Scientists have had little information about how prescribed fire and cattle grazing—common practices in many Western ponderosa pine forests—affect plant abundance and reproduction in the forest understory. Pacific Northwest Research Station scientists began to explore how these practices affect vegetation in a five-year study of postfire vegetation in eastern Oregon ponderosa pine forests where cattle have been routinely pastured from late June or early July through early to mid August. For this area of eastern Oregon, they found that excluding cattle grazing during peak growing season increased native plant cover and grass flowering capability in ungrazed areas compared to grazed areas. Because vegetation was measured prior to releasing cattle on the land, the study's results tend to reflect lasting grazing impacts rather than simple consumption.

Sunderland are looking for a new club secretary with Brett Baker moving on this summer

Canadian ratings firm NUMERIS has released data showing radio retaining 88% of its weekly reach during the pandemic in CANADA. The number, at 93% for adults 18+ in metered markets last FALL, … more

BLACKENED/Q PRIME rockers METALLICA and drummer LARS ULRICH have reached out to fans with  a message of encouragement on the band’s new SIRIUSXM  MANDATORY METALLICA … more

Puntos destacados de la investigación: En un estudio de más de 37 000 estadounidenses, aquellos que comieron la mayor cantidad de proteína vegetal tenían un 29% menos de probabilidades de morir de una enfermedad coronaria. Sustituir una porción por...

Homelessness is a global public health concern, and traumatic brain injury (TBI) could represent an underappreciated factor in the health trajectories of homeless and marginally housed individuals. We aimed to evaluate the lifetime prevalence of TBI in this population, and to summarise findings on TBI incidence and the association between TBI and health-related or functioning-related outcomes.

Lots of users have asked us about more customization and control over the sound of our audio algorithms in the past, so today, we have introduced some advanced algorithm parameters for our singletrack version in a private beta program!

• Leveler Presets are basically complete new leveling algorithms
• Maximum Loudness Range (LRA) Target to control the strength of our Leveler
• Maximum True Peak Level
• Better Hum and Noise Reduction Controls

UPDATE Nov. 2018:
We released a complete rework of the Adaptive Leveler parameters and the description here is not valid anymore!
Please see Auphonic Adaptive Leveler Customization (Beta Update)!

Please join our private beta program and let us know how you use these new features or if you need even more control!

Leveler Presets

Our Adaptive Leveler corrects level differences between speakers, between music and speech and will also apply dynamic range compression to achieve a balanced overall loudness. If you don't know about the Leveler yet, take a look at our Audio Examples.

Leveler presets are basically complete new leveling algorithms, which we have been working on in the past few months:
Our current Leveler tries to normalize all speakers to the same loudness. However, in some cases, you might want more or less loudness differences (dynamic range / loudness range) between the speakers and music segments, or more or less compression, etc.
For these use cases, we have developed additional Leveler Presets and the parameter Maximum Loudness Range.

Preset Medium:

This is our current leveling algorithm as demonstrated in the Audio Examples.

Preset Hard:

The hard preset reacts faster and applies more gain and compression compared to the medium preset. It is built for recordings with extreme loudness differences, for example very quiet questions from the audience in a lecture recording, extremely soft and loud voices within one audio track, etc.

Preset Soft:

This preset reacts slower, applies less gain and compression compared to the medium preset. Use it if you want to keep more loudness differences (dynamic narration), if you want your voices to sound "less compressed/processed", for dynamic music (concert/classical recordings), background music, etc.

Preset Softer:

Like soft, but softer :)

Preset Speech Medium, Music Soft:

Uses the medium preset in speech segments and the soft preset in music segments. It is built for music live recordings or dynamic music mixes, where you want to amplify all speakers but keep the loudness differences within and between music segments.

Preset Medium, No Compressor:

Like the medium preset, but only (mid-term) leveling and no (short-term) compression is applied. This preset is optimal if you just use a Maximum Loudness Range Target and want to avoid any additional compression as much as possible.

Maximum Loudness Range (LRA) Target

The loudness range (LRA) indicates the variation of loudness over the course of a program and is measured in LU (loudness units) - for more details see Loudness Measurement and Normalization or EBU Tech 3342.

