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Abstract Quadrotors have found widespread use in indoor applications, including tracking and mapping. In general, to carry out such tasks effectively, a navigation solution should provide both accuracy and battery efficiency. To achieve both, we propose a cost-effective and lightweight wheeled quadrotor that combines both driving and flying capabilities. Our design allows the quadrotor to perform both functions seamlessly. We provide a detailed description of the design and construction process, highlighting its advantages. Our focus was on the Tello quadrotor, which weighs 80 grams. Our design allowed driving capability with an increased weight of only fifteen grams, resulting in less than 20% of the added weight. Furthermore, we evaluate the quadrotor’s pure inertial navigation performance and corresponding battery consumption by employing various flying and driving patterns. Our results show that when only driving the battery consumption was the lowest with 10% and some flying scenarios improve the positioning error by more than 70%.

Background and Aims: A family history of colorectal cancer (CRC) in a first-degree relative is a well-established risk factor for CRC. When individuals have 2 parents with CRC, the impact on risk is uncertain, and there are no established guidelines for surveillance. We sought to define the surveillance practices and outcomes in individuals with a family history of CRC in both parents. Methods: We identified probands with a family history of CRC in both parents from our Hereditary Gastrointestinal Cancer Database. Charts were retrospectively reviewed for colonoscopy surveillance patterns and incidence of adenomas and CRC. Results: Sixty-six patients met the inclusion criteria. Forty-two patients (64%) had genetic testing, and no pathogenic germline mutations were identified. During a mean surveillance period of 144 ± 82.2 months and a mean surveillance interval of 33.4 ± 16.6 months, a total of 3.2 ± 8.9 adenomas were found per patient. These were small (median 6.5 mm), and 96% exhibited only low-grade dysplasia. Six patients (9%) were diagnosed with CRC at a mean age of 61.5 ± 11.3 years, corresponding to an incidence rate of 14 cases/10,000 person-years. Patients with CRC were older at first colonoscopy than those without cancer (59 vs 46 years, P = .03), and half of these cases were diagnosed at this first colonoscopy. Conclusion: Among patients with a family history of CRC in both parents, cases of CRC were seen primarily in those who significantly delayed their first colonoscopy. Initiation of colonoscopy at age 40 should be recommended to individuals with CRC in both parents, consistent with recommendations for those with 1 first-degree relative with CRC.

BackgroundHuman telomerase reverse transcriptase (hTERT)- mRNA was shown to be elevated in exosomes derived from the sera of a variety of hematological and solid cancer patients. We aimed to evaluate its role as a diagnostic marker in patients with newly diagnosed colon cancer and in hereditary syndromes with predisposition to colon cancer.MethodshTERT -mRNA levels were determined in serum-derived exosomes from 88 patients with colon cancer, 71 Lynch-syndrome carriers with unknown active malignancies and 50 healthy controls. Data, including demographics, background diseases, clinical data regarding tumor characteristics and genetic data, were retrieved data from medical files.ResultsPatients with colon cancer had both higher exosomal hTERT mRNA levels and a higher proportion of patients with positive exosomal hTERT mRNA than controls (29.5% vs. 4%, respectively, P values < 0.001). Within the cancer group, patients with a metastatic disease had higher levels of telomerase mRNA than non-metastatic disease patients, and these levels correlated with CEA levels. Likewise, Lynch syndrome carriers had a higher proportion of positive exosomal hTERT mRNA than controls (21.1% vs. 4%, respectively, P value 0.008) but only a trend towards higher exosomal hTERT mRNA levels. Higher telomerase mRNA levels were not correlated with the mutated gene.ConclusionsExosomal serum hTERT –mRNA levels are associated with metastatic colon cancer and were also demonstrated in a subset of Lynch syndrome carriers. Its significance as a biomarker for developing malignancy should be elucidated.

