Oftentimes, the focus on mobile websites isn’t on adding as much information as possible or even as much detail. It’s all about making the mobile viewing experience as simple and enjoyable as the web designer possibly can. People who use their mobile devices for browsing and research do not have as much time or patience …

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Communication has been quite a challenge during the COVID-19 pandemic, and data visualization hasn't been the most helpful given the low quality of the data – see Amanda Makulec's plea to think harder about making another coronavirus chart. A great example of how to do things right is the widely-circulated Flatten the Curve information graphic/cartoon. […]

This paper develops a method for obtaining guaranteed outer approximations for global attractors of continuous and discrete time nonlinear dynamical systems. The method is based on a hierarchy of semidefinite programming problems of increasing size with guaranteed convergence to the global attractor. The approach taken follows an established line of reasoning, where we first characterize the global attractor via an infinite dimensional linear programming problem (LP) in the space of Borel measures. The dual to this LP is in the space of continuous functions and its feasible solutions provide guaranteed outer approximations to the global attractor. For systems with polynomial dynamics, a hierarchy of finite-dimensional sum-of-squares tightenings of the dual LP provides a sequence of outer approximations to the global attractor with guaranteed convergence in the sense of volume discrepancy tending to zero. The method is very simple to use and based purely on convex optimization. Numerical examples with the code available online demonstrate the method.

Let $H$ be an infinite dimensional, reflexive, separable Hilbert space and $NA(H)$ the class of all norm-attainble operators on $H.$ In this note, we study an implicit scheme for a canonical representation of nonexpansive contractions in norm-attainable classes.

Temporal event segmentation of a long video into coherent events requires a high level understanding of activities' temporal features. The event segmentation problem has been tackled by researchers in an offline training scheme, either by providing full, or weak, supervision through manually annotated labels or by self-supervised epoch based training. In this work, we present a continual learning perceptual prediction framework (influenced by cognitive psychology) capable of temporal event segmentation through understanding of the underlying representation of objects within individual frames. Our framework also outputs attention maps which effectively localize and track events-causing objects in each frame. The model is tested on a wildlife monitoring dataset in a continual training manner resulting in $80\%$ recall rate at $20\%$ false positive rate for frame level segmentation. Activity level testing has yielded $80\%$ activity recall rate for one false activity detection every 50 minutes.

A standard approach to evaluating language models analyzes how models assign probabilities to valid versus invalid syntactic constructions (i.e. is a grammatical sentence more probable than an ungrammatical sentence). Our work uses ambiguous relative clause attachment to extend such evaluations to cases of multiple simultaneous valid interpretations, where stark grammaticality differences are absent. We compare model performance in English and Spanish to show that non-linguistic biases in RNN LMs advantageously overlap with syntactic structure in English but not Spanish. Thus, English models may appear to acquire human-like syntactic preferences, while models trained on Spanish fail to acquire comparable human-like preferences. We conclude by relating these results to broader concerns about the relationship between comprehension (i.e. typical language model use cases) and production (which generates the training data for language models), suggesting that necessary linguistic biases are not present in the training signal at all.

Position encoding (PE), an essential part of self-attention networks (SANs), is used to preserve the word order information for natural language processing tasks, generating fixed position indices for input sequences. However, in cross-lingual scenarios, e.g. machine translation, the PEs of source and target sentences are modeled independently. Due to word order divergences in different languages, modeling the cross-lingual positional relationships might help SANs tackle this problem. In this paper, we augment SANs with emph{cross-lingual position representations} to model the bilingually aware latent structure for the input sentence. Specifically, we utilize bracketing transduction grammar (BTG)-based reordering information to encourage SANs to learn bilingual diagonal alignments. Experimental results on WMT'14 English$Rightarrow$German, WAT'17 Japanese$Rightarrow$English, and WMT'17 Chinese$Leftrightarrow$English translation tasks demonstrate that our approach significantly and consistently improves translation quality over strong baselines. Extensive analyses confirm that the performance gains come from the cross-lingual information.