The parameter Max Loudness Range controls how much leveling is applied:
volume changes of our Adaptive Leveler will be restricted so that the loudness range of the output file is below the selected value.
High loudness range values will result in very dynamic output files, low loudness range values in compressed output audio. If the LRA value of your input file is already below the maximum loudness range value, no leveling at all will be applied.

It is also important which Leveler Preset you select, for example, if you use the soft(er) preset, it won't be possible to achieve very low loudness range targets.

Also, the Max Loudness Range parameter is not such a precise target value as the Loudness Target. The LRA of your output file might be off a few LU, as it is not reasonable to reach the exact target value.

Use Cases: The Maximum LRA parameter allows you to control the strength of our leveling algorithms, in combination with the parameter Leveler Preset. This might be used for automatic mixdowns with different LRA values for different target platforms (very compressed ones like mobile devices or Alexa, very dynamic ones like home cinema, etc.).

Maximum True Peak Level

This parameter sets the maximum allowed true peak level of the processed output file, which is controlled by the True Peak Limiter after our Global Loudness Normalization algorithms.

If set to Auto (which is the current default), a reasonable value according to the selected loudness target is used: -1dBTP for 23 LUFS (EBU R128) and higher, -2dBTP for -24 LUFS (ATSC A/85) and lower loudness targets.

The maximum true peak level parameter is already available in our desktop program.

Better Hum and Noise Reduction Controls

Hum Base Frequency:

Set the hum base frequency to 50Hz or 60Hz (if you know it), or use Auto to automatically detect the hum base frequency in each speech region.

Hum Reduction Amount:

Maximum hum reduction amount in dB, higher values remove more noise.
In Auto mode, a classifier decides how much hum reduction is necessary in each speech region. Set it to a custom value (> 0), if you prefer more hum reduction or want to bypass our classifier. Use Disable Dehum to disable hum reduction and use our noise reduction algorithms only.

Behavior of noise and hum reduction parameter combinations:

Advanced Parameters Private Beta and Feedback

At the moment the advanced algorithm parameters are for beta users only. This is to allow us to get user feedback, so we can change the parameters to suit user needs.
Please let us know your case studies, if you need any other algorithm parameters or if you have any questions!

Here are some private beta invitation codes:

y6KCBI4yo0 ksIFEsmI1y BDZec2a21V i4XRGLlVm2 0UDxuS0vbu aaBxi35sKN aaiDSZUbmY bu8lPF80Ih eMsSl6Sf8K DaWpsUnyjo
2YM00m8zDW wh7K2pPmSa jCX7mMy2OJ ZGvvhzCpTF HI0lmGhjVO eXqVhN6QLU t4BH0tYcxY LMjQREVuOx emIogTCAth 0OTPNB7Coz
VIFY8STj2f eKzRSWzOyv 40cMMKKCMN oBruOxBkqS YGgPem6Ne7 BaaFG9I1xZ iSC0aNXoLn ZaS4TykKIa l32bTSBbAx xXWraxS40J
zGtwRJeAKy mVsx489P5k 6SZM5HjkxS QmzdFYOIpf 500AHHtEFA 7Kvk6JRU66 z7ATzwado6 4QEtpzeKzC c9qt9Z1YXx pGSrDzbEED
MP3JUTdnlf PDm2MOLJIG 3uDietVFSL 1i7jZX0Y9e zPkSgmAqqP 5OhcmHIZUP E0vNsPxZ4s FzTIyZIG2r 5EywA0M7r5 FMhpcFkVN5
oRLbRGcRmI 2LTh8GlN7h Cjw6Z3cveP fayCewjE55 GbkyX89Lxu 4LpGZGZGgc iQV7CXYwkH pGLyQPgaha e3lhKDRUMs Skrei1tKIa

If you have an invitation code, you can enter it here to activate the advanced audio algorithm parameters:
Auphonic Algorithm Parameters Private Beta Activation

In late August, we launched the private beta program of our advanced audio algorithm parameters. After feedback by our users and many new experiments, we are proud to release a complete rework of the Adaptive Leveler parameters:

In the previous version, we based our Adaptive Leveler parameters on the Loudness Range descriptor (LRA), which is included in the EBU R128 specification.
Although it worked, it turned out that it is very difficult to set a loudness range target for diverse audio content, which does include speech, background sounds, music parts, etc. The results were not predictable and it was hard to find good target values.
Therefore we developed our own algorithm to measure the dynamic range of audio signals, which works similarly for speech, music and other audio content.

