Miracle-dz/UniFAR-FTUs huggingface dataset:
A conceptual map of invasion biology: Integrating hypotheses into a consensus network

Abstract Background and aims Since its emergence in the mid‐20th century, invasion biology has matured into a productive research field addressing questions of fundamental and applied importance. Not only has the number of empirical studies increased through time, but also has the number of competing, overlapping and, in some cases, contradictory hypotheses about biological invasions. To make these contradictions and redundancies explicit, and to gain insight into the field’s current theoretical structure, we developed and applied a Delphi approach to create a consensus network of 39 existing invasion hypotheses. Results The resulting network was analysed with a link‐clustering algorithm that revealed five concept clusters (resource availability, biotic interaction, propagule, trait and Darwin’s clusters) representing complementary areas in the theory of invasion biology. The network also displays hypotheses that link two or more clusters, called connecting hypotheses, which are important in determining network structure. The network indicates hypotheses that are logically linked either positively (77 connections of support) or negatively (that is, they contradict each other; 6 connections). Significance The network visually synthesizes how invasion biology’s predominant hypotheses are conceptually related to each other, and thus, reveals an emergent structure – a conceptual map – that can serve as a navigation tool for scholars, practitioners and students, both inside and outside of the field of invasion biology, and guide the development of a more coherent foundation of theory. Additionally, the outlined approach can be more widely applied to create a conceptual map for the larger fields of ecology and biogeography.
Psychological Distress Among Primary Care Givers of Tuberculous Patients at TB Sanatorium Kotri & Hyderabad

Objective: To study the psychological health of the primary caregivers of TB patients.Methodology: This is cross-sectional descriptive study done from July 2015 to October 2015 at TB Sanatorium Kotri and TB Sanatorium Hyderabad. A sample of 158 self-professed primary caregivers chosen via convenience sampling after taking informed consent. Caregiver psychological distress was measured using interview based structured questionnaire which included the 42 point DAS scale (Urdu version). Results: Majority of respondents were male (60.8%), educated (78.5%) and living in joint families (57%). Caregivers mainly belonged to lower (35.4%) and middle (54.5%) economic class. Health worries (both personal and those related to the patient), emotional fatigue due to long and strict treatment regimen, decreased quality of life and uncertainty of positive treatment outcome were the main factors significantly weighing down psychological health of primary caregivers. Moderate mean levels of Depression (16.1), Anxiety (10.4) and Stress (19.1) were noted.Conclusion: Psychological wellbeing of the primary caregivers should be given adequate attention and psychiatric counselling should be provided to the caregivers so they may be better able to tend to the patients.
Feedforward and Recurrent Processing in Scene Segmentation: Electroencephalography and Functional Magnetic Resonance Imaging

In texture segregation, an example of scene segmentation, we can discern two different processes: texture boundary detection and subsequent surface segregation [Lamme, V. A. F. , Rodriguez-Rodriguez, V. , & Spekreijse, H. Separate processing dynamics for texture elements, boundaries and surfaces in primary visual cortex of the macaque monkey. Cerebral Cortex, 9, 406413, 1999]. Neural correlates of texture boundary detection have been found in monkey V1 [Sillito, A. M. , Grieve, K. L. , Jones, H. E. , Cudeiro, J., & Davis, J. Visual cortical mechanisms detecting focal orientation discontinuities. Nature, 378, 492496, 1995; Grosof, D. H. , Shapley, R. M. , & Hawken, M. J. Macaque-V1 neurons can signal illusory contours. Nature, 365, 550552, 1993], but whether surface segregation occurs in monkey V1 [Rossi, A. F. , Desimone, R. , & Ungerleider, L. G. Contextual modulation in primary visual cortex of macaques. Journal of Neuroscience, 21, 16981709, 2001; Lamme, V. A. F. The neurophysiology of figure ground segregation in primary visual-cortex. Journal of Neuroscience, 15, 16051615, 1995], and whether boundary detection or surface segregation signals can also be measured in human V1, is more controversial [Kastner, S. , De Weerd, P., & Ungerleider, L. G. Texture segregation in the human visual cortex: A functional MRI study. Journal of Neurophysiology, 83, 24532457, 2000]. Here we present electroencephalography (EEG) and functional magnetic resonance imaging data that have been recorded with a paradigm that makes it possible to differentiate between boundary detection and scene segmentation in humans. In this way, we were able to show with EEG that neural correlates of texture boundary detection are first present in the early visual cortex around 92 msec and then spread toward the parietal and temporal lobes. Correlates of surface segregation first appear in temporal areas (around 112 msec) and from there appear to spread to parietal, and back to occipital areas. After 208 msec, correlates of surface segregation and boundary detection also appear in more frontal areas. Blood oxygenation level-dependent magnetic resonance imaging results show correlates of boundary detection and surface segregation in all early visual areas including V1. We conclude that texture boundaries are detected in a feedforward fashion and are represented at increasing latencies in higher visual areas. Surface segregation, on the other hand, is represented in reverse hierarchical fashion and seems to arise from feedback signals toward early visual areas such as V1.
Identification and Antimicrobial Resistance Pattern of Staphylococcus aureus Isolated from Bovine Raw Milk in Addis Ababa, Ethiopia