Human visual attention is a complex phenomenon. A computational modeling of this phenomenon must take into account where people look in order to evaluate which are the salient locations (spatial distribution of the fixations), when they look in those locations to understand the temporal development of the exploration (temporal order of the fixations), and how they move from one location to another with respect to the dynamics of the scene and the mechanics of the eyes (dynamics). State-of-the-art models focus on learning saliency maps from human data, a process that only takes into account the spatial component of the phenomenon and ignore its temporal and dynamical counterparts. In this work we focus on the evaluation methodology of models of human visual attention. We underline the limits of the current metrics for saliency prediction and scanpath similarity, and we introduce a statistical measure for the evaluation of the dynamics of the simulated eye movements. While deep learning models achieve astonishing performance in saliency prediction, our analysis shows their limitations in capturing the dynamics of the process. We find that unsupervised gravitational models, despite of their simplicity, outperform all competitors. Finally, exploiting a crowd-sourcing platform, we present a study aimed at evaluating how strongly the scanpaths generated with the unsupervised gravitational models appear plausible to naive and expert human observers.

High-resolution remote sensing images (HRRSIs) contain substantial ground object information, such as texture, shape, and spatial location. Semantic segmentation, which is an important task for element extraction, has been widely used in processing mass HRRSIs. However, HRRSIs often exhibit large intraclass variance and small interclass variance due to the diversity and complexity of ground objects, thereby bringing great challenges to a semantic segmentation task. In this paper, we propose a new end-to-end semantic segmentation network, which integrates lightweight spatial and channel attention modules that can refine features adaptively. We compare our method with several classic methods on the ISPRS Vaihingen and Potsdam datasets. Experimental results show that our method can achieve better semantic segmentation results. The source codes are available at https://github.com/lehaifeng/SCAttNet.

In the era of Internet of Things (IoT), Malware has been proliferating exponentially over the past decade. Traditional anti-virus software are ineffective against modern complex Malware. In order to address this challenge, researchers have proposed Hardware-assisted Malware Detection (HMD) using Hardware Performance Counters (HPCs). The HPCs are used to train a set of Machine learning (ML) classifiers, which in turn, are used to distinguish benign programs from Malware. Recently, adversarial attacks have been designed by introducing perturbations in the HPC traces using an adversarial sample predictor to misclassify a program for specific HPCs. These attacks are designed with the basic assumption that the attacker is aware of the HPCs being used to detect Malware. Since modern processors consist of hundreds of HPCs, restricting to only a few of them for Malware detection aids the attacker. In this paper, we propose a Moving target defense (MTD) for this adversarial attack by designing multiple ML classifiers trained on different sets of HPCs. The MTD randomly selects a classifier; thus, confusing the attacker about the HPCs or the number of classifiers applied. We have developed an analytical model which proves that the probability of an attacker to guess the perfect HPC-classifier combination for MTD is extremely low (in the range of $10^{-1864}$ for a system with 20 HPCs). Our experimental results prove that the proposed defense is able to improve the classification accuracy of HPC traces that have been modified through an adversarial sample generator by up to 31.5%, for a near perfect (99.4%) restoration of the original accuracy.

Moving a module in a modular robot is a very complex and error-prone process. Unlike in swarm, in the modular robots we are targeting, the moving module must keep the connection to, at least, one other module. In order to miniaturize each module to few millimeters, we have proposed a design which is using electrostatic actuator. However, this movement is composed of several attachment, detachment creating the movement and each small step can fail causing a module to break the connection. The idea developed in this paper consists in creating a new kind of deformable module allowing a movement which keeps the connection between the moving and the fixed modules. We detail the geometry and the practical constraints during the conception of this new module. We then validate the possibility of movement for a module in an existing configuration. This implies the cooperation of some of the modules placed along the path and we show in simulation that it exists a motion process to reach every free positions of the surface for a given configuration.

While the exact definition and implementation of accountability depend on the specific context, at its core accountability describes a mechanism that will make decisions transparent and often provides means to sanction "bad" decisions. As such, accountability is specifically relevant for Cyber-Physical Systems, such as robots or drones, that embed themselves into a human society, take decisions and might cause lasting harm. Without a notion of accountability, such systems could behave with impunity and would not fit into society. Despite its relevance, there is currently no agreement on its meaning and, more importantly, no way to express accountability properties for these systems. As a solution we propose to express the accountability properties of systems using Structural Causal Models. They can be represented as human-readable graphical models while also offering mathematical tools to analyze and reason over them. Our central contribution is to show how Structural Causal Models can be used to express and analyze the accountability properties of systems and that this approach allows us to identify accountability patterns. These accountability patterns can be catalogued and used to improve systems and their architectures.