The following advanced parameters for our Adaptive Leveler allow you to customize which parts of the audio should be leveled (foreground, all, speech, music, etc.), how much they should be leveled (dynamic range), and how much micro-dynamics compression should be applied.

To try out the new algorithms, please join our private beta program and let us know your feedback!

Leveler Preset

The Leveler Preset defines which parts of the audio should be adjusted by our Adaptive Leveler:

• Default Leveler:
  Our classic, default leveling algorithm as demonstrated in the Leveler Audio Examples. Use it if you are unsure.
• Foreground Only Leveler:
  This preset reacts slower and levels foreground parts only. Use it if you have background speech or background music, which should not be amplified.
• Fast Leveler:
  A preset which reacts much faster. It is built for recordings with fast and extreme loudness differences, for example, to amplify very quiet questions from the audience in a lecture recording, to balance fast-changing soft and loud voices within one audio track, etc.
• Amplify Everything:
  Amplify as much as possible. Similar to the Fast Leveler, but also amplifies non-speech background sounds like noise.

Leveler Dynamic Range

Our default Leveler tries to normalize all speakers to a similar loudness so that a consumer in a car or subway doesn't feel the need to reach for the volume control.
However, in other environments (living room, cinema, etc.) or in dynamic recordings, you might want more level differences (Dynamic Range, Loudness Range / LRA) between speakers and within music segments.

The parameter Dynamic Range controls how much leveling is applied: Higher values result in more dynamic output audio files (less leveling). If you want to increase the dynamic range by 3dB (or LU), just increase the Dynamic Range parameter by 3dB.
We also like to call this Loudness Comfort Zone: above a maximum and below a minimum possible level (the comfort zone), no leveling is applied. So if your input file already has a small dynamic range (is within the comfort zone), our leveler will be just bypassed.

Example Use Cases:
Higher dynamic range values should be used if you want to keep more loudness differences in dynamic narration or dynamic music recordings (live concert/classical).
It is also possible to utilize this parameter to generate automatic mixdowns with different loudness range (LRA) values for different target environments (very compressed ones like mobile devices or Alexa, very dynamic ones like home cinema, etc.).

Compressor

Controls Micro-Dynamics Compression:
The compressor reduces the volume of short and loud spikes like "p", "t" or laughter ( short-term dynamics) and also shapes the sound of your voice (it will sound more or less "processed").
The Leveler, on the other hand, adjusts mid-term level differences, as done by a sound engineer, using the faders of an audio mixer, so that a listener doesn't have to adjust the playback volume all the time.
For more details please see Loudness Normalization and Compression of Podcasts and Speech Audio.

• Auto:
  The compressor setting depends on the selected Leveler Preset. Medium compression is used in Foreground Only and Default Leveler presets, Hard compression in our Fast Leveler and Amplify Everything presets.
• Soft:
  Uses less compression.
• Medium:
  Our default setting.
• Hard:
  More compression, especially tries to compress short and extreme level overshoots. Use this preset if you want your voice to sound very processed, our if you have extreme and fast-changing level differences.
• Off:
  No short-term dynamics compression is used at all, only mid-term leveling. Switch off the compressor if you just want to adjust the loudness range without any additional micro-dynamics compression.

Separate Music/Speech Parameters

Use the switch Separate MusicSpeech Parameters (top right), to see separate Adaptive Leveler parameters for music and speech segments, to control all leveling details separately for speech and music parts:

For dialog intelligibility improvements in films and TV, it is important that the speech/dialog level and loudness range is not too soft compared to the overall programme level and loudness range. This parameter allows you to use more leveling in speech parts while keeping music and FX elements less processed.
Note: Speech, music and overall loudness and loudness range of your production are also displayed in our Audio Processing Statistics!

