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Max Weber and Eugen Ehrlich: On the Janus-headed Construction of Weber's Ideal Type in the Sociology of Law

H. Treiber
2008 Max Weber Studies  
Rottleuthner (1987: 25) has summed up the divergent positions of Max Weber and Eugen Ehrlich in the following overstatement: ' The pandektistische Begriffsjurisprudenz and the consistency of abstract  ...  Kelsen follows Max Weber's line to the extent that he reproaches Ehrlich not only for his confusing and contradictory terminological definitions, but also and above all for a hopeless 'confusion of the  ... 
doi:10.15543/mws/2008/2/6 fatcat:gej6u4aw4jahfhuxf3cnxb32km

Quantization Guided JPEG Artifact Correction [article]

Max Ehrlich, Larry Davis, Ser-Nam Lim, Abhinav Shrivastava
2020 arXiv   pre-print
The JPEG image compression algorithm is the most popular method of image compression because of its ability for large compression ratios. However, to achieve such high compression, information is lost. For aggressive quantization settings, this leads to a noticeable reduction in image quality. Artifact correction has been studied in the context of deep neural networks for some time, but the current state-of-the-art methods require a different model to be trained for each quality setting,
more » ... limiting their practical application. We solve this problem by creating a novel architecture which is parameterized by the JPEG files quantization matrix. This allows our single model to achieve state-of-the-art performance over models trained for specific quality settings.
arXiv:2004.09320v2 fatcat:p7rpqe3p4bfhpdfzsjyjgnki3a

A Frequency Perspective of Adversarial Robustness [article]

Shishira R Maiya, Max Ehrlich, Vatsal Agarwal, Ser-Nam Lim, Tom Goldstein, Abhinav Shrivastava
2021 arXiv   pre-print
logit y max ).  ...  . , N − 1} and λ k =    1 N for k = 0 2 N else. (3) We denote an adversarial attack that is bound by budget by max ||δ||p≤ L(h(x + δ; θ), y) (4) where L is the loss associated with the network and δ  ... 
arXiv:2111.00861v1 fatcat:o67u7i2wkfemdlxbdljebbkum4

Action-Affect Classification and Morphing using Multi-Task Representation Learning [article]

Timothy J. Shields, Mohamed R. Amer, Max Ehrlich, Amir Tamrakar
2016 arXiv   pre-print
Most recent work focused on affect from facial expressions, and not as much on body. This work focuses on body affect analysis. Affect does not occur in isolation. Humans usually couple affect with an action in natural interactions; for example, a person could be talking and smiling. Recognizing body affect in sequences requires efficient algorithms to capture both the micro movements that differentiate between happy and sad and the macro variations between different actions. We depart from
more » ... itional approaches for time-series data analytics by proposing a multi-task learning model that learns a shared representation that is well-suited for action-affect classification as well as generation. For this paper we choose Conditional Restricted Boltzmann Machines to be our building block. We propose a new model that enhances the CRBM model with a factored multi-task component to become Multi-Task Conditional Restricted Boltzmann Machines (MTCRBMs). We evaluate our approach on two publicly available datasets, the Body Affect dataset and the Tower Game dataset, and show superior classification performance improvement over the state-of-the-art, as well as the generative abilities of our model.
arXiv:1603.06554v1 fatcat:qkupuqbbina5vib4w5bl45z6du

Bedeutung der lokalen O2-Spannung im Ehrlich-Ascites-Tumor für die Wirkung von 2,5-Bis-n-propoxy-3,6-bisäthylen-imino-benzochinon(1,4) (Bayer E 39)

Max Frimmer
1962 Die Naturwissenschaften  
Pharmakologisches Institut der fustus Liebig-UniversitdL Giefien MAx Die Konfinuii~t der Plastlden In dieser Zeitschrift wurden vor kurzem yon ~V~IHLETHALER ulld BELL 1) iiberraschellde Befunde tiber  ...  Wir haben dies geprfift, illdem wit MXuse mit Ehrlich-Ascites-Tumor lokal mit E 39 behandelten, eillen Teil der Tiere zusgtzlich nach der intraperitonealen Injektion yon E39 einem Oe-Druck yon 4 atfi aussetzten  ... 
doi:10.1007/bf00633966 fatcat:ttxd65kqs5fl7gu47t6cdrnsdu