Coreset is usually a small weighted subset of an input set of items, that provably approximates their loss function for a given set of queries (models, classifiers, hypothesis). That is, the maximum (worst-case) error over all queries is bounded. To obtain smaller coresets, we suggest a natural relaxation: coresets whose average error over the given set of queries is bounded. We provide both deterministic and randomized (generic) algorithms for computing such a coreset for any finite set of queries. Unlike most corresponding coresets for the worst-case error, the size of the coreset in this work is independent of both the input size and its Vapnik–Chervonenkis (VC) dimension. The main technique is to reduce the average-case coreset into the vector summarization problem, where the goal is to compute a weighted subset of the n input vectors which approximates their sum. We then suggest the first algorithm for computing this weighted subset in time that is linear in the input size, for n≫1/ε, where ε is the approximation error, improving, e.g., both [ICML’17] and applications for principal component analysis (PCA) [NIPS’16]. Experimental results show significant and consistent improvement also in practice. Open source code is provided.

A common technique for compressing a neural network is to compute the k-rank ℓ2 approximation Ak of the matrix A∈Rn×d via SVD that corresponds to a fully connected layer (or embedding layer). Here, d is the number of input neurons in the layer, n is the number in the next one, and Ak is stored in O((n+d)k) memory instead of O(nd). Then, a fine-tuning step is used to improve this initial compression. However, end users may not have the required computation resources, time, or budget to run this fine-tuning stage. Furthermore, the original training set may not be available. In this paper, we provide an algorithm for compressing neural networks using a similar initial compression time (to common techniques) but without the fine-tuning step. The main idea is replacing the k-rank ℓ2 approximation with ℓp, for p∈[1,2], which is known to be less sensitive to outliers but much harder to compute. Our main technical result is a practical and provable approximation algorithm to compute it for any p≥1, based on modern techniques in computational geometry. Extensive experimental results on the GLUE benchmark for compressing the networks BERT, DistilBERT, XLNet, and RoBERTa confirm this theoretical advantage.

Secure report is the problem of a client that retrieves all records matching specified attributes from a database table at the server (e.g. cloud), as in SQL SELECT queries, but where the query and the database are encrypted. Here, only the client has the secret key, but still the server is expected to compute and return the encrypted result. Secure report is theoretically possible with Fully Homomorphic Encryption (FHE). However, the current state-of-the-art solutions are realized by a polynomial of degree that is at least linear in the number m of records, which is too slow in practice even for very small databases. We present the first solution that is realized by a polynomial that attains degree independent of the number of records m, as well as the first implementation of an FHE solution to Secure report. This is by suggesting a novel paradigm that forges a link between cryptography and modern data summarization techniques known as coresets (core-sets), and sketches in particular. The key idea is to compute only a coreset of the desired report. Since the coreset is small, the client can quickly decode the desired report that the server computes after decrypting the coreset. We implemented our main reporting system in an open source library. This is the first implemented system that can answer such database queries when processing only FHE encrypted data and queries. As our analysis promises, the experimental results show that we can run Secure report queries on billions records in minutes on an Amazon EC2 server, compared to less than a hundred-thousands in previous FHE based solutions.

Background: Melanosis coli, a brown discoloration of colonic mucosa, is considered as a benign condition mainly observed in patients under chronic anthranoid laxatives. Recent data link this condition with an increased adenoma detection rate. Moreover, its tumorigenic potential and possible association with the development of colorectal cancer remains uncertain. We conducted this study to compare the polyp detection rate and colorectal cancer diagnosis in patients with melanosis against matched control group without melanosis. Patients and methods: A retrospective single-center study. Patients diagnosed with melanosis coli on colonoscopy over a 15-year period were included. Each melanosis coli patient was matched with three controls by age, gender, setting (inpatient/outpatient), and procedure’s indication. Polyp detection rate and diagnosis of colorectal cancer were recorded and compared between the groups before and after adjustment for bowel preparation. Results: A cohort of 718 patients with melanosis and 2154 controls were included. The polyp detection rates were 33.4% and 21.8% of melanosis and control groups, respectively ( P