Example Use Case:
Music live recordings or dynamic music mixes, where you want to amplify all speakers (speech dynamic range should be small) but keep the dynamic range within and between music segments (music dynamic range should be high).
Dialog intelligibility improvements for films and TV, without effecting music and FX elements.

Other Advanced Audio Algorithm Parameters

We also offer advanced audio parameters for our Noise, Hum Reduction and Global Loudness Normalization algorithms:

For more details, please see the Advanced Audio Algorithms Documentation.

Want to know more?

If you want to know more details about our advanced algorithm parameters (especially the leveler parameters), please listen to the following podcast interview with Chris Curran (Podcast Engineering School):
Auphonic’s New Advanced Features, with Georg Holzmann – PES 108

Advanced Parameters Private Beta and Feedback

At the moment the advanced algorithm parameters are for beta users only. This is to allow us to get user feedback, so we can change the parameters to suit user needs.
Please let us know your case studies, if you need any other algorithm parameters or if you have any questions!

Here are some private beta invitation codes:

jbwCVpLYrl 6zmLqq8o3z RXYIUbC6al QDmIZLuPKa JIrnGRZBgl SWQOWeZOBD ISeBCA9gTy w5FdsyhZVI qWAvANQ5mC twOjdHrit3
KwnL2Le6jB 63SE2V54KK G32AULFyaM 3H0CLYAwLU mp1GFNVZHr swzvEBRCVa rLcNJHUNZT CGGbL0O4q1 5o5dUjruJ9 hAggWBpGvj
ykJ57cFQSe 0OHAD2u1Dx RG4wSYTLbf UcsSYI78Md Xedr3NPCgK mI8gd7eDvO 0Au4gpUDJB mYLkvKYz1C ukrKoW5hoy S34sraR0BU
J2tlV0yNwX QwNdnStYD3 Zho9oZR2e9 jHdjgUq420 51zLbV09p4 c0cth0abCf 3iVBKHVKXU BK4kTbDQzt uTBEkMnSPv tg6cJtsMrZ
BdB8gFyhRg wBsLHg90GG EYwxVUZJGp HLQ72b65uH NNd415ktFS JIm2eTkxMX EV2C5RAUXI a3iwbxWjKj X1AT7DCD7V y0AFIrWo5l

If you have an invitation code, you can enter it here to activate the advanced audio algorithm parameters:
Auphonic Algorithm Parameters Private Beta Activation

Last weekend, at the Subscribe10 conference, we released Advanced Audio Algorithm Parameters for Multitrack Productions:

We launched our advanced audio algorithm parameters for Singletrack Productions last year. Now these settings (and more) are available for Multitrack Algorithms as well, which gives you detailed control for each track of your production.

• Fore/Background Settings: keep your music/clip tracks unchanged and set a custom background gain
• Multitrack Leveler Parameters: control the stereo panorama, leveling algorithm, dynamic range and compression
• Better Hum and Noise Reduction Controls for each track
• Maximum True Peak Level setting for the final mixdown
• Full API Support

Please join our private beta program and let us know how you use these new features or if you need even more control!

Fore/Background Settings

The parameter Fore/Background controls whether a track should be in foreground, in background, ducked, or unchanged, which is especially important for music or clip tracks.
For more details, please see Automatic Ducking, Foreground and Background Tracks .

Unchanged (Foreground):

We sometimes received complaints from users, which produced very complex music or clip tracks, that Auphonic changes the levels too hard.
If you set the parameter Fore/Background to the new option Unchanged (Foreground), Level relations within this track won’t be changed at all. It will be added to the final mixdown so that foreground/solo parts of this track will be as loud as (foreground) speech from other tracks.

Background Level:

It is now possible to set the level of background segments/tracks (compared to foreground segments) in background and ducking tracks. By default, background and ducking segments are 18dB softer than foreground segments.

Leveler Parameters

Similar to our Singletrack Advanced Leveler Parameters (see this previous blog post), we also released leveling parameters for Multitrack Productions now.
The following advanced parameters for our Multitrack Adaptive Leveler can be set for each track and allow you to customize which parts of the audio should be leveled, how much they should be leveled, how much dynamic range compression should be applied and to set the stereo panorama (balance):

Leveler Preset:

Select the Speech or Music Leveler for this track.
If set to Automatic (default), a classifier will decide if this is a music or speech track.