Leveraging Bitstream Metadata for Fast and Accurate Video Compression Correction [article]

Max Ehrlich, Jon Barker, Namitha Padmanabhan, Larry Davis, Andrew Tao, Bryan Catanzaro, Abhinav Shrivastava
2022 arXiv   pre-print
This was solved by Ehrlich et al. [26] , [27] using a formulation which was conditioned on the JPEG quantization matrix and later improved by Jiang et al.  ... 
arXiv:2202.00011v1 fatcat:uzhn3rcdjrcfhia5qh73frue54

Interpretable Automated Diagnosis of Retinal Disease Using Deep OCT Analysis [article]

Evan Wen, Max Ehrlich
2022 arXiv   pre-print
30 million Optical Coherence Tomography (OCT) imaging tests are issued every year to diagnose various retinal diseases, but accurate diagnosis of OCT scans requires trained ophthalmologists who are still prone to making errors. With better systems for diagnosis, many cases of vision loss caused by retinal disease could be entirely avoided. In this work, we develop a novel deep learning architecture for explainable, accurate classification of retinal disease which achieves state-of-the-art
more » ... cy. Furthermore, we place an emphasis on producing both qualitative and quantitative explanations of the model's decisions. Our algorithm produces heatmaps indicating the exact regions in the OCT scan the model focused on when making its decision. In combination with an OCT segmentation model, this allows us to produce quantitative breakdowns of the specific retinal layers the model focused on for later review by an expert. Our work is the first to produce detailed quantitative explanations of the model's decisions in this way. Our combination of accuracy and interpretability can be clinically applied for better patient care.
arXiv:2109.02436v2 fatcat:cl5ymra57vbpdb5efseb53thrm

Analyzing and Mitigating JPEG Compression Defects in Deep Learning [article]

Max Ehrlich, Larry Davis, Ser-Nam Lim, Abhinav Shrivastava
2021 arXiv   pre-print
Ehrlich and Davis [14] formulate a fully JPEG domain residual network and again show a speed improvement.  ...  Ehrlich et al. [17] formulate a JPEG artifact correction network on DCT coefficients and attain state-of-the-art results for color images.  ... 
arXiv:2011.08932v2 fatcat:zedts7ok7rcejfgmpxwrk5c4wa

Deep Residual Learning in the JPEG Transform Domain [article]

Max Ehrlich, Larry Davis
2019 arXiv   pre-print
We introduce a general method of performing Residual Network inference and learning in the JPEG transform domain that allows the network to consume compressed images as input. Our formulation leverages the linearity of the JPEG transform to redefine convolution and batch normalization with a tune-able numerical approximation for ReLu. The result is mathematically equivalent to the spatial domain network up to the ReLu approximation accuracy. A formulation for image classification and a model
more » ... version algorithm for spatial domain networks are given as examples of the method. We show that the sparsity of the JPEG format allows for faster processing of images with little to no penalty in the network accuracy.
arXiv:1812.11690v3 fatcat:mqqwezztrrgmzczqnoq4pmic6y

Endocytosis by Random Initiation and Stabilization of Clathrin-Coated Pits

Marcelo Ehrlich, Werner Boll, Antoine van Oijen, Ramesh Hariharan, Kartik Chandran, Max L. Nibert, Tomas Kirchhausen
2004 Cell  
Kirchhausen, 1999). The life cycle of a clathrin-coated vesicle, from coat assembly, cargo loading, and vesicle budding to coat disassembly and cargo delivery, in-
doi:10.1016/j.cell.2004.08.017 pmid:15339664 fatcat:s6nfd43cljaevi3tqfuwygbx3u

