Abstract: Disclosed herein is an in-use calibration method for a spectrometer device. The method includes: providing the at least one spectrometer device including at least one optical measurement element and at least one optical calibration element having different optical properties; providing at least one sample; performing at least two measurements using the spectrometer device; generating by the at least one detector at least one first detector signal S_d1 according to the measurement without the sample and at least one second detector signal S_d2 according to the measurement with the sample; and deriving at least one calibrated optical property of the at least one sample from the first detector signal S_d1 and the second detector signal S_d2. Further disclosed herein are a spectrometer device configured for performing an in-use calibration method and various uses thereof.

Abstract: Disclosed herein is a spectrometer device for analyzing a sample. The spectrometer device includes at least one light emitting element; and at least one reference light emitting element. Further, the spectrometer device includes at least one interface element, at least one segmented aperture, and at least one optical separation element. Further, the spectrometer device includes at least one detector array including a plurality of detector elements. Further disclosed herein are a spectrometer system, a method for determining at least one information related to a spectrum of a sample with the spectrometer device and various uses of a spectrometer system.

Abstract: Here is dynamic and contextual ranking of reference documentation based on an interactively selected position in new source logic. A computer receives a vocabulary of lexical tokens, a sequence of references that contains a first reference to a first reference document before a second reference to a second reference document, respective subsets of the vocabulary that occur in the first and second reference documents, a new source logic that contains a sequence of lexical tokens, respective measurements of semantic distance between the new source logic and the first and second reference documents, and a selected position in the sequence of lexical tokens. Based on the selected position, the measurements of semantic distance are selectively increased. Based on that increasing the measurements of the semantic distance, a relative ordering of the first and second references is reversed to generate and display a reordered sequence of references.

Abstract: Herein is a machine learning (ML) explainability (MLX) approach in which a natural language explanation is generated based on analysis of a parse tree such as for a suspicious database query or web browser JavaScript. In an embodiment, a computer selects, based on a respective relevance score for each non-leaf node in a parse tree of a statement, a relevant subset of non-leaf nodes. The non-leaf nodes are grouped in the parse tree into groups that represent respective portions of the statement. Based on a relevant subset of the groups that contain at least one non-leaf node in the relevant subset of non-leaf nodes, a natural language explanation of why the statement is anomalous is generated.

Abstract: A method and one or more non-transitory storage media are provided to train and implement a one-hot encoder. During a training phase, computation of an encoder state is performed by executing a set of relational statements to extract unique categories in a first training data set, associate each unique category with a unique index, and generate a one-hot encoding for each unique category. The set of relational statements are executed by a query optimization engine. Execution of the set of relational statements is postponed until a result of each relational statement is needed, and the query optimization engine implements one or more optimizations when executing the set of relational statements. During an encoding phase, a set of categorical features in a second training data set are encoded based on the encoder state to form a set of encoded categorical features.

Abstract: In an embodiment providing natural language processing (NLP), a computer generates a histogram that correctly represents a graph that represents a lexical text, and generates a token sequence encoder that is trainable and untrained. During training such as pretraining, the token sequence encoder infers an encoded sequence that incorrectly represents the lexical text, and the encoded sequence is dense and saves space. To increase the accuracy of the sequence encoder by learning, the token sequence encoder is adjusted based on, as discussed herein, an indirectly measured numeric difference between the encoded sequence that incorrectly represents the lexical text and the histogram that correctly represents the graph.

Abstract: Techniques are described that extend supervised machine-learning algorithms for use with semi-supervised training. Random labels are assigned to unlabeled training data, and the data is split into k partitions. During a label-training iteration, each of these k partitions is combined with the labeled training data, and the combination is used train a single instance of the machine-learning model. Each of these trained models are then used to predict labels for data points in the k?1 partitions of previously-unlabeled training data that were not used to train of the model. Thus, every data point in the previously-unlabeled training data obtains k?1 predicted labels. For each data point, these labels are aggregated to obtain a composite label prediction for the data point. After the labels are determined via one or more label-training iterations, a machine-learning model is trained on data with the resulting composite label predictions and on the labeled data set.