Dynamic Range:

The parameter Dynamic Range controls how much leveling is applied: Higher values result in more dynamic output audio files (less leveling). If you want to increase the dynamic range by 3dB (or LU), just increase the Dynamic Range parameter by 3dB.
For more details, please see Multitrack Leveler Parameters.

Compressor:

Select a preset for Micro-Dynamics Compression: Auto, Soft, Medium, Hard or Off.
The Compressor adjusts short-term dynamics, whereas the Leveler adjusts mid-term level differences.
For more details, please see Multitrack Leveler Parameters.

Stereo Panorama (Balance):

Change the stereo panorama (balance for stereo input files) of the current track.
Possible values: L100, L75, L50, L25, Center, R25, R50, R75 and R100.

If you understand German and want to know more about our Advanced Leveler Parameters and audio dynamics in general, watch our talk at the Subscribe10 conference:
Video: Audio Lautheit und Dynamik.

Better Hum and Noise Reduction Controls

Noise Reduction Amount:

Maximum noise and hum reduction amount in dB, higher values remove more noise.
In Auto mode, a classifier decides if and how much noise reduction is necessary (to avoid artifacts). Set to a custom (non-Auto) value if you prefer more noise reduction or want to bypass our classifier.

Hum Base Frequency:

Set the hum base frequency to 50Hz or 60Hz (if you know it), or use Auto to automatically detect the hum base frequency in each speech region.

Hum Reduction Amount:

Maximum hum reduction amount in dB, higher values remove more noise.
In Auto mode, a classifier decides how much hum reduction is necessary in each speech region. Set it to a custom value (> 0), if you prefer more hum reduction or want to bypass our classifier. Use Disable Dehum to disable hum reduction and use our noise reduction algorithms only.

Behavior of noise and hum reduction parameter combinations:

Maximum True Peak Level

In the Master Algorithm Settings of your multitrack production, you can set the maximum allowed true peak level of the processed output file, which is controlled by the True Peak Limiter after our Loudness Normalization algorithms.

If set to Auto (which is the current default), a reasonable value according to the selected loudness target is used: -1dBTP for 23 LUFS (EBU R128) and higher, -2dBTP for -24 LUFS (ATSC A/85) and lower loudness targets.

Full API Support

All advanced algorithm parameters, for Singletrack and Multitrack Productions, are available in our API as well, which allows you to integrate them into your scripts, external workflows and third-party applications.

Join the Beta and Send Feedback

Please join our beta and let us know your case studies, if you need any other algorithm parameters or if you have any questions!

Here are some private beta invitation codes:

8tZPc3T9pH VAvO8VsDg9 0TwKXBW4Ni kjXJMivtZ1 J9APmAAYjT Zwm6HabuFw HNK5gF8FR5 Do1MPHUyPW CTk45VbV4t xYOzDkEnWP
9XE4dZ0FxD 0Sl3PxDRho uSoRQxmKPx TCI62OjEYu 6EQaPYs7v4 reIJVOwIr8 7hPJqZmWfw kti3m5KbNE GoM2nF0AcN xHCbDC37O5
6PabLBRm9P j2SoI8peiY olQ2vsmnfV fqfxX4mWLO OozsiA8DWo weJw0PXDky VTnOfOiL6l B6HRr6gil0 so0AvM1Ryy NpPYsInFqm
oFeQPLwG0k HmCOkyaX9R G7DR5Sc9Kv MeQLSUCkge xCSvPTrTgl jyQKG3BWWA HCzWRxSrgW xP15hYKEDl 241gK62TrO Q56DHjT3r4
9TqWVZHZLE aWFMSWcuX8 x6FR5OTL43 Xf6tRpyP4S tDGbOUngU0 5BkOF2I264 cccHS0KveO dT29cF75gG 2ySWlYp1kp iJWPhpAimF

If you have an invitation code, you can enter it here to activate the Multitrack Advanced Audio Algorithm Parameters:
Auphonic Algorithm Parameters Private Beta Activation

Every day thousands of business cards exchange hands, and these business cards often get lost in mounds of other cards. Often, clients are unable to reach you just because they couldn't find your...