Milk and its products are highly susceptible to microbial attack because of their rich composition. Therefore, the current cross-sectional study was conducted from November 2013 to April 2014 to isolate Staphylococcus aureus (S. aureus) and evaluate its in vitro antimicrobial resistance pattern from bovine raw milk of lactating dairy cows of five private dairy farms of Addis Ababa, Ethiopia. A total of 267 milk samples were randomly and aseptically collected and tested using bacteriological and antibiotic susceptibility tests. The findings were analyzed using WHONET 5.6 and SPSS Version 16 Softwares. The phenotypic results showed that out of the total 267 cultured raw milk samples 14(5.24%) were found to be coagulase positive S. aureus. All the positive isolates were subjected to antibiotic susceptibility tests against twelve antimicrobial agents of different antibiotic classes. The antibiogram profile of the tested S. aureus results revealed as large proportions of the isolates were found to be highly susceptible to Streptomycin (71.4%), Trimethoprim/Sulfamethoxazole (71.4%), Kanamycin (64.3%), Chloramphenicol (57.1%), Gentamycin (57.1%), and Methicillin (57.1%). However these isolates were highly resistant to Amoxicillin (78.6%), Tetracycline (78.6%), Ampicillin (71.4%), and Cloxacillin (57.1%). But the isolates were showed intermediate response to Neomycin (57.1%) and Polymyxin B (28.6%). Of the tested isolates 92.9% were developed a multidrug resistant. Hence, there should be a practice of proper use of antibiotics and hygienic measures in the farms, and genotypic based studies should be undertaken.
Chemotherapy-Associated Genomic Toxicity and Future of Cancer Treatment