Abstract: Herein is validation of a trained classifier based on novel and accelerated estimation of a confusion matrix. In an embodiment, a computer hosts a trained classifier that infers, from many objects, an inferred frequency of each class. An upscaled magnitude of each class is generated from the inferred frequency of the class. An integer of each class is generated from the upscaled magnitude of the class. Based on those integers of the classes and a target integer for each class, counts are generated of the objects that are true positives, false positives, and false negatives of the class. Based on those counts, an estimated total of true positives, false positives, false negatives are generated that characterizes fitness of the trained classifier. In an embodiment, those counts and totals are downscaled to be fractions from zero to one.

Abstract: Herein is natural language processing (NLP) to detect an anomalous log entry using a language model that infers an encoding of the log entry from novel generation of numeric lexical tokens. In an embodiment, a computer extracts an original numeric lexical token from a variable sized log entry. Substitute numeric lexical token(s) that represent the original numeric lexical token are generated, such as with a numeric exponent or by trigonometry. The log entry does not contain the substitute numeric lexical token. A novel sequence of lexical tokens that represents the log entry and contains the substitute numeric lexical token is generated. The novel sequence of lexical tokens does not contain the original numeric lexical token. The computer hosts and operates a machine learning model that generates, based on the novel sequence of lexical tokens that represents the log entry, an inference that characterizes the log entry with unprecedented accuracy.

Abstract: A method and one or more non-transitory storage media are provided to train and implement a one-hot encoder. During a training phase, computation of an encoder state is performed by executing a set of relational statements to extract unique categories in a first training data set, associate each unique category with a unique index, and generate a one-hot encoding for each unique category. The set of relational statements are executed by a query optimization engine. Execution of the set of relational statements is postponed until a result of each relational statement is needed, and the query optimization engine implements one or more optimizations when executing the set of relational statements. During an encoding phase, a set of categorical features in a second training data set are encoded based on the encoder state to form a set of encoded categorical features.

Abstract: Disclosed herein is a method for calibrating a spectrometer device. The method includes the following steps: a) illuminating at least one detector device of the spectrometer device with at least one broadband light source through at least one narrow band pass filter having a plurality of predetermined transmission bands; b) generating, by using the detector device, a plurality of detector signals depending on the illumination of step a); c) determining at least one item of wavelength calibration information, where the item of wavelength calibration information includes at least one assignment assigning wavelengths of incident light to corresponding photosensitive elements being responsive to these wavelengths; and d) determining at least one item of stray light calibration information based on the plurality of detector signals. Further disclosed herein are a system for calibrating a spectrometer device, a computer program and a computer-readable storage medium.

Abstract: A computer performs deduplication of an original training corpus for maintaining accuracy of accelerated training of a reconstructive or other machine learning (ML) model. Distinct multidimensional points are detected in the original training corpus that contains duplicates. Based on duplicates in the original training corpus, a respective observed frequency of each distinct multidimensional point is increased. In a reconstructive embodiment and based on a particular distinct multidimensional point as input, a reconstruction of the particular distinct multidimensional point is generated by a reconstructive ML model. Based on increasing the observed frequency of the particular distinct multidimensional point, a scaled error of the reconstruction of the particular distinct multidimensional point is increased. Based on the scaled error of the reconstruction of the particular distinct multidimensional point, accuracy of the reconstructive model is increased.

Abstract: In an embodiment, a computer generates a multi-sequence vector that contains a plurality of distinct sequences of distinct nodes of a parse tree of source logic. Based on the multi-sequence vector, the computer trains a logic encoder. After training and in a production environment, the logic encoder infers a fixed-size encoded logic from new source logic. Based on the fixed-size encoded logic, the new source logic is detected as anomalous by an anomaly detector. Both of the logic encoder and the anomaly detector are machine learning models and, herein, they may be separately trained. In an embodiment, the logic encoder is based on a natural language processing (NLP) language model architecture such as bidirectional encoder representations from transformers (BERT), or novel training herein may be self-supervised according to skip-gram for use with an unlabeled training corpus.