We're back and this week, we're talking to Avatar's colorful frontman Johannes Eckerström. If you've ever seen the band live,...

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By Jessica Corbett Common Dreams “We face a stark choice [between] radical, disruptive changes to our physical world or radical, disruptive changes to our political and economic systems to avoid those outcomes.” As people across the globe mobilize to demand … Continue reading →

By Jessica Corbett Common Dreams “We face a stark choice [between] radical, disruptive changes to our physical world or radical, disruptive changes to our political and economic systems to avoid those outcomes.” As people across the globe mobilize to demand … Continue reading →

By Jessica Corbett Common Dreams “We face a stark choice [between] radical, disruptive changes to our physical world or radical, disruptive changes to our political and economic systems to avoid those outcomes.” As people across the globe mobilize to demand … Continue reading →

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 …

Little Details That Matter on a Mobile Website Read More »

The paper proves the Riemann Hypothesis.

A natural problem in the context of the coupon collector's problem is the behavior of the maximum of independent geometrically distributed random variables (with distinct parameters). This question has been addressed by Brennan et al. (British J. of Math. & CS. 8 (2015), 330-336). Here we provide explicit asymptotic expressions for the moments of that maximum, as well as of the maximum of exponential random variables with corresponding parameters. We also deal with the probability of each of the variables being the maximal one.

The calculations lead to expressions involving Hurwitz's zeta function at certain special points. We find here explicitly the values of the function at these points. Also, the distribution function of the maximum we deal with is closely related to the generating function of the partition function. Thus, our results (and proofs) rely on classical results pertaining to the partition function.

We construct a 2-equivalence $mathfrak{CohTheory}^ ext{op} simeq mathfrak{TypeSpaceFunc}$. Here $mathfrak{CohTheory}$ is the 2-category of positive theories and $mathfrak{TypeSpaceFunc}$ is the 2-category of type space functors. We give a precise definition of interpretations for positive logic, which will be the 1-cells in $mathfrak{CohTheory}$. The 2-cells are definable homomorphisms. The 2-equivalence restricts to a duality of categories, making precise the philosophy that a theory is `the same' as the collection of its type spaces (i.e. its type space functor).

In characterising those functors that arise as type space functors, we find that they are specific instances of (coherent) hyperdoctrines. This connects two different schools of thought on the logical structure of a theory.

The key ingredient, the Deligne completeness theorem, arises from topos theory, where positive theories have been studied under the name of coherent theories.

We propose a new method for realistic human motion transfer using a generative adversarial network (GAN), which generates a motion video of a target character imitating actions of a source character, while maintaining high authenticity of the generated results. We tackle the problem by decoupling and recombining the posture information and appearance information of both the source and target characters. The innovation of our approach lies in the use of the projection of a reconstructed 3D human model as the condition of GAN to better maintain the structural integrity of transfer results in different poses. We further introduce a detail enhancement net to enhance the details of transfer results by exploiting the details in real source frames. Extensive experiments show that our approach yields better results both qualitatively and quantitatively than the state-of-the-art methods.

An important goal in reinforcement learning is to create agents that can quickly adapt to new goals while avoiding situations that might cause damage to themselves or their environments. One way agents learn is through exploration mechanisms, which are needed to discover new policies. However, in deep reinforcement learning, exploration is normally done by injecting noise in the action space. While performing well in many domains, this setup has the inherent risk that the noisy actions performed by the agent lead to unsafe states in the environment. Here we introduce a novel approach called Meta-Learned Instinctual Networks (MLIN) that allows agents to safely learn during their lifetime while avoiding potentially hazardous states. At the core of the approach is a plastic network trained through reinforcement learning and an evolved "instinctual" network, which does not change during the agent's lifetime but can modulate the noisy output of the plastic network. We test our idea on a simple 2D navigation task with no-go zones, in which the agent has to learn to approach new targets during deployment. MLIN outperforms standard meta-trained networks and allows agents to learn to navigate to new targets without colliding with any of the no-go zones. These results suggest that meta-learning augmented with an instinctual network is a promising new approach for safe AI, which may enable progress in this area on a variety of different domains.

