Abstract: The invention provides agonistic anti-human VISTA antibodies and antibody fragments. These agonist antibodies and antibody fragments may be used to potentiate or enhance or mimic VISTA's suppressive effects on T cell immunity and thereby suppress T cell immunity. These agonist antibodies and antibody fragments are especially useful in the treatment of autoimmunity, allergy, inflammatory conditions, GVHD, sepsis and transplant recipients. Screening assays for identifying these agonists are also provided.

Abstract: The invention provides antagonistic and agonistic anti-human VISTA antibodies and antibody fragments. These antagonist antibodies and antibody fragments may be used to inhibit or block VISTA's suppressive effects on T cell immunity and thereby promote T cell immunity. These agonist antibodies and antibody fragments may be used to potentiate or enhance or mimic VISTA's suppressive effects on T cell immunity and thereby suppress T cell immunity. These antagonist antibodies and antibody fragments are especially useful in the treatment of cancer and infectious conditions. These agonist antibodies and antibody fragments are especially useful in the treatment of autoimmunity, allergy, inflammatory conditions, GVHD, sepsis and transplant recipients. Screening assays for identifying these agonists are also provided.

Abstract: Using a deep learning inference system, respective similarities are measured for each of a set of intermediate representations to input information used as an input to the deep learning inference system. The deep learning inference system includes multiple layers, each layer producing one or more associated intermediate representations. Selection is made of a subset of the set of intermediate representations that are most similar to the input information. Using the selected subset of intermediate representations, a partitioning point is determined in the multiple layers used to partition the multiple layers into two partitions defined so that information leakage for the two partitions will meet a privacy parameter when a first of the two partitions is prevented from leaking information. The partitioning point is output for use in partitioning the multiple layers of the deep learning inference system into the two partitions.

Abstract: The invention provides antagonistic and agonistic anti-human VISTA antibodies and antibody fragments. These antagonist antibodies and antibody fragments may be used to inhibit or block VISTA's suppressive effects on T cell immunity and thereby promote T cell immunity. These agonist antibodies and antibody fragments may be used to potentiate or enhance or mimic VISTA's suppressive effects on T cell immunity and thereby suppress T cell immunity. These antagonist antibodies and antibody fragments are especially useful in the treatment of cancer and infectious conditions. These agonist antibodies and antibody fragments are especially useful in the treatment of autoimmunity, allergy, inflammatory conditions, GVHD, sepsis and transplant recipients. Screening assays for identifying these agonists are also provided.

Abstract: A method, computer system, and a computer program product for data processing, comprising obtaining a plurality of files from a data source. These files are analyzed the files for information about the content and in order to determine structural information of each file. Once the files have been analyzed, information in each file may be sorted and categorized by common content. Sensitive information may also be extracted and categorized separately. Information may then be then merged using the categories to create a single unified file.

Abstract: A neural network is augmented to enhance robustness against adversarial attack. In this approach, a fully-connected additional layer is associated with a last layer of the neural network. The additional layer has a lower dimensionality than at least one or more intermediate layers. After sizing the additional layer appropriately, a vector bit encoding is applied. The encoding comprises an encoding vector for each output class. Preferably, the encoding is an n-hot encoding, wherein n represents a hyperparameter. The resulting neural network is then trained to encourage the network to associated features with each of the hot positions. In this manner, the network learns a reduced feature set representing those features that contain a high amount of information with respect to each output class, and/or to learn constraints between those features and the output classes. The trained neural network is used to perform a classification that is robust against adversarial examples.

Abstract: Mechanisms are provided to implement a hardened ensemble artificial intelligence (AI) model generator. The hardened ensemble AI model generator co-trains at least two AI models. The hardened ensemble AI model generator modifies, based on a comparison of the at least two AI models, a loss surface of one or more of the at least two AI models to prevent an adversarial attack on one AI model, in the at least two AI models, transferring to another AI model in the at least two AI models, to thereby generate one or more modified AI models. At least one of the one or more modified AI models then processes an input to generate an output result.

Abstract: The invention provides anti-VISTA antibody drug conjugates which may be used for targeted delivery of anti-inflammatory agents such as steroids to immune cells, e.g., myeloid cells. The invention also provides methods of using anti-VISTA antibody drug conjugates in the treatment of inflammatory and/or autoimmune conditions and/or for alleviating the toxicity of anti-inflammatory agents such as steroids.

Abstract: An approach for detection of malware is disclosed. The approach involves the use of using IR level analysis and embedding of canonical representation on a suspecting sample of software code. The approach can be applied to both malicious and benign software. Specifically, the approach includes converting a binary code to an IR (intermediate representation), canonicalizing the IR into a canonical IR, extracting one or more similarity representation based on the extracted features and comparing the one or more similarity representation to known malware.

Abstract: A method, apparatus and computer program product to defend learning models that are vulnerable to adversarial example attack. It is assumed that data (a “dataset”) is available in multiple modalities (e.g., text and images, audio and images in video, etc.). The defense approach herein is premised on the recognition that the correlations between the different modalities for the same entity can be exploited to defend against such attacks, as it is not realistic for an adversary to attack multiple modalities. To this end, according to this technique, adversarial samples are identified and rejected if the features from one (the attacked) modality are determined to be sufficiently far away from those of another un-attacked modality for the same entity. In other words, the approach herein leverages the consistency between multiple modalities in the data to defend against adversarial attacks on one modality.