Cancer genome is usually unstable and, therefore, constantly acquires changes at both the nucleotide sequence as well as chromosomal levels [1-3]. Ongoing genomic changes, which confer new characteristics to the recipient cells, underlie their progression to advanced disease states including acquisition of drug resistance and treatment failure. Data from our laboratory have shown that increased number of mutations correlates with poor survival of myeloma patients [2]. One of the consequences of genomic instability and increased mutational burden can also be the formation of more neoantigens which help recognition of cancer cells as non-self by immune system. However, continued acquisition of genomic changes can also give new characteristics to cancer cells which may help them escape immune surveillance [4]. Consistent with unstable genome, cancer cells display a number of genomic aberrations including increased levels of spontaneous DNA breaks. Using esophageal adenocarcinoma and multiple myeloma as model systems, we have shown that homologous recombination, the most precise DNA repair mechanism, is dysregulated (or spontaneously elevated) in cancer cells and contributes to ongoing genomic evolution [3,5], drug resistance [3] and growth of cancer cells in subcutaneous tumor model [6]. We have recently also shown that apurinic/apyrimidinic nucleases (APEX1 and APEX2) contribute to increased DNA breaks and homologous recombination activity in myeloma cells [7]. Cancer drugs which are genotoxic or induce DNA damage or breaks, either directly or indirectly, kill cancer cells by increasing the damage to their DNA. However, following such treatments the subsets of cancer cells which survive (and not killed by) as well as normal cells of the patient now have increased levels of DNA damage and breaks. This aspect of chemotherapy poses a risk of development of resistance to treatment in cancer cells and transformation of normal cells. Consistent with this view, we have shown that melphalan, a chemotherapeutic agent, induces homologous recombination activity and genomic instability in myeloma cells in vitro [7]. Similarly, certain chemotherapeutic agents have been linked to development of secondary cancers [8,9]. There are also reports which suggest that chemotherapy has higher likelihood of contributing to development of leukemia as compared to radiation. It is, therefore, necessary to develop drugs which target mechanisms underlying increased genomic damage and instability in cancer cells. Such drugs have potential to inhibit/delay progression by reducing genomic instability and evolution. There is also evidence that such drugs may have ability to increase cytotoxicity while minimizing/ reducing genomic toxicity caused by chemotherapeutic agents [7].
Firewall from Effective Field Theory

For an effective field theory in the background of an evaporating black hole with spherical symmetry, we consider non-renormalizable interactions and their relevance to physical effects. The background geometry is determined by the semi-classical Einstein equation for an uneventful horizon where the vacuum energy–momentum tensor is small for freely falling observers. Surprisingly, after Hawking radiation appears, the transition amplitude from the Unruh vacuum to certain multi-particle states grows exponentially with time for a class of higher-derivative operators after the collapsing matter enters the near-horizon region, despite the absence of large curvature invariants. Within the scrambling time, the uneventful horizon transitions towards a firewall, and eventually the effective field theory breaks down.
Paxos vs Raft: have we reached consensus on distributed consensus?

Distributed consensus is a fundamental primitive for constructing fault-tolerant, strongly-consistent distributed systems. Though many distributed consensus algorithms have been proposed, just two dominate production systems: Paxos, the traditional, famously subtle, algorithm; and Raft, a more recent algorithm positioned as a more understandable alternative to Paxos. In this paper, we consider the question of which algorithm, Paxos or Raft, is the better solution to distributed consensus? We analyse both to determine exactly how they differ by describing a simplified Paxos algorithm using Raft's terminology and pragmatic abstractions. We find that both Paxos and Raft take a very similar approach to distributed consensus, differing only in their approach to leader election. Most notably, Raft only allows servers with up-to-date logs to become leaders, whereas Paxos allows any server to be leader provided it then updates its log to ensure it is up-to-date. Raft's approach is surprisingly efficient given its simplicity as, unlike Paxos, it does not require log entries to be exchanged during leader election. We surmise that much of the understandability of Raft comes from the paper's clear presentation rather than being fundamental to the underlying algorithm being presented.
Secret Sharing based Secure Regressions with Applications

Nowadays, the utilization of the ever expanding amount of data has made a huge impact on web technologies while also causing various types of security concerns. On one hand, potential gains are highly anticipated if different organizations could somehow collaboratively share their data for technological improvements. On the other hand, data security concerns may arise for both data holders and data providers due to commercial or sociological concerns. To make a balance between technical improvements and security limitations, we implement secure and scalable protocols for multiple data holders to train linear regression and logistic regression models. We build our protocols based on the secret sharing scheme, which is scalable and efficient in applications. Moreover, our proposed paradigm can be generalized to any secure multiparty training scenarios where only matrix summation and matrix multiplications are used. We demonstrate our approach by experiments which shows the scalability and efficiency of our proposed protocols, and finally present its real-world applications.
The EXOTIME project: signals in the O–C diagrams of the rapidly pulsating subdwarfs DW Lyn, V1636 Ori, QQ Vir, and V541 Hya

Aims. We aim to investigate variations in the arrival time of coherent stellar pulsations due to the light-travel time effect to test for the presence of sub-stellar companions. Those companions are the key to one possible formation scenario of apparently single sub-dwarf B stars. Methods. We made use of an extensive set of ground-based observations of the four large amplitude p-mode pulsators DW Lyn, V1636 Ori, QQ Vir, and V541 Hya. Observations of the TESS space telescope are available on two of the targets. The timing method compares the phase of sinusoidal fits to the full multi-epoch light curves with phases from the fit of a number of subsets of the original time series. Results. Observations of the TESS mission do not sample the pulsations well enough to be useful due to the (currently) fixed two-minute cadence. From the ground-based observations, we infer evolutionary parameters from the arrival times. The residual signals show many statistically significant periodic signals, but no clear evidence for changes in arrival time induced by sub-stellar companions. The signals can be explained partly by mode beating effects. We derive upper limits on companion masses set by the observational campaign.
A polariton electric field sensor

We experimentally demonstrate a dipolar polariton based electric field sensor. We tune and optimize the sensitivity of the sensor by varying the dipole moment of polaritons. We show polariton interactions play an important role in determining the conditions for optimal electric field sensing, and achieve a sensitivity of 0.12 V-m$^{-1}$-Hz$^{-0.5}$. Finally we apply the sensor to illustrate that excitation of polaritons modify the electric field in a spatial region much larger than the optical excitation spot.
Liverpool-Maidanak monitoring of the Einstein Cross in 2006–2019

Quasar microlensing offers a unique opportunity to resolve tiny sources in distant active galactic nuclei and study compact object populations in lensing galaxies. We therefore searched for microlensing-induced variability of the gravitationally lensed quasar QSO 2237+0305 (Einstein Cross) using 4374 optical frames taken with the 2.0 m Liverpool Telescope and the 1.5 m Maidanak Telescope. These gVrRI frames over the 2006–2019 period were homogeneously processed to generate accurate long-term multi-band light curves of the four quasar images A–D. Through difference light curves, we found strong microlensing signatures. We then focused on the analytical modelling of two putative caustic-crossing events in image C, finding compelling evidence that this image experienced a double caustic crossing. Additionally, our overall results indicate that a standard accretion disc accounts reasonably well for the brightness profile of UV continuum emission sources and for the growth in source radius when the emission wavelength increases: Rλ ∝ λα, α = 1.33 ± 0.09. However, we caution that numerical microlensing simulations are required before firm conclusions can be reached on the UV emission scenario because the VRI-band monitoring during the first caustic crossing and one of our two α indicators lead to a few good solutions with α ≈ 1.
Secret Key Generation From Vector Gaussian Sources With Public and Private Communications

In this paper, we consider the problem of secret key generation with one-way communication through both a rate-limited public channel and a rate-limited secure channels where the public channel is from Alice to Bob and Eve and the secure channel is from Alice to Bob. In this model, we do not pose any constraints on the sources, i.e. Bob is not degraded to or less noisy than Eve. We obtain the optimal secret key rate in this problem, for the vector Gaussian sources setting. The characterization is derived by suitably applying the enhancement argument, and Proving a new extremal inequality. The extremal inequality can be seen as coupling of two extremal inequalities, which are related to the degraded compound MIMO Gaussian broadcast channel, and the vector generalization of Costa’s entropy power inequality, accordingly.
Stability analysis of an ensemble of simple harmonic oscillators

In this paper, we investigate the stability of the configurations of harmonic oscillator potential that are directly proportional to the square of the displacement. We derive expressions for fluctuations in partition function due to variations of the parameters, viz. the mass, temperature and the frequency of oscillators. Here, we introduce the Hessian matrix of the partition function as the model embedding function from the space of parameters to the set of real numbers. In this framework, we classify the regions in the parameter space of the harmonic oscillator fluctuations where they yield a stable statistical configuration. The mechanism of stability follows from the notion of the fluctuation theory. In Secs. 7 and 8, we provide the nature of local and global correlations and stability regions where the system yields a stable or unstable statistical basis, or it undergoes into geometric phase transitions. Finally, in Sec. 9, the comparison of results is provided with reference to other existing research.
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?

How can we collect and use a video dataset to further improve spatiotemporal 3D Convolutional Neural Networks (3D CNNs)? In order to positively answer this open question in video recognition, we have conducted an exploration study using a couple of large-scale video datasets and 3D CNNs. In the early era of deep neural networks, 2D CNNs have been better than 3D CNNs in the context of video recognition. Recent studies revealed that 3D CNNs can outperform 2D CNNs trained on a large-scale video dataset. However, we heavily rely on architecture exploration instead of dataset consideration. Therefore, in the present paper, we conduct exploration study in order to improve spatiotemporal 3D CNNs as follows: (i) Recently proposed large-scale video datasets help improve spatiotemporal 3D CNNs in terms of video classification accuracy. We reveal that a carefully annotated dataset (e.g., Kinetics-700) effectively pre-trains a video representation for a video classification task. (ii) We confirm the relationships between #category/#instance and video classification accuracy. The results show that #category should initially be fixed, and then #instance is increased on a video dataset in case of dataset construction. (iii) In order to practically extend a video dataset, we simply concatenate publicly available datasets, such as Kinetics-700 and Moments in Time (MiT) datasets. Compared with Kinetics-700 pre-training, we further enhance spatiotemporal 3D CNNs with the merged dataset, e.g., +0.9, +3.4, and +1.1 on UCF-101, HMDB-51, and ActivityNet datasets, respectively, in terms of fine-tuning. (iv) In terms of recognition architecture, the Kinetics-700 and merged dataset pre-trained models increase the recognition performance to 200 layers with the Residual Network (ResNet), while the Kinetics-400 pre-trained model cannot successfully optimize the 200-layer architecture.
Pattern Discovery in Colored Strings

In this article, we consider the problem of identifying patterns of interest in colored strings. A colored string is a string where each position is assigned one of a finite set of colors. Our task is to find substrings of the colored string that always occur followed by the same color at the same distance. The problem is motivated by applications in embedded systems verification, in particular, assertion mining. The goal there is to automatically find properties of the embedded system from the analysis of its simulation traces. We show that, in our setting, the number of patterns of interest is upper-bounded by O(n2), where n is the length of the string. We introduce a baseline algorithm, running in O(n2) time, which identifies all patterns of interest satisfying certain minimality conditions for all colors in the string. For the case where one is interested in patterns related to one color only, we also provide a second algorithm that runs in O(n2 log n) time in the worst case but is faster than the baseline algorithm in practice. Both solutions use suffix trees, and the second algorithm also uses an appropriately defined priority queue, which allows us to reduce the number of computations. We performed an experimental evaluation of the proposed approaches over both synthetic and real-world datasets, and found that the second algorithm outperforms the first algorithm on all simulated data, while on the real-world data, the performance varies between a slight slowdown (on half of the datasets) and a speedup by a factor of up to 11.
Edge collapse and subsequent longitudinal accretion in filament S242

Filament S242 is 25 pc long with massive clumps and YSO clusters concentrated in its end regions; it is considered a good example of edge collapse. We mapped this filament in the 12CO(1–0) and 13CO(1–0) lines. A large-scale velocity gradient along filament S242 has been detected; the relative velocity between the two end-clumps is ~3 km s−1, indicating an approaching motion between them. These signatures are consistent with the filament S242 being formed through the collapse of a single elongated entity, where an effect known as “gravitational focusing” drives the ends of the filament to collapse (edge collapse). Based on this picture, we estimate a collapse timescale of ~4.2 Myr, which is the time needed for a finite and elongated entity evolving to the observed filament S242. For the whole filament, we find that increases in surface densities lead to increases in velocity dispersion, which can be consistently explained as the result of self-gravity. We also calculated the contribution of longitudinal collapse to the observed velocity dispersion and found it to be the dominant effect in driving the gas motion near the end-clumps. We propose that our filament S242 is formed through a two-stage collapse model, where the edge collapse of a truncated filament is followed by a stage of longitudinal accretion toward the dense end-clumps.