Neural networks have opened up many new opportunities to utilize remotely sensed images in meteorology. Common applications include image classification, e.g., to determine whether an image contains a tropical cyclone, and image translation, e.g., to emulate radar imagery for satellites that only have passive channels. However, there are yet many open questions regarding the use of neural networks in meteorology, such as best practices for evaluation, tuning and interpretation. This article highlights several strategies and practical considerations for neural network development that have not yet received much attention in the meteorological community, such as the concept of effective receptive fields, underutilized meteorological performance measures, and methods for NN interpretation, such as synthetic experiments and layer-wise relevance propagation. We also consider the process of neural network interpretation as a whole, recognizing it as an iterative scientist-driven discovery process, and breaking it down into individual steps that researchers can take. Finally, while most work on neural network interpretation in meteorology has so far focused on networks for image classification tasks, we expand the focus to also include networks for image translation.

By Jon Queally Common Dreams Greenpeace says world leaders must not let the fossil fuel industry stand in the way of the necessary—and attainable—transition to a clean and safe energy future With scientists and experts from around the world telling … Continue reading →

[IMAGE-1] Noah Baumbach's Marriage Story begins as its central marriage is coming to an end. Our two protagonists are fiercely independent, articulate, opinionated creative types: Charlie (Adam Driver) is the director of an avant-garde theater troupe in New York City; Nicole (Scarlett Johansson) is an actress and one of his primary collaborators.…

Processing metadata includes storing, in a data storage system, a specification for each of multiple sources, each specification including information identifying one or more data elements of the corresponding source; and processing, in a data processing system coupled to the data storage system, data elements from the sources, including generating a set of rules for each source based on a corresponding one of the stored specifications, and matching data elements of different sources and determining a quality metric characterizing a given match between a first data element of a first source and a second data element of a second source according to the set of rules generated for the first source and the set of rules generated for the second source.

Described herein are compounds and compositions that modulate the activity of beta-lactamases. In some embodiments, the compounds described herein inhibit beta-lactamase. In certain embodiments, the compounds described herein are useful in the treatment of bacterial infections.

The present invention relates to novel heterocycle-substituted diphosphonic acids, and the pharmaceutically-acceptable salts and esters thereof, in which the diphosphonate-substituted carbon atom moiety is attached to a carbon atom in a nitrogen-containing six membered ring heterocycle, preferably a piperidine ring. The heterocycle-substituted diphosphonic acid compounds have the general structure: ##STR1## wherein Z is a nitrogen-containing six membered ring heterocycle moiety selected from piperidinyl, diazinyl and triazinyl; m, n and m+n are from 0 to 10; Q is a covalent bond or a moiety selected from oxygen, sulfur or nitrogen; and R1, R2, R3 and R4 are substituent groups.The present invention further relates to pharmaceutical compositions containing these novel compounds. Finally this invention relates to methods for treating or preventing diseases characterized by abnormal calcium and phosphate metabolism by utilizing a compound or pharmaceutical composition of the present invention.

This invention relates to a process for polymerizing ethylene comprising contacting ethylene and optional comonomers with a catalyst system comprising an activator and a transition metal compound represented by the formula: ##STR1## Wherein R1 and R2 are independently hydrogen or a group having up to 100 carbon atoms, Cp1 is a bulky ligand; Cp2 is a bulky ligand or a heteroatom optionally bound to a C1 to C50 hydrocarbyl group, n is the valence state of the transition metal, Tm is a Group 3 to 10 metal, and each X is independently an anionic leaving group.

A process for producing an alkali metal cyclopentadienylide is disclosed which comprises reacting in a solvent an alkali metal hydride with a disubstituted or trisubstituted 1,3-cyclopentadiene. Further, a process for producing a dihalobis(η-substituted-cyclopentadienyl)zirconium is disclosed which comprises reacting a zirconium halide with the above alkali metal cyclopentadienylide. The former process enables performing the reaction between the disubstituted or trisubstituted 1,3-cyclopentadiene and the alkali metal hydride at an easily controllable temperature of room temperature to about 150° C. and also enables obtaining the alkali metal cyclopentadienylide in high yield. The latter process enables obtaining the dihalobis(η-substituted-cyclopentadienyl)zirconium in high yield.