Miracle-dz/UniFAR-FTUs huggingface dataset:
Objective Assessment of Social Skills Using Automated Language Analysis for Identification of Schizophrenia and Bipolar Disorder

Several studies have shown that speech and language features, automatically extracted from clinical interviews or spontaneous discourse, have diagnostic value for mental disorders such as schizophrenia and bipolar disorder. They typically make use of a large feature set to train a classifier for distinguishing between two groups of interest, i.e. a clinical and control group. However, a purely data-driven approach runs the risk of overfitting to a particular data set, especially when sample sizes are limited. Here, we first down-select the set of language features to a small subset that is related to a well-validated test of functional ability, the Social Skills Performance Assessment (SSPA). This helps establish the concurrent validity of the selected features. We use only these features to train a simple classifier to distinguish between groups of interest. Linear regression reveals that a subset of language features can effectively model the SSPA, with a correlation coefficient of 0.75. Furthermore, the same feature set can be used to build a strong binary classifier to distinguish between healthy controls and a clinical group (AUC = 0.96) and also between patients within the clinical group with schizophrenia and bipolar I disorder (AUC = 0.83).
Using tweets to understand changes in the spatial crime distribution for hockey events in Vancouver

Abstract The use of social media data for the spatial analysis of crime patterns during social events has proven to be instructive. This study analyzes the geography of crime considering hockey game days, criminal behaviour, and Twitter activity. Specifically, we consider the relationship between geolocated crime‐related Twitter activity and crime. We analyze six property crime types that are aggregated to the dissemination area base unit in Vancouver, for two hockey seasons through a game and non‐game temporal resolution. Using the same method, geolocated Twitter messages and environmental variables are aggregated to dissemination areas. We employ spatial clustering, dictionary‐based mining for tweets, spatial autocorrelation, and global and local regression models (spatial lag and geographically weighted regression). Findings show an important influence of Twitter data for theft‐from‐vehicle and mischief, mostly on hockey game days. Relationships from the geographically weighted regression models indicate that tweets are a valuable independent variable that can be used in explaining and understanding crime patterns.
Generated Loss and Augmented Training of MNIST VAE

The variational autoencoder (VAE) framework is a popular option for training unsupervised generative models, featuring ease of training and latent representation of data. The objective function of VAE does not guarantee to achieve the latter, however, and failure to do so leads to a frequent failure mode called posterior collapse. Even in successful cases, VAEs often result in low-precision reconstructions and generated samples. The introduction of the KL-divergence weight $\beta$ can help steer the model clear of posterior collapse, but its tuning is often a trial-and-error process with no guiding metrics. Here we test the idea of using the total VAE loss of generated samples (generated loss) as the proxy metric for generation quality, the related hypothesis that VAE reconstruction from the mean latent vector tends to be a more typical example of its class than the original, and the idea of exploiting this property by augmenting training data with generated variants (augmented training). The results are mixed, but repeated encoding and decoding indeed result in qualitatively and quantitatively more typical examples from both convolutional and fully-connected MNIST VAEs, suggesting that it may be an inherent property of the VAE framework.
The Scientific Method in the Science of Machine Learning

In the quest to align deep learning with the sciences to address calls for rigor, safety, and interpretability in machine learning systems, this contribution identifies key missing pieces: the stages of hypothesis formulation and testing, as well as statistical and systematic uncertainty estimation -- core tenets of the scientific method. This position paper discusses the ways in which contemporary science is conducted in other domains and identifies potentially useful practices. We present a case study from physics and describe how this field has promoted rigor through specific methodological practices, and provide recommendations on how machine learning researchers can adopt these practices into the research ecosystem. We argue that both domain-driven experiments and application-agnostic questions of the inner workings of fundamental building blocks of machine learning models ought to be examined with the tools of the scientific method, to ensure we not only understand effect, but also begin to understand cause, which is the raison d'\^{e}tre of science.
Association between obesity in 4‐ to 7‐year‐old children and eight types of crime: a hierarchical linear modelling approach