Person re-identification (re-ID) remains challenging in a real-world scenario, as it requires a trained network to generalise to totally unseen target data in the presence of variations across domains. Recently, generative adversarial models have been widely adopted to enhance the diversity of training data. These approaches, however, often fail to generalise to other domains, as existing generative person re-identification models have a disconnect between the generative component and the discriminative feature learning stage. To address the on-going challenges regarding model generalisation, we propose an end-to-end domain adaptive attention network to jointly translate images between domains and learn discriminative re-id features in a single framework. To address the domain gap challenge, we introduce an attention module for image translation from source to target domains without affecting the identity of a person. More specifically, attention is directed to the background instead of the entire image of the person, ensuring identifying characteristics of the subject are preserved. The proposed joint learning network results in a significant performance improvement over state-of-the-art methods on several benchmark datasets.

The temporal segmentation of events is an essential task and a precursor for the automatic recognition of human actions in the video. Several attempts have been made to capture frame-level salient aspects through attention but they lack the capacity to effectively map the temporal relationships in between the frames as they only capture a limited span of temporal dependencies. To this end we propose a complete end-to-end supervised learning approach that can better learn relationships between actions over time, thus improving the overall segmentation performance. The proposed hierarchical recurrent attention framework analyses the input video at multiple temporal scales, to form embeddings at frame level and segment level, and perform fine-grained action segmentation. This generates a simple, lightweight, yet extremely effective architecture for segmenting continuous video streams and has multiple application domains. We evaluate our system on multiple challenging public benchmark datasets, including MERL Shopping, 50 salads, and Georgia Tech Egocentric datasets, and achieves state-of-the-art performance. The evaluated datasets encompass numerous video capture settings which are inclusive of static overhead camera views and dynamic, ego-centric head-mounted camera views, demonstrating the direct applicability of the proposed framework in a variety of settings.

Public key encryption with equality test (PKEET) allows testing whether two ciphertexts are generated by the same message or not. PKEET is a potential candidate for many practical applications like efficient data management on encrypted databases. Potential applicability of PKEET leads to intensive research from its first instantiation by Yang et al. (CT-RSA 2010). Most of the followup constructions are secure in the random oracle model. Moreover, the security of all the concrete constructions is based on number-theoretic hardness assumptions which are vulnerable in the post-quantum era. Recently, Lee et al. (ePrint 2016) proposed a generic construction of PKEET schemes in the standard model and hence it is possible to yield the first instantiation of PKEET schemes based on lattices. Their method is to use a $2$-level hierarchical identity-based encryption (HIBE) scheme together with a one-time signature scheme. In this paper, we propose, for the first time, a direct construction of a PKEET scheme based on the hardness assumption of lattices in the standard model. More specifically, the security of the proposed scheme is reduces to the hardness of the Learning With Errors problem.

We consider the problem of protecting and manipulating elections by recounting and changing ballots, respectively. Our setting involves a plurality-based election held across multiple districts, and the problem formulations are based on the model proposed recently by~[Elkind et al, IJCAI 2019]. It turns out that both of the manipulation and protection problems are NP-complete even in fairly simple settings. We study these problems from a parameterized perspective with the goal of establishing a more detailed complexity landscape. The parameters we consider include the number of voters, and the budgets of the attacker and the defender. While we observe fixed-parameter tractability when parameterizing by number of voters, our main contribution is a demonstration of parameterized hardness when working with the budgets of the attacker and the defender.

If you’re not already thinking about 2020 web design, the time is now. Already, web design trends for 2020 have started to emerge, and if you want to stay on-trend and engage site visitors, it’s crucial to pay attention. But what is the future of web design in 2020? Will everything change? Well — not […]

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Welcome to the Design Patterns Demystified (DPD) series, in this edition we are going to discuss Template Design Pattern. So let us understand the why, how, what, and where of Template Design Pattern.

The Why

Let us understand first, why we need this pattern with the help of an example. Let's you are building a reusable library which is orchestrating the operation of buying an item on an e-commerce platform. Now, irrespective of what you are buying, you will follow the same sequence of steps like building your cart, adding an address, filling in payment details, and then finishing the payment. The details in these steps will vary based on what you are buying, how much you are buying, the delivery address, and the preferred mode of payment, but the complete orchestration of steps remains the same.