Abstract: Anomalous control and data flow paths in a program are determined by machine learning the program's normal control flow paths and data flow paths. A subset of those paths also may be determined to involve sensitive data and/or computation. Learning involves collecting events as the program executes, and associating those event with metadata related to the flows. This information is used to train the system about normal paths versus anomalous paths, and sensitive paths versus non-sensitive paths. Training leads to development of a baseline “provenance” graph, which is evaluated to determine “sensitive” control or data flows in the “normal” operation. This process is enhanced by analyzing log data collected during runtime execution of the program against a policy to assign confidence values to the control and data flows. Using these confidence values, anomalous edges and/or paths with respect to the policy are identified to generate a “program execution” provenance graph associated with the policy.

Abstract: A method, apparatus and computer program product to defend learning models that are vulnerable to adversarial example attack. It is assumed that data (a “dataset”) is available in multiple modalities (e.g., text and images, audio and images in video, etc.). The defense approach herein is premised on the recognition that the correlations between the different modalities for the same entity can be exploited to defend against such attacks, as it is not realistic for an adversary to attack multiple modalities. To this end, according to this technique, adversarial samples are identified and rejected if the features from one (the attacked) modality are determined to be sufficiently far away from those of another un-attacked modality for the same entity. In other words, the approach herein leverages the consistency between multiple modalities in the data to defend against adversarial attacks on one modality.

Abstract: A processor-implemented method generates adversarial example objects. One or more processors represent an adversarial input generation process as a graph. The processor(s) explore the graph, such that a sequence of edges on the graph are explored. The processor(s) create, based on the exploring, an adversarial example object, and utilize the created adversarial example object to harden an existing process model against vulnerabilities.

Abstract: The invention provides antagonistic and agonistic anti-human VISTA antibodies and antibody fragments. These antagonist antibodies and antibody fragments may be used to inhibit or block VISTA's suppressive effects on T cell immunity and thereby promote T cell immunity. These agonist antibodies and antibody fragments may be used to potentiate or enhance or mimic VISTA's suppressive effects on T cell immunity and thereby suppress T cell immunity. These antagonist antibodies and antibody fragments are especially useful in the treatment of cancer and infectious conditions. These agonist antibodies and antibody fragments are especially useful in the treatment of autoimmunity, allergy, inflammatory conditions, GVHD, sepsis and transplant recipients. Screening assays for identifying these agonists are also provided.

Abstract: Multiple execution traces of an application are accessed. The multiple execution traces have been collected at a basic block level. Basic blocks in the multiple execution traces are scored. Scores for the basic blocks represent benefits of performing binary slimming at the corresponding basic blocks. Runtime binary slimming is performed of the application based on the scores of the basic blocks.

Abstract: The invention provides agonistic anti-human VISTA antibodies and antibody fragments. These agonist antibodies and antibody fragments may be used to potentiate or enhance or mimic VISTA's suppressive effects on T cell immunity and thereby suppress T cell immunity. These agonist antibodies and antibody fragments are especially useful in the treatment of autoimmunity, allergy, inflammatory conditions, GVHD, sepsis and transplant recipients. Screening assays for identifying these agonists are also provided.

Abstract: The invention provides agonistic anti-human VISTA antibodies and antibody fragments. These agonist antibodies and antibody fragments may be used to potentiate or enhance or mimic VISTA's suppressive effects on T cell immunity and thereby suppress T cell immunity. These agonist antibodies and antibody fragments are especially useful in the treatment of autoimmunity, allergy, inflammatory conditions, GVHD, sepsis and transplant recipients. Screening assays for identifying these agonists are also provided.

Abstract: The invention provides antagonistic and agonistic anti-human VISTA antibodies and antibody fragments. These antagonist antibodies and antibody fragments may be used to inhibit or block VISTA's suppressive effects on T cell immunity and thereby promote T cell immunity. These agonist antibodies and antibody fragments may be used to potentiate or enhance or mimic VISTA's suppressive effects on T cell immunity and thereby suppress T cell immunity. These antagonist antibodies and antibody fragments are especially useful in the treatment of cancer and infectious conditions. These agonist antibodies and antibody fragments are especially useful in the treatment of autoimmunity, allergy, inflammatory conditions, GVHD, sepsis and transplant recipients. Screening assays for identifying these agonists are also provided.

Abstract: A method, system, and computer-usable medium for analyzing security data formatted in STIX™ format. Data related to actions performed by one or more users is captured. Individual tasks, such as analytics or extract, transform, load (ETL) tasks related to the captured data is created. Individual tasks are registered to a workflow for executing particular security threat or incident analysis. The workflow is executed and visualized to perform the security threat or incident analysis.

Abstract: Mechanisms are provided to determine a susceptibility of a trained machine learning model to a cybersecurity threat. The mechanisms execute a trained machine learning model on a test dataset to generate test results output data, and determine an overfit measure of the trained machine learning model based on the generated test results output data. The overfit measure quantifies an amount of overfitting of the trained machine learning model to a specific sub-portion of the test dataset. The mechanisms apply analytics to the overfit measure to determine a susceptibility probability that indicates a likelihood that the trained machine learning model is susceptible to a cybersecurity threat based on the determined amount of overfitting of the trained machine learning model. The mechanisms perform a corrective action based on the determined susceptibility probability.