Abstract: Disclosed herein is a method of controlling operation of at least one light-emitting system including at least one light-emitting element. The method includes: a) monitoring at least temporarily, at least one electrical parameter of the light-emitting element; b) evaluating the electrical parameter and verifying if the electrical parameter meets at least one predetermined criterion; and c) taking at least one safeguard action depending on the outcome of step b).

Abstract: Techniques for implementing a semi-supervised framework for purpose-oriented anomaly detection are provided. In one technique, a data item in inputted into an unsupervised anomaly detection model, which generates first output. Based on the first output, it is determined whether the data item represents an anomaly. In response to determining that the data item represents an anomaly, the data item is inputted into a supervised classification model, which generates second output that indicates whether the data item is unknown. In response to determining that the data item is unknown, a training instance is generated based on the data item. The supervised classification model is updated based on the training instance.

Abstract: In an embodiment, a computer generates sentence fingerprints that represent respective pluralities of similar database statements. Based on the sentence fingerprints, an artificial neural network is trained. After training the artificial neural network on a large corpus of fingerprinted database statements, the artificial neural network is ready to be used for zero-shot transfer learning to a downstream task in training. Database statement fingerprinting also anonymizes literal values in raw SQL statements. The trained artificial neural network can be safely reused without risk of disclosing sensitive data in the artificial neural network's vocabulary. After training, the artificial neural network infers a fixed-size encoded database statement from a new database statement. Based on the fixed-size encoded database statement, the new database statement is detected as anomalous, which increases database security and preserves database throughput by not executing the anomalous database statement.

Abstract: In an embodiment, a computer stores and operates a logic encoder that is an artificial neural network that infers a fixed-size encoded logic from textual or tokenized source logic. Without machine learning, a special parser generates a parse tree that represents the source logic and a fixed-size correctly encoded tree that represents the parse tree. For finetuning the logic encoder, an encoded tree generator is an artificial neural network that accepts the fixed-size encoded logic as input and responsively infers a fixed-size incorrectly encoded tree that represents the parse tree. The neural weights of the logic encoder (and optionally of the encoded tree generator) are adjusted based on backpropagation of error (i.e. loss) as a numerically measured difference between the fixed-size incorrectly encoded tree and the fixed-size correctly encoded tree.

Abstract: Herein for each source logic in a corpus, a computer stores an identifier of the source logic and operates a logic encoder that infers a distinct fixed-size encoded logic that represents the variable-size source logic. At build time, a multidimensional index is generated and populated based on the encoded logics that represent the source logics in the corpus. At runtime, a user may edit and select a new source logic such as in a text editor or an integrated development environment (IDE). The logic encoder infers a new encoded logic that represents the new source logic. The multidimensional index accepts the new encoded logic as a lookup key and automatically selects and returns a result subset of encoded logics that represent similar source logics in the corpus. For display, the multidimensional index may select and return only encoded logics that are the few nearest neighbors to the new encoded logic.

Abstract: Herein is resource-constrained feature enrichment for analysis of parse trees such as suspicious database queries. In an embodiment, a computer receives a parse tree that contains many tree nodes. Each tree node is associated with a respective production rule that was used to generate the tree node. Extracted from the parse tree are many sequences of production rules having respective sequence lengths that satisfy a length constraint that accepts at least one fixed length that is greater than two. Each extracted sequence of production rules consists of respective production rules of a sequence of tree nodes in a respective directed tree path of the parse tree having a path length that satisfies that same length constraint. Based on the extracted sequences of production rules, a machine learning model generates an inference. In a bag of rules data structure, the extracted sequences of production rules are aggregated by distinct sequence and duplicates are counted.

Abstract: Disclosed herein is a spectroscopic light source. The spectroscopic light source includes: at least one light emitting element; at least one electronic circuit configured for applying electric power to the light emitting element; and at least one housing, where the housing at least partially surrounds the light emitting element; and at least two output channels going through the housing, where each one of the output channels is configured for decoupling at least one light beam from the spectroscopic light source. The spectroscopic light source is configured for independently controlling each one of the output channels.