Summary

One feature that Go does not offer that is really useful in visitor patterns is overriding methods. The basic idea is to write a concrete class that contains all the VisitX methods with empty implementations, and a subclass can choose to only override the methods it cares about, ignoring the rest.

We'll see an example of how to implement this pattern in idiomatic Go code.

At these gatherings in northeast Washington, the jackboot of tyranny is always said to be descending, the hand of the federal government always inches away from stealing your guns, your land, your freedom to speak or to pray.…

There's one thing the Innovia Foundation can never say: That it hadn't been told.…

As COVID-19 fears grew, public officials and sports execs contemplated health risks — and debated a PR message — but let 33,000 fans into a Seattle Sounders soccer match, emails show. By Ken Armstrong, ProPublica, and David Gutman and Lewis Kamb, The Seattle Times On March 6, at 2:43 p.m., the health officer for Public Health — Seattle & King County, the hardest-hit region in the first state to be slammed by COVID-19, sent an email to a half-dozen colleagues, saying, “I want to cancel large group gatherings now.”…

This is normally the time of year when we're up to our eyeballs in concert announcements, but in these topsy-turvy times, we're instead having to write about all the concerts being canceled due to COVID-19. It's a real bummer.…

When Washington Gov. Jay Inslee solemnly looked into the camera on Friday, May 1, he wasn't ready to declare victory yet.…

Embodiments of the invention provide systems and methods for analyzing sequential data. The sequential data can comprise a sequence of data points arranged in a particular order and thus representing a sequence. A number of such sequences can be analyzed, for example, to identify patterns or commonalities within the sequences or portions of sequences represented by the data. According to one embodiment, a method of identifying patterns in sequences of data points can comprise reading a set of sequential data. The sequential data can comprises a plurality of sequences and each of the plurality of sequences can represent an ordered sequence of tokens. A dotplot representing matches between each sequence of the plurality sequences can be generated. One or more patterns within the sequential data can then be identified based on the dotplot.

Embodiments of the disclosure include a method for partitioning a deterministic finite automaton (DFA) into a plurality of groups. The method includes selecting, with a processing device, a subset of the plurality of states and mapping each state of the subset onto a group of the plurality of groups by assigning one or more transition rules associated with each state to a rule line of the group, wherein each rule line is assigned at most two transition rules and an extended address associated with one of the at most two transition rules. The method also includes iteratively processing each state of the subset mapped onto the group by removing the extended address from each rule line in the group with transition rules referring to a current state if the transition rules in the rule line branch within the group.

The invention relates to Fatty Acid Fumarate Derivatives; compositions comprising an effective amount of a Fatty Acid Fumarate Derivative; and methods for treating or preventing cancer, a metabolic disorder or neurodegenerative disorder comprising the administration of an effective amount of a Fatty Acid Fumarate Derivative.

A process for producing C10-C26 monobranched fatty acids or alkyl esters thereof which includes isomerizing unsaturated C10-C26 fatty acids or alkyl esters thereof in the presence of a catalyst which comprises both a zeolite and a Lewis base. The zeolite can be reused after simple separation from the reaction products without having to regenerate. The process is particularly suitable for producing highly monobranched fatty acids or alkyl esters thereof.

This invention relates to an improved process for the preparation of green fatty acid methyl esters (FAME; commonly called as biodiesel) from different triglyceride oils using mixed metal oxides derived from layered double hydroxides (referred here as LDHs) as reusable solid heterogeneous base catalysts. This process uses very low alcohohoil molar ratio and catalyst and/or products are easily separable after the reaction through simple physical processes. The properties of thus obtained biodiesel meet the standard biodiesel values and can directly be used as transport fuel.

A method for lowering content of a sterol in a highly unsaturated fatty acid-concentrated oil in a method to concentrate a highly unsaturated fatty acid by using a lipase reaction, may include removing the free form of the sterol from a raw material oil that includes a highly unsaturated fatty acid-containing glyceride prior to performing the lipase reaction; and thereafter concentrating the highly unsaturated fatty acid in the glyceride by using a lipase that is less reactive for the highly unsaturated fatty acid.

The invention relates to a method for cleaving unsaturated fatty chains comprising a step of oxidative cleavage in which at least one fatty acid derivative having at least one unsaturation is reacted in the liquid phase with hydrogen peroxide in the presence of a catalyst for activating the reaction of oxidative cleavage and of molecular oxygen and in the absence of organic solvent.

The present invention is directed at a binder for a battery electrode comprising an ethylene oxide-containing copolymer including a first monomer of ethylene oxide (EO) and at least one additional monomer selected from an alkylene-oxide that is different from the first monomer of EO, an alkyl glycidyl ether, or a combination thereof; wherein the ethylene oxide-containing copolymer has a weight average molecular weight less than about 200,000 g/mole (e.g., from about 10,000 to about 100,000), the molar fraction of the first monomer of EO (XEo) in the ethylene oxide-containing copolymer is greater than 0.80 (e.g., from about 0.80 to about 0.995), and the ethylene oxide-containing copolymer has a peak melting temperature (Tp), in ° C., for a selected XEO in the range of about 0.80 to about 0.995, which is below a maximum value of Tpmax, at the selected XEO, which is calculated using the equation Tpmax=(60−150 (1−XEO)).

The invention is directed to a pulverulent compound of the formula NiaM1bM2cOx(OH)y where M1 is at least one element selected from the group consisting of Fe, Co, Zn, Cu and mixtures thereof, M2 is at least one element selected from the group consisting of Mn, Al, Cr, B, Mg, Ca, Sr, Ba, Si and mixtures thereof, 0.3≦a≦0.83, 0.1≦b≦0.5, 0.01≦c≦0.5, 0.01≦x≦0.99 and 1.01≦y≦1.99, wherein the ratio of tapped density measured in accordance with ASTM B 527 to the D50 of the particle size distribution measured in accordance with ASTM B 822 is at least 0.2 g/cm3·μm. The invention is also directed to a method for the production of the pulverulent compound and the use as a precursor material for producing lithium compounds for use in lithium secondary batteries.

Provided are a negative active material, a method of preparing the same, and a lithium battery including the negative active material. The negative active material includes a carbonaceous core that has a sulfur content of about 10 ppm to 900 ppm; and an amorphous carbon layer continuously formed on a surface of the carbonaceous core, wherein the carbonaceous core has a crystalloid plate structure, and a crystallite size measured from a full width at half maximum of the peak with respect to the surface (002) of about 10 nm to about 45 nm in an X-ray diffraction spectrum of the carbonaceous core. The lithium battery including a negative electrode including the negative active material has improved capacity characteristics and ring lifetime characteristics.

The disclosure relates generally to a method for fabricating a patterned medium. The method includes providing a substrate with an exterior layer under a lithographically patterned surface layer, the lithographically patterned surface layer comprising a first pattern in a first region and a second pattern in a second region, applying a first masking material over the first region, transferring the second pattern into the exterior layer in the second region, forming self-assembled block copolymer structures over the lithographically patterned surface layer, the self-assembled block copolymer structures aligning with the first pattern in the first region, applying a second masking material over the second region, transferring the polymer block pattern into the exterior layer in the first region, and etching the substrate according to the second pattern transferred to the exterior layer in the second region and the polymer block pattern transferred to the exterior layer in the first region.

The invention is directed to a matting and/or frosting additive concentrate for polymers or polymer blends, said additive comprising to 75% by weight of hollow glass microspheres and 20 to 95% by weight of a liquid or waxy carrier material and optionally up to 75% by weight of additives.

An artificial silica marble comprises a matrix and a line pattern portion. The line pattern portion comprises fine lines having a width of about 50 to about 500 μm and forms a web- or net-like pattern. The line pattern portion divides or partitions the artificial silica marble into a plurality of irregularly shaped pattern portions to form an amorphous pattern in the cross section of the artificial silica marble.

Apparatuses and methods of calibrating a memory interface are described. Calibrating a memory interface can include loading and outputting units of a first data pattern into and from at least a portion of a register to generate a first read capture window. Units of a second data pattern can be loaded into and output from at least the portion of the register to generate a second read capture window. One of the first read capture window and the second read capture window can be selected and a data capture point for the memory interface can be calibrated according to the selected read capture window.

Disclosed herein are stabilized powder and aqueous formulations comprising a substantially water insoluble lipophilic bioactive compound and a micelle-forming surfactant. In one embodiment, the formulation further comprises a water soluble reducing agent, and/or a water insoluble reducing agent, and/or a metal chelator, and/or a metal bisulfite reducing agent, or combinations thereof, wherein the formulation remains substantially clear and stable when stored at or below room temperature for a period of at least 6 months or at least 12 months; and methods for preparing these formulations.

Disclosed are processes relating to the production of polyglycerol ethers of fatty alcohols, in particular, one step process using fatty alcohol and glycerine to synthesize polyglycerides of fatty alcohols will provide a 100% renewable surfactant that is cost effective efficient and CMR free. The synthetic methods mentioned in prior art uses hazardous chemicals as glycidyl ethers, epichlorohydrin that are listed as CMR and known carcinogens and hazardous to handle.

Aspects of the invention relate to techniques of generating guiding patterns for via-type feature groups. A guiding pattern may be constructed for a via-type feature group that comprises two or more via-type features in a layout design. A backbone structure may then be determined for the guiding pattern. Based on the backbone structure and a self-assembly model, simulated locations of the two or more via-type features are computed. The simulated locations are compared with targeted locations. If the simulated locations do not match the targeted locations based on a predetermined criterion, the simulated locations adjusted to derive modified locations. Using the modified locations, the above operations may be repeated until the simulated locations match the targeted location based on a predetermined criterion or for a predetermined number of times.

In a method for fracturing or mask data preparation or mask process correction for charged particle beam lithography, a plurality of shots are determined that will form a pattern on a surface, where shots are determined so as to reduce sensitivity of the resulting pattern to changes in beam blur (βf). In some embodiments, the sensitivity to changes in βf is reduced by varying the charged particle surface dosage for a portion of the pattern. Methods for forming patterns on a surface, and for manufacturing an integrated circuit are also disclosed, in which pattern sensitivity to changes in βf is reduced.

A method and system for optical proximity correction (OPC) is disclosed in which a set of shaped beam shots is determined which, when used in a shaped beam charged particle beam writer, will form a pattern on a reticle, where some of the shots overlap, where the pattern on the reticle is an OPC-corrected version of an input pattern, and where the sensitivity of the pattern on the reticle to manufacturing variation is reduced. A method for fracturing or mask data preparation is also disclosed.

Methods, systems, and apparatus, including computer program products, for extracting information from unstructured text. Fact pairs are used to extract basic patterns from a body of text. Patterns are generalized by replacing words with classes of similar words. Generalized patterns are used to extract further fact pairs from the body of text. The process can begin with fact pairs, basic patterns, or generalized patterns.

A method of signal processing according to one embodiment includes calculating an envelope of a first signal that is based on a low-frequency portion of a speech signal, calculating an envelope of a second signal that is based on a high-frequency portion of the speech signal, and calculating a plurality of gain factor values according to a time-varying relation between the envelopes of the first and second signal. The method includes attenuating, based on a variation over time of a relation between the envelopes of the first and second signals, at least one of the plurality of gain factor values. In one example, the variation over time of a relation between the envelopes is indicated by at least one distance among the plurality of gain factor values.

A first circuit having a first coil electrically charges a second circuit having a second coil through electromagnetic coupling of the two coils. When data signals are to be transferred between the first and second circuits, signal transfer is started only after the second circuit has been charged for a predetermined period of time. The position relationship between the coils is also detected, and a charging/transfer selector changes a duty ratio between charge transfer and data transfer in accordance with the detected result. The charge is transferred in an intermittent manner, and the charging rate is adjusted according to the difference between the voltage of a secondary battery observed during a charging phase and the voltage of the secondary battery observed a certain time after interruption of the charging phase, or vice versa.

According to one embodiment, an actinic-ray- or radiation-sensitive resin composition includes any of the compounds (A) of general formula (I) below that when exposed to actinic rays or radiation, generates an acid and a resin (B) whose rate of dissolution into an alkali developer is increased by the action of an acid. (The characters used in general formula (I) have the meanings mentioned in the description.)

A system, methods and server for monitoring health and safety of individuals in a population and sending alert notifications when exceptions are detected include comparing biometric data obtained from the individuals to a biometric model generated for the individual through computer-learning methods. Biometric data may be gathered by wireless biometric sensor devices which transmit biometric data to receiver devices, which relay the biometric data to a server. The biometric model may be maintained in the server and include nominal and threshold biometric parameters for each individual based on biometric sensor data gathered or analyzed over a period of time. An alert may be issued by the server when an individual's biometric data is outside a threshold in the biometric model. The transmitted alert may depend upon the nature of the exception, user settings and past notification experience. Alerts may be escalated when not answered within defined durations.