can collaboratively train a deep learning model efficiently while preserving the privacy of each party's data. More specifically, we decompose the forward propagation and backpropagation of the neural network into four different steps and propose a novel protocol to handle information leakage in these steps. Our extensive experiments on different datasets demonstrate not only stable training without accuracy loss, but also more than 100 times speedup compared with the state-of-the-art system.
TurboCC: A Practical Frequency-Based Covert Channel With Intel Turbo Boost

Covert channels are communication channels used by attackers to transmit information from a compromised system when the access control policy of the system does not allow doing so. Previous work has shown that CPU frequency scaling can be used as a covert channel to transmit information between otherwise isolated processes. Modern systems either try to save power or try to operate near their power limits in order to maximize performance, so they implement mechanisms to vary the frequency based on load. Existing covert channels based on this approach are either easily thwarted by software countermeasures or only work on completely idle systems. In this paper, we show how the automatic frequency scaling provided by Intel Turbo Boost can be used to construct a covert channel that is both hard to prevent without significant performance impact and can tolerate significant background system load. As Intel Turbo Boost selects the maximum CPU frequency based on the number of active cores, our covert channel modulates information onto the maximum CPU frequency by placing load on multiple additional CPU cores. Our prototype of the covert channel achieves a throughput of up to 61 bit/s on an idle system and up to 43 bit/s on a system with 25% utilization.