Facial Attributes Classification Using Multi-task Representation Learning

Max Ehrlich, Timothy J. Shields, Timur Almaev, Mohamed R. Amer
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
This paper presents a new approach for facial attribute classification using a multi-task learning approach. Unlike other approaches that uses hand engineered features, our model learns a shared feature representation that is wellsuited for multiple attribute classification. Learning a joint feature representation enables interaction between different tasks. For learning this shared feature representation we use a Restricted Boltzmann Machine (RBM) based model, enhanced with a factored
more » ... k component to become Multi-Task Restricted Boltzmann Machine (MT-RBM). Our approach operates directly on faces and facial landmark points to learn a joint feature representation over all the available attributes. We use an iterative learning approach consisting of a bottom-up/top-down pass to learn the shared representation of our multi-task model and at inference we use a bottom-up pass to predict the different tasks. Our approach is not restricted to any type of attributes, however, for this paper we focus only on facial attributes. We evaluate our approach on three publicly available datasets, the Celebrity Faces (CelebA), the Multi-task Facial Landmarks (MTFL), and the ChaLearn challenge dataset. We show superior classification performance improvement over the state-of-the-art.
doi:10.1109/cvprw.2016.99 dblp:conf/cvpr/EhrlichSAA16 fatcat:5xdpyy24wffw7c4jakq3gagthi

MoBoogie

Megan K. Halpern, Jakob Tholander, Max Evjen, Stuart Davis, Andrew Ehrlich, Kyle Schustak, Eric P.S. Baumer, Geri Gay
2011 Proceedings of the 2011 annual conference on Human factors in computing systems - CHI '11  
In this paper we describe MoBoogie, an application that allows users to manipulate and arrange music through movement. MoBoogie is designed to foster experiences in creative expression for children and potentially adults. The application responds to users' movements by changing variables in a continuous stream of music loops. Results from this study suggest that the creative expressions arose in the joint space of movement and music, and did not primarily have to be in one form or the other.
more » ... s allowed users with limited experience in dance and music making to be creative in such forms of expression.
doi:10.1145/1978942.1979020 dblp:conf/chi/HalpernTEDESBG11 fatcat:p2ha5t2b5fcgrk3eh7ykbph27q

Action-Affect-Gender Classification Using Multi-task Representation Learning

Timothy J. Shields, Mohamed R. Amer, Max Ehrlich, Amir Tamrakar
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
Recent work in affective computing focused on affect from facial expressions, and not as much on body. This work focuses on body affect. Affect does not occur in isolation. Humans usually couple affect with an action; for example, a person could be running and happy. Recognizing body affect in sequences requires efficient algorithms to capture both the micro movements that differentiate between happy and sad and the macro variations between different actions. We depart from traditional
more » ... s for time-series data analytics by proposing a multi-task learning model that learns a shared representation that is wellsuited for action-affect-gender classification. For this paper we choose a probabilistic model, specifically Conditional Restricted Boltzmann Machines, to be our building block. We propose a new model that enhances the CRBM model with a factored multi-task component that enables scaling over larger number of classes without increasing the number of parameters. We evaluate our approach on two publicly available datasets, the Body Affect dataset and the Tower Game dataset, and show superior classification performance improvement over the state-of-the-art.
doi:10.1109/cvprw.2017.279 dblp:conf/cvpr/ShieldsAET17 fatcat:ybtrldahxjfoflz2dxdcjlrgqq

Safety of Computed Tomographic Angiography in the Evaluation of Patients With Acute Stroke

Matthew E. Ehrlich, Heather L. Turner, Lillian J. Currie, Max Wintermark, Bradford B. Worrall, Andrew M. Southerland
2016 Stroke  
Ehrlich, MD, MPH; Heather L. Turner, BSN; Lillian J. Currie, PhD, RN; Max Wintermark, MD; Bradford B. Worrall, MD, MSc; Andrew M.  ... 
doi:10.1161/strokeaha.116.013973 pmid:27364528 fatcat:lybbq672rjdkdo775yfzit2ve4

A GERIATRIC CLINIC IN A GENERAL HOSPITAL

Louis Friedfeld, Aaron Silver, Max Needleman, Samuel Mausner, Mortimer E. Ehrlich, Freda B. Goldfeld, Ruth Michaels, Helen Bloch
1959 Journal of The American Geriatrics Society  
., MAX NEEDLEMAN, M.D., SAMUEL MAUSNER, M.D., MORTIMER E. EHRLICH, M.D., FREDA B. GOLDFELD, M.8.8., RUTH MICHAELS, M.8.8. AND HELEN BLOCH, M.8.8.W.  ... 
doi:10.1111/j.1532-5415.1959.tb00345.x pmid:13825068 fatcat:eu5wcknzcba7bp6xuyffqv35wq
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