The k-means problem is to compute a set of k centers (points) that minimizes the sum of squared distances to a given set of n points in a metric space. Arguably, the most common algorithm to solve it is k-means++ which is easy to implement and provides a provably small approximation error in time that is linear in n. We generalize k-means++ to support outliers in two sense (simultaneously): (i) nonmetric spaces, e.g., M-estimators, where the distance dist(p,x) between a point p and a center x is replaced by mindist(p,x),c for an appropriate constant c that may depend on the scale of the input. (ii) k-means clustering with m≥1 outliers, i.e., where the m farthest points from any given k centers are excluded from the total sum of distances. This is by using a simple reduction to the (k+m)-means clustering (with no outliers).

We suggest a provable and practical approximation algorithm for fitting a set P of n points in R d to a sphere. Here, a sphere is represented by its center x ∈ R d and radius r > 0 . The goal is to minimize the sum ∑ p ∈ P ∣ p − x − r ∣ of distances to the points up to a multiplicative factor of 1 ± ε , for a given constant ε > 0 , over every such r and x. Our main technical result is a data summarization of the input set, called coreset, that approximates the above sum of distances on the original (big) set P for every sphere. Then, an accurate sphere can be extracted quickly via an inefficient exhaustive search from the small coreset. Most articles focus mainly on sphere identification (e.g., circles in 2 D image) rather than finding the exact match (in the sense of extent measures), and do not provide approximation guarantees. We implement our algorithm and provide extensive experimental results on both synthetic and real-world data. We then combine our algorithm in a mechanical pressure control system whose main bottleneck is tracking a falling ball. Full open source is also provided.

A coreset of a dataset is a small weighted set, such that querying the coreset provably yields a ( 1 + ε )-factor approximation to the original (full) dataset, for a given family of queries. This paper suggests accurate coresets ( ε = 0 ) that are subsets of the input for fundamental optimization problems. These coresets enabled us to implement a “Guardian Angel” system that computes pose-estimation in a rate > 20 frames per second. It tracks a toy quadcopter which guides guests in a supermarket, hospital, mall, airport, and so on. We prove that any set of n matrices in R d × d whose sum is a matrix S of rank r, has a coreset whose sum has the same left and right singular vectors as S, and consists of O ( d r ) = O ( d 2 ) matrices, independent of n. This implies the first (exact, weighted subset) coreset of O ( d 2 ) points to problems such as linear regression, PCA/SVD, and Wahba’s problem, with corresponding streaming, dynamic, and distributed versions. Our main tool is a novel usage of the Caratheodory Theorem for coresets, an algorithm that computes its set in time that is linear in its cardinality. Extensive experimental results on both synthetic and real data, companion video of our system, and open code are provided.

Let P be a set of n points in R d , k ≥ 1 be an integer and ε ∈ ( 0 , 1 ) be a constant. An ε-coreset is a subset C ⊆ P with appropriate non-negative weights (scalars), that approximates any given set Q ⊆ R d of k centers. That is, the sum of squared distances over every point in P to its closest point in Q is the same, up to a factor of 1 ± ε to the weighted sum of C to the same k centers. If the coreset is small, we can solve problems such as k-means clustering or its variants (e.g., discrete k-means, where the centers are restricted to be in P, or other restricted zones) on the small coreset to get faster provable approximations. Moreover, it is known that such coreset support streaming, dynamic and distributed data using the classic merge-reduce trees. The fact that the coreset is a subset implies that it preserves the sparsity of the data. However, existing such coresets are randomized and their size has at least linear dependency on the dimension d. We suggest the first such coreset of size independent of d. This is also the first deterministic coreset construction whose resulting size is not exponential in d. Extensive experimental results and benchmarks are provided on public datasets, including the first coreset of the English Wikipedia using Amazon’s cloud.