Evidence of the association between childhood obesity and neighbourhood crime is inconclusive. Most previous studies have included children of all ages, and few have examined different types of crime. The objective of this study was to investigate the association between obesity and eight different types of crime (i.e. commercial robbery, street robbery, assault, other violence, commercial break and enter, residential break and enter, theft of vehicle and theft from vehicle) among 4‐ to 7‐year‐old children in a large western Canadian city.
Measurement of 3D bubble distribution using digital inline holography

The paper presents a hybrid bubble hologram processing approach for measuring the size and 3D distribution of bubbles over a wide range of size and shape. The proposed method consists of five major steps, including image enhancement, digital reconstruction, small bubble segmentation, large bubble/cluster segmentation, and post-processing. Two different segmentation approaches are proposed to extract the size and the location of bubbles in different size ranges from the 3D reconstructed optical field. Specifically, a small bubble is segmented based on the presence of the prominent intensity minimum in its longitudinal intensity profile, and its depth is determined by the location of the minimum. In contrast, a large bubble/cluster is segmented using a modified watershed segmentation algorithm and its depth is measured through a wavelet-based focus metric. Our processing approach also determines the inclination angle of a large bubble with respect to the hologram recording plane based on the depth variation along its edge on the plane. The accuracy of our processing approach on the measurements of bubble size, location and inclination is assessed using the synthetic bubble holograms and a 3D printed physical target. The holographic measurement technique is further implemented to capture the fluctuation of instantaneous gas leakage rate from a ventilated supercavity generated in a water tunnel experiment. Overall, our paper introduces a low cost, compact and high-resolution bubble measurement technique that can be used for characterizing low void fraction bubbly flow in a broad range of applications.
Characterizing ABC-Transporter Substrate-Likeness Using a Clean-Slate Genetic Background

Mutations in ATP Binding Cassette (ABC)-transporter genes can have major effects on the bioavailability and toxicity of the drugs that are ABC-transporter substrates. Consequently, methods to predict if a drug is an ABC-transporter substrate are useful for drug development. Such methods traditionally relied on literature curated collections of ABC-transporter dependent membrane transfer assays. Here, we used a single large-scale dataset of 376 drugs with relative efficacy on an engineered yeast strain with all ABC-transporter genes deleted (ABC-16), to explore the relationship between a drug’s chemical structure and ABC-transporter substrate-likeness. We represented a drug’s chemical structure by an array of substructure keys and explored several machine learning methods to predict the drug’s efficacy in an ABC-16 yeast strain. Gradient-Boosted Random Forest models outperformed all other methods with an AUC of 0.723. We prospectively validated the model using new experimental data and found significant agreement with predictions. Our analysis expands the previously reported chemical substructures associated with ABC-transporter substrates and provides an alternative means to investigate ABC-transporter substrate-likeness.
Three Methods for Training on Bandit Feedback

There are three quite distinct ways to train a machine learning model on recommender system logs. The first method is to model the reward prediction for each possible recommendation to the user, at the scoring time the best recommendation is found by computing an argmax over the personalized recommendations. This method obeys principles such as the conditionality principle and the likelihood principle. A second method is useful when the model does not fit reality and underfits. In this case, we can use the fact that we know the distribution of historical recommendations (concentrated on previously identified good actions with some exploration) to adjust the errors in the fit to be evenly distributed over all actions. Finally, the inverse propensity score can be used to produce an estimate of the decision rules expected performance. The latter two methods violate the conditionality and likelihood principle but are shown to have good performance in certain settings. In this paper we review the literature around this fundamental, yet often overlooked choice and do some experiments using the RecoGym simulation environment.
Joint Time and Power Allocation Algorithm in NOMA Relaying Network

Nonorthogonal multiple access (NOMA) is one of the promising access techniques in 5G network. The application of relay in NOMA system is a hotspot in recent research. NOMA-based cooperative relay network can achieve a higher spectral efficiency and a lower outage probability. In this paper, we analyse the performance of the two-hop DF relay NOMA network scenario, where the number of cell edge users is more than the cell center user, and obtained the closed-form expression of the user's ergodic rates and outage probabilities under the high signal-to-noise (SNR) ratio. Then, we establish an optimization model to maximize the system rates, and a joint optimal time and power allocation algorithm based on the exhaustive search and the binary algorithm is proposed. Simulation results show that the proposed scheme can outperform exiting scheme in terms of achieving a higher ergodic sum rate, a lower outage probability under the premise of fairness.
Preventing information leakage in NDN with name and flow filters. (Prévenir la fuite d'information dans les NDN grâce aux filtres de noms et de flux)

In recent years, Named Data Networking (NDN) has emerged as one of the most promising future networking architectures. To be adopted at Internet scale, NDN needs to resolve the inherent issues of the current Internet. Since information leakage from an enterprise is one of the big issues even in the Internet and it is very crucial to assess the risk before replacing the Internet with NDN completely, this thesis investigates whether a new security threat causing the information leakage can happen in NDN. Assuming that (i) a computer is located in the enterprise network that is based on an NDN architecture, (ii) the computer has already been compromised by suspicious media such as a malicious email, and (iii) the company installs a firewall connected to the NDN-based future Internet, this thesis focuses on a situation that the compromised computer (i.e., malware) attempts to send leaked data to the outside attacker. The contributions of this thesis are fivefold. Firstly, this thesis proposes an information leakage attack through a Data and through an Interest in NDN. Secondly, in order to address the information leakage attack, this thesis proposes an NDN firewall which monitors and processes the NDN traffic coming from the consumers with the whitelist and blacklist. Thirdly, this thesis proposes an NDN name filter to classify a name in the Interest as legitimate or not. The name filter can, indeed, reduce the throughput per Interest, but to ameliorate the speed of this attack, malware can send numerous Interests within a short period of time. Moreover, the malware can even exploit an Interest with an explicit payload in the name (like an HTTP POST message in the Internet), which is out of scope in the proposed name filter and can increase the information leakage throughput by adopting a longer payload. To take traffic flow to the NDN firewall from the consumer into account, fourthly, this thesis proposes an NDN flow monitored at an NDN firewall. Fifthly, in order to deal with the drawbacks of the NDN name filter, this thesis proposes an NDN flow filter to classify a flow as legitimate or not. The performance evaluation shows that the flow filter complements the name filter and greatly chokes the information leakage throughput
Swarm intelligent-based congestion management using optimal rescheduling of generators

Congestion management (CM) refers to the controlling of transmission system such that the power transfer/flow limits are observed. In the restructured electrical system, the challenges of CM for the system operator (SO) is to maintain the desired level of system reliability and security in the short and long terms, while improving the market/system efficiency. In this paper, the CM problem is tackled by using the centralised optimisation, i.e., optimal rescheduling of generators, which in turn is solved by using the Swarm intelligent techniques. Here, the CM problem is solved by using the particle swarm optimisation (PSO), fitness distance ratio-PSO (FDR-PSO) and fuzzy adaptive-PSO (FA-PSO). First, the generators are selected based on sensitivity to the over-loaded transmission line, and then these generators are rescheduled to remove the congestion in the transmission line. The suitability and effectiveness of the proposed CM approach is examined on the standard IEEE 30 bus and practical Indian 75 bus systems.
Research Article: Envisioning the Future of Water Governance: A Survey of Central Arizona Water Decision Makers

The future of the American West depends on sustainable water resource governance. A variety of uncertainties associated with limited freshwater supplies, population growth, land use change, drought, and climate change impacts present substantial challenges. To inform decision making, managers are adopting new techniques such as scenario planning to understand how water resources might change and what practices can support economic, environmental, and social sustainability. Scenario planning can be informed by understanding the normative future preferences of a variety of stakeholders, including decision makers, who influence water governance. This article presents a survey of central Arizona decision makers to understand their visions for a desirable future for the water system in terms of supply, delivery, demand, outflow, and crosscutting activities. Principle components analysis is used to identify patterns underlying responses about preferences for each domain of the system and correlation analysis is used to evaluate associations between themes across the domains. The results reveal two distinct visions for water in central Arizona—one in which water experts and policy makers pursue supply augmentation to serve metropolitan development, and another in which broadened public engagement is used in conjunction with policy tools to reduce water consumption, restore ecosystem services, and limit metropolitan expansion. The results of this survey will inform the development of a set of normative scenarios for use in exploratory modeling and anticipatory governance activities.
Evolutionary relationships and reproductive ecology of endemic Sorbus species in south west UK: Implications for conservation.

2 Acknowledgments . 4 Chapter 1: General Introduction . 20 1.1 Plant conservation . 20 1.2 Taxonomic complexity and diversification . 21 1.3 Hybridisation and polyploidy . 22 1.4 Polyploid formation . 23 1.5 Polyploid speciation . 25 1.5. 1 Reproductive isolation . 26 1.5. 2 Evolution of new breeding systems: Polyploidy and apomixis . 27 1.6 Methodological challenges for genetic studies of apomictic polyploids. 28 1.6. 1 Molecular markers. 28 1.6. 2 Development of molecular methods for this study.30 1.6. 3 Molecular analysis of mixed ploidy groups containing sexual and apomictic individuals.31 1.7 Pollination and population sustainability.33 1.8 Sorbus as a model system of a Taxonomically Complex Group . 35 1.8. 1 Life history . 36 1.8. 2 Reproduction in Sorbus . 40 1.8. 3 Taxonomic complexity of Sorbus . 42 7 1.8. 4 What constitutes a species in Sorbus? 44 1.8. 5 Diversification in Sorbus . 45 1.8. 6 Conservation status. 47 1.9 Sorbus in Devon and north Somerset . 48 1.9. 1 Distribution . 48 1.9. 2 History and origins of the Devon and north Somerset Sorbus . 53 1.9. 3 Conservation and threats . 55 1.9. 4 Tree identification and sampling.58 1.10 Conclusion . 66 1.11 Thesis aims and structure . 66 References . 69 Chapter 2: Apomixis and hybridisation drives reticulate evolution and phyletic differentiation in Sorbus L. : Implications for conservation . 80 Abstract . 81. 81 2.
Alaska Marine Mammal Stock Assessments, 2018

NOTE – NMFS is in the process of reviewing humpback whale stock structure under the Marine Mammal Protection Act (MMPA) in light of the 14 Distinct Population Segments established under the Endangered Species Act (ESA) (81 FR 62259, 8 September 2016). A complete revision of the humpback whale stock assessments will be postponed until this review is complete. In the interim, new information on humpback whale mortality and serious injury is provided within this report.
Alaska marine mammal stock assessments, 2011

indicates data are unknown. UNDET (undetermined) PBR indicates data are available to calculate a PBR level but a determination has been made that calculating a PBR level using those data is inappropriate (see stock assessment for details). Species Stock N (est) CV N(min) Survey interval/ year of last survey Rmax F(r) PBR Fishery mort. Subsist. mort. Total mort. Status Baird’s beaked whale Alaska N/A N/A 0.04 0.50 N/A 0 0 0 NS Bearded seal Alaska N/A N/A 0.12 0.50 N/A 2.7 6,788 6,791 NS Beluga whale Beaufort Sea 39,258 0.23 32,453 19/1992 0.04 0.50 UNDET 0 126 126 NS Beluga whale E. Chukchi Sea 3,710 N/A 3,710 12/1998 0.04 1.00 UNDET 0 94.2 94.2 NS Beluga whale E. Bering Sea 28,406 0.24 20,231 10/2000 0.04 1.00 UNDET 0 192.6 192.6 NS Beluga whale Bristol Bay 2,877 0.2 2,467 6/2005 0.04 1.00 49 0.8 20.0 20.8 NS Beluga whale Cook Inlet 345 0.13 309 1/2010 0.04 0.1 UNDET 0 0 0 S Bowhead whale W. Arctic 10,545 0.13 9,472 9/2001 0.04 0.50 95 0.2 39.6 40 S Cuvier’s beaked whale Alaska N/A N/A 0.04 0.50 N/A 0 0 0 NS Dall’s porpoise Alaska 83,400 0.097 N/A 15/1993 0.04 1.00 UNDET 28.4 0 28.4 NS Fin whale NE Pacific 5,700 N/A 5,700 7/2003 0.04 0.10 11.4 0 0 0.4 S Gray whale E. N. Pacific 19,126 0.07 18,017 2/2007 0.04 1.00 360 3.3 121 127 NS Harbor porpoise SE Alaska 11,146 0.242 9,116 12/1997 0.04 0.50 UNDET 22.8 0 22.8 S Harbor porpoise Gulf of Alaska 31,046 0.214 25,987 11/1998 0.04 0.50 UNDET 72 0 74 S Harbor porpoise Bering Sea 48,215 0.223 40,039 10/1999 0.04 0.50 UNDET 0.7 0 2 S Harbor seal Aleutian Islands 3,579 3,313 3/2010 0.12 0.50 99 0.4 90 NS Harbor seal Pribilof Islands 232 232 3/2010 0.12 0.50 7 0.4 0 NS Harbor seal Bristol Bay 18,577 17,690 3/2010 0.12 1.0 1,061 0.4 141 NS NOAA-TM-AFSC-234 Allen, B. M. , and R. P. Angliss
Acute toxicity and behavioral response of freshwater fish, Mystus vittatus exposed to pulp mill effluent

has been widely recommended as a preliminary step in toxicological studies on fishes (McLeay, 1976; Whittle and Flood, 1977; USEPA, 1973, 2005; APHA, 1998, 2005; Chapman, 2000; Ali and Sree- Krishnan, 2001; ASTM, 2002; Parrott et al., 2006; Moreira et al., 2008). The static bioassay procedures to study the toxicity of pulp and papermill effluent to variety
Fast and Slow Responses to Global Warming: Sea Surface Temperature and Precipitation Patterns

AbstractThe time-dependent response of sea surface temperature (SST) to global warming and the associated atmospheric changes are investigated based on a 1% yr−1 CO2 increase to the quadrupling experiment of the Geophysical Fluid Dynamics Laboratory Climate Model, version 2.1. The SST response consists of a fast component, for which the ocean mixed layer is in quasi equilibrium with the radiative forcing, and a slow component owing to the gradual warming of the deeper ocean in and beneath the thermocline. A diagnostic method is proposed to isolate spatial patterns of the fast and slow responses. The deep ocean warming retards the surface warming in the fast response but turns into a forcing for the slow response. As a result, the fast and slow responses are nearly opposite to each other in spatial pattern, especially over the subpolar North Atlantic/Southern Ocean regions of the deep-water/bottom-water formation, and in the interhemispheric SST gradient between the southern and northern subtropics. Wind–e.
Cool tropical temperatures shift the global δ18O‐T relationship: An explanation for the ice core δ18O ‐ borehole thermometry conflict?

The discrepancy between central Greenland borehole temperatures and the isotopic composition of Last Glacial Maximum ice can be explained by a shift in the δ18O‐T relationship for the hydrological cycle linked to cooler tropical temperatures. This concept is illustrated using a simple Rayleigh distillation model. An estimate for α=Δδ18 O/ΔT (LGM‐Holocene) of −0.37 ‰/°C is determined with a simple graphical technique.
Differences in Head Orientation between Speakers and Listeners in Multi-party Conversations

We investigated whether people can identify who is speaking in multiparty conversations solely on the basis of the head orientations of the participants in the conversation. Two experiments were conducted using a virtual meeting room. Participants were shown the head orientations of four virtual humans and were asked to identify the speaker. In the first experiment participants were shown static frames, in the second an animation of an entire speaker turn. Head orientation data for both frame and animation experiment were taken from real-life meetings. Given that the animations provide more context, we assumed better scores for this experiment. However, participants did not score significantly better in the animation experiment (42.36%) compared to the static experiment (40.63%).
The Biogeochemical Sulfur Cycle of Marine Sediments

Microbial dissimilatory sulfate reduction to sulfide is a predominant terminal pathway of organic matter mineralization in the anoxic seabed. Chemical or microbial oxidation of the produced sulfide establishes a complex network of pathways in the sulfur cycle, leading to intermediate sulfur species and partly back to sulfate. The intermediates include elemental sulfur, polysulfides, thiosulfate, and sulfite, which are all substrates for further microbial oxidation, reduction or disproportionation. New microbiological discoveries, such as long-distance electron transfer through sulfide oxidizing cable bacteria, add to the complexity. Isotope exchange reactions play an important role for the stable isotope geochemistry and for the experimental study of sulfur transformations using radiotracers. Microbially catalyzed processes are partly reversible whereby the back-reaction affects our interpretation of radiotracer experiments and provides a mechanism for isotope fractionation. We here review the progress and current status in our understanding of the sulfur cycle in the seabed with respect to its microbial ecology, biogeochemistry, and isotope